Ambulatory EEG Monitoring [1 ed.] 9781617052781, 9781620701010

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Ambulatory EEG Monitoring

Ambulatory EEG Monitoring Editor William O. Tatum, IV, DO Professor of Neurology Mayo Clinic College of Medicine & Science Mayo Clinic Jacksonville, Florida

New York

Visit our website at www.demosmedical.com ISBN: 978-1-6207-0101-0 e-book ISBN: 978-1-6170-5278-1 Acquisitions Editor: Beth Barry Compositor: diacriTech Copyright © 2017 Springer Publishing Company. Demos Medical Publishing is an imprint of Springer Publishing Company, LLC. All rights reserved. This book is protected by copyright. No part of it may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Medicine is an ever-changing science. Research and clinical experience are continually expanding our knowledge, in particular our understanding of proper treatment and drug therapy. The authors, editors, and publisher have made every effort to ensure that all information in this book is in accordance with the state of knowledge at the time of production of the book. Nevertheless, the authors, editors, and publisher are not responsible for errors or omissions or for any consequences from application of the information in this book and make no warranty, expressed or implied, with respect to the contents of the publication. Every reader should examine carefully the package inserts accompanying each drug and should carefully check whether the dosage schedules mentioned therein or the contraindications stated by the manufacturer differ from the statements made in this book. Such examination is particularly important with drugs that are either rarely used or have been newly released on the market. Library of Congress Cataloging-in-Publication Data Names: Tatum, William O., IV, editor. Title: Ambulatory EEG monitoring / editor, William O. Tatum. Other titles: Ambulatory EEG monitoring (Tatum) Description: New York : Demos Medical, [2017] | Includes bibliographical  references and index. Identifiers: LCCN 2016054675| ISBN 9781620701010 | ISBN 9781617052781 (e-book) Subjects: MESH: Electroencephalography | Monitoring, Ambulatory–methods Classification: LCC RC386.6.E43 | NLM WL 150 | DDC 616.8/047547–dc23 LC record available at https://lccn.loc.gov/2016054675 Contact us to receive discount rates on bulk purchases. We can also customize our books to meet your needs. For more information please contact: [email protected] Printed in the United States of America by LSC Communications. 17 18 19 20 21 / 5 4 3 2 1

This book is dedicated to my wonderful family, superb colleagues at the Mayo Clinic, and to my patients who teach me every day about the incredible power of the human spirit.

DKWILY

Contents

Contributors ix Preface xi Share Ambulatory EEG Monitoring 1: The History of Ambulatory EEG—A Personal Perspective  1 John S. Ebersole 2: Technical Aspects of Ambulatory Electroencephalography  13 J. Andrew Ehrenberg and Charles M. Epstein 3: Instrumentation and Polygraphic EEG  33 Elizabeth Waterhouse 4: Artifact and Ambulatory EEG  41 William O. Tatum, IV 5: Clinical Use of Ambulatory EEG in Adults  75 Donald L. Schomer 6: Clinical Use of Ambulatory EEG in Pediatrics  99 Adriana Ulate-Campos and Tobias Loddenkemper 7: Short-Term Ambulatory EEG  115 Jason L. Siegel and William O. Tatum, IV 8: Ambulatory Sleep Monitoring  131 Madeleine M. Grigg-Damberger and Frank M. Ralls 9: Chronic Ambulatory EEG With Implanted Electrodes  155 Benjamin N. Blond and Lawrence J. Hirsch 10: Future Directions in Ambulatory EEG  179 Steven C. Schachter 11: Reimbursement Issues in Ambulatory EEG  191 Marc R. Nuwer 12: Ambulatory EEG Cases  203 Joseph Drazkowski and Ejerzain Aniles-Renova Epilogue 219 Index 221

vii

Contributors

Ejerzain Aniles-Renova, BS, R. EEG T., CLTM  Department of Neurology, Mayo Clinic in Arizona, Phoenix, Arizona Benjamin N. Blond, MD  Epilepsy Fellow, Department of Neurology, Comprehensive Epilepsy Center, Yale University School of Medicine, New Haven, Connecticut Joseph Drazkowski, MD  Professor, Department of Neurology, Mayo Clinic in Arizona, Phoenix, Arizona John S. Ebersole, MD  Senior Scientific Consultant, MEG Center, Overlook Medical Center, Summit, New Jersey J. Andrew Ehrenberg, BSc, R. EEG T., CNIM  Department of Neurology, Emory University School of Medicine, Atlanta, Georgia Charles M. Epstein, MD  Professor, Department of Neurology, Emory University School of Medicine, Atlanta, Georgia Madeleine M. Grigg-Damberger, MD  Professor of Neurology, Medical Director Pediatric Sleep Medicine Services, Department of Neurology, University of New Mexico, Albuquerque, New Mexico Lawrence J. Hirsch, MD  Professor of Neurology, Chief, Division of Epilepsy and EEG, Co-Director, Comprehensive Epilepsy Center; Co-Director, Critical Care EEG Monitoring Program, Yale University School of Medicine, New Haven, Connecticut Tobias Loddenkemper, MD  Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts Marc R. Nuwer, MD, PhD  Professor and Vice Chair, Department of Neurology, David Geffen School of Medicine at UCLA; Department Head, Department of Clinical Neurophysiology, Ronald Reagan UCLA Medical Center, Los Angeles, California

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x • Contributors

Frank M. Ralls, MD  Associate Professor of Internal Medicine, Medical Director, Adult Sleep Medicine Services Program Director, UNM Sleep Medicine Fellowship, University of New Mexico, Albuquerque, New Mexico Steven C. Schachter, MD  Professor, Chief Academic Officer, Consortia for Improving Medicine Through Innovation and Technology, Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Harvard University, Boston, Massachusetts Donald L. Schomer, MD  Professor, Department of Neurology, Harvard Medical School, Harvard University; Director, Laboratory of Clinical Neurophysiology; Chief, Comprehensive Epilepsy Program, Beth Israel Deaconess Medical Center, Boston, Massachusetts Jason L. Siegel, MD  Neurocritical Care Fellow, Department of Neurology, Mayo Clinic Florida and Mayo College of Medicine, Jacksonville, Florida William O. Tatum, IV, DO  Professor, Department of Neurology, Mayo Clinic College of Medicine & Science, Mayo Clinic; Director, Comprehensive Epilepsy Center, Mayo Clinic Florida, Jacksonville, Florida Adriana Ulate-Campos, MD  Pediatric Neurologist, Department of Neurology, National Children’s Hospital “Dr. Carlos Saenz Herrera,” San José, Costa Rica Elizabeth Waterhouse, MD  Professor, Department of Neurology, Virginia Commonwealth University School of Medicine, Richmond, Virginia

Preface

The Epilepsy Foundation notes that 10% of the population will have a sei­ zure in their lifetime. The first seizure rate in new-onset epilepsy suggests that  the prevalence increases with aging. While many experience focal ­seizures, detection may be challenging. Electroencephalography (EEG) is the functional method to identify abnormalities in patients experiencing seizures. Furthermore, some seizures are subtle and difficult to recognize, thus delaying diagnosis. Frequently patients with epilepsy will be unaware of their seizures, making EEG an essential tool for quantifying seizure frequency. EEG has the ability with a high level of clinical evidence to predict first seizure recurrence. These first seizures constitute the basis of epilepsy when an EEG is abnormal and demonstrates epileptiform discharges. However, a routine scalp EEG is only “positive” in about one-third to one-half of cases. Overall, approximately 3 million people in the United States have epilepsy, and more than half a million have spells that look like epilepsy. EEG monitoring with video has become a major breakthrough for patients with seizures and seizure-like episodes. In the hospital, video-EEG monitoring (VEM) has identified 30% of patients who do not have the diagnosis of epilepsy despite treatment for the condition. EEG monitoring can also impact treatment by identifying epileptiform discharges that help classify seizures for proper antiseizure drug selection. Beyond classifying the type of seizures and epilepsy with identification of epileptiform discharges, it may quantify the number of seizures in patients with epilepsy that cannot be adequately controlled with antiseizure medication or address the presence of seizures without awareness. The National Institute of Neurological Disorders and Stroke notes that at least 20% of ­persons with epilepsy have ongoing seizures that are not responsive to treatment, though the representation is higher at specialized epilepsy centers. The standard approach to an epilepsy diagnosis in most cases involves a routine scalp EEG performed as an outpatient. The result of an initial EEG is often a normal result even when patients have a clinical diagnosis of epilepsy. When routine scalp EEG fails to provide a conclusive result and the clinical history suggests seizures, ambulatory EEG (aEEG) may provide information to change the course of treatment. Most commercial EEG systems now include video, and therefore the choice of VEM technique is dependent on location and duration of recording. xi

xii • Preface

The purpose of this book is to fill the void of knowledge as to when and how to use aEEG in the evaluation of patients with paroxysmal neurological events and especially epilepsy. Ambulatory EEG monitoring is not limited to the home environment. It is a useful diagnostic test to evaluate an individual in whom seizures or s­ eizure-like episodes are suspected, and can be performed in his or her natural environment wherever that may be. I remember a patient with “Ryan’s Steakhouse seizures”: whenever the patient entered that environment, a seizure occurred. Similar experiences result when provocative techniques occur with specific environmental exposure or actions outside the ­hospital or home. At this point, seizure provocation utilizing antiseizure drug taper is best managed in the safe confines of the hospital. Nevertheless, outside of safety issues with drug reduction, digital technology has evolved to the point of computer-assisted aEEG with up to 32 channels and video capability as well as software additions that make spike and seizure detection possible over several days of recording, expanding the utility of aEEG exponentially in any setting. Dr. John Ebersole, one of the pioneers of aEEG, recounts his experience with this technology prior to our current state of technology in Chapter 1. Similar to inpatient VEM, aEEG has many of the same features. In Chapters 2 and 3, Andrew Ehrenberg and Dr. Charles M. Epstein review some of the background distinctions for the technique of aEEG, while Dr. Elizabeth Waterhouse reviews instrumentation and polygraphic aEEG recording. Ambulatory EEG monitoring often yields information in the evaluation of epilepsy and in the differential diagnosis of other conditions that mimic epilepsy, including psychogenic nonepileptic seizures (PNES), syncope, cardiogenic etiologies, parasomnias, migraine, and transient ischemic attacks. In subsequent chapters, Dr. Don Schomer addresses the use of aEEG in adults, Drs. Jason Siegel and William Tatum review the short-term use of aEEG, and Adriana UlateCampos and Tobias Loddenkemper discuss the yield of aEEG in children within the context of long-term monitoring techniques in epilepsy. Drs. Madeleine Grigg-Damberger and Frank Ralls provide a thorough assessment of ambulatory sleep studies. Chronic intracranial aEEG monitoring is covered by Drs. Benjamin Blond and Lawrence Hirsch, focusing on newer forms of chronic intracranial aEEG monitoring that provide outpatient electrocorticography within the context encompassing a recent encompass treatment using neuromodulation. Dr. Steven Schachter then delivers an inside look at aEEG in the future, with development of “wearables” that will allow us to learn new information that involves not only detection but also prediction. Finally, the practical implications of aEEG involved in proper coding and billing are reviewed by Dr. Marc Nuwer. Case histories are included of patients evaluated with aEEG to underscore key points of practical value by Drs. Joseph Drazkowski and Ejerzain Aniles-Renova.

Preface • xiii

This book has contributions by notable experts within the field of clinical neurophysiology. Understandably, aEEG is a technology-based electrophysiological technique that is subject to dynamic change over time. I am grateful to the authors for sharing their expertise and for their contributions not only to this book, but also to the important advances in the field of clinical neurophysiology dealing with EEG. We are limited in assessing the value of signals arising from the brain that compose human EEG, not by technology or by design, but only by our imagination of where, when, and how to measure it. William O. Tatum, IV, DO

Share Ambulatory EEG Monitoring

CHAPTER 1

THE HISTORY OF AMBULATORY EEG— A PERSONAL PERSPECTIVE JOHN S. EBERSOLE, MD

PROLOGUE Reviewing this history is useful not for the details of technologies that are now ­obsolete, but for the story of the struggle to bring a new diagnostic procedure to ­fruition. Hopefully, this will inspire young clinical investigators to accept and address similar challenges in future eras. INTRODUCTION To understand the significance of the development of ambulatory EEG (aEEG) and the problems faced and eventually overcome in its evolution, one must go back in time to the clinical neurophysiology of the 1970s. The Grass eight-channel, pen-writing EEG machine was the long-standing clinical workhorse. New on the horizon was a 16-channel EEG machine that would become the latest great thing at the best centers. The value of longterm EEG monitoring (LTM) with video recording was just becoming established. Now seizures could be recorded commonly, not simply spikes, as was usual for laboratory EEG. Early pioneers of LTM had to be creative regarding how to record both EEG and patient behavior in a synchronized fashion. Initially, two cameras were used with split-screen video technology. Remember, this was a predigital era. One camera imaged the moving EEG paper with newly penned waveforms, and the other recorded the patient. Engineering advances eventually included the “reformatter” that recorded multiplexed EEG on the edge of the videotape recording the patient. These advances in epilepsy diagnosis and characterization were monumental at the time, but they came with some drawbacks. There were only a handful of epilepsy monitoring units (EMUs) in the country, hospitalization was 1

2  •  Ambulatory EEG Monitoring

required, it was expensive to keep patients in hospital for days, and the normal day-to-day triggers for seizures were not present in a hospital setting. Patient activity and mobility were restricted often to sitting or lying in bed. Being in the hospital was clearly unnatural and likely to inhibit both real epileptic and nonepileptic episodes. In fact, it was soon realized that a patient’s typical seizure frequency often plummeted when he or she was hospitalized. If there was only a way to obtain the benefits of long-term EEG recording in an outpatient, rather than hospital, setting. Cardiologists have always faced a similar problem to that of epileptologists, namely the detection of physiological abnormalities that are paroxysmal. Cardiac arrhythmias are often intermittent, and they are often not recorded on a standard duration ECG. Fortunately for them, the ECG is relatively large in amplitude, and one channel of data is usually sufficient to identify the problem. Accordingly, the development of ambulatory ECG monitoring was technically easier, and it preceded that of aEEG. Norman Holter in 1953 devised a system for the radio-telemetry of single-channel ECG from an ambulatory patient. Later this transitioned to long-term, single channel, cassette recordings of ECG, and the famous “Holter monitor” was born (1).

CONTINUOUS aEEG RECORDING However, the development of any reasonable aEEG counterpart would have to await the solution of four technical problems—more channels than just one, preamplification of the much smaller EEG signal, a recording duration that was at least 24 hours, and a means to play back and analyze all the recorded data in an efficient fashion. One by one in the early 1970s these obstacles were overcome. A four-channel, portable tape recorder, weighing approximately 1.5 pounds, was developed by Marson and McKinnon for industrial purposes (2). Ives and Wood later showed that recording EEG on it was feasible (3). These recorders were analog devices that used 1/8-inch tape and four recording heads. Tape speed was reduced to 2 mm/sec, so that a standard C120 cassette could record at least 24 hours of continuous data. However, the problem of amplifying the EEG signals sufficiently before transcription onto tape remained. Theoretically, it made sense to try to amplify the EEG as close as possible to the head in order to minimize lead artifact from movements during wakefulness. In 1978, Quy developed an a­ mplifier chip that could be glued onto the scalp with collodion (4). Each chip r­ epresented a single channel and had its own Grid 1 and 2 inputs. Electrode leads were plugged into the chip, so montages were necessarily derived on the head. Electrodes with bifurcated leads were necessary if montages with linked channels, such as a bipolar chain, were desired. That still left the problem of reviewing hours of recorded data. One ­solution was to print out the entire recording with a jet ink writer at up

1.  The History of AMBULATORY EEG—A Personal Perspective  •  3

to 20× real time. To reduce the volume of paper produced, which was still large, the recording paper speed could be reduced, but this meant that only generalized seizures could be discerned. The paginated, rapid video playback was the conceptual breakthrough that made efficient analysis of aEEG data feasible (5). This new video playback was also an analog device, that is, essentially an oscilloscope. Repeated refreshing of the screen allowed both a static page of data to be visualized and also sequential pages of data at 20× or 60× real time. Video page lengths could be either 8 or 16 seconds. At the fastest replay speed, 24 hours of data could be reviewed in 24 minutes. A critical additional feature of the playback unit was the simultaneous audio reproduction of one data channel. At 20× or in particular 60×, standard EEG frequencies would become audible. If an accurate time registration during the recording was desired, unfortunately one of the four channels had to be sacrificed for that function. Thus, in 1979, Oxford Medilog introduced a completed, commercially available, four-channel aEEG recorder, preamplification system, and paginated video playback (Figure 1.1). However, questions remained as to whether such a system would be clinically useful and how it should be used to extract the most information efficiently. Now that epileptologists had become accustomed to eight- and soon 16-channel EEG, it was going to be difficult for them to accept four-, let alone three-, channel EEG data. That was clearly a giant step backward. It was accordingly easy to dismiss the new recorders as less than useful toys. It took a bit more insight to see the benefit that a longer recording brought, particularly as and outpatient, even with significantly fewer channels. Convincing neurologists that a three-channel EEG carried any worth would require a c­ ontrolled

FIGURE 1.1  The Oxford Medilog 4–24 recorder was the first commercial aEEG cassette recorder. It utilized the HDX-82 on-head preamplifier chip. The Oxford Medilog page mode display unit (PMD12) was the first commercial device to enable analysis of rapidly replayed aEEG in both a visual and auditory format. Source: From Ref. (15). Ebersole, JS (Ed.): Ambulatory EEG Monitoring. New York, NY: Raven Press, Wolters Kluwer Health; 1989:365.

4  •  Ambulatory EEG Monitoring

comparison. The first study undertaken by Rob Leroy and me was to design rationally aEEG montages that would maximize the detection of focal ictal and interictal abnormalities (6). Proper montages were clearly necessary before even attempting to evaluate the efficacy of the technique. Ambulatory EEG montages had to fulfill two goals. Although the first was spike/seizure detection, equally important was that the data had to be displayed in a form that was conductive to perception on rapid video playback. Nothing would be gained by recording an event that was overlooked on review. We thought that it might not be necessary to cover the head uniformly, and thus fewer channels would be reasonable. We recorded interictal and ictal EEG abnormalities from over a hundred adult and adolescent patients sequentially admitted to the West Haven VA EMU for monitoring and determined, perhaps really not to anyone’s surprise, that the frontal and temporal head regions bore the greatest percentage of epileptiform abnormalities, namely 78% (6). The central, parietal, and occipital regions, particularly in adults, were relatively quiet. Given that preferential sampling was a technical necessity with a three- to four-channel aEEG, it became clear that we had to concentrate our montages in these areas. We chose to develop three-channel montages because the fourth channel was commonly used for time/event marking or ECG. Montages with longitudinal temporal and transverse frontal derivations were found to be most useful. When these channels were organized into a chain in lefttransverse-right sequence, surface negative potentials in the frontotemporal regions appeared as a phase reversal common to two channels. This afforded enhanced perception of the most common focal abnormalities. Generalized discharges were easily detected, particularly in the frontal ­channel. We quickly learned that it was not a good idea to use a truly linked ­three-channel montage utilizing only four electrodes. Loss or significant artifact in one of the frontotemporal electrodes would confound two thirds of the data. Accordingly, separate electrodes were used for each of the three channels. Having our montage, we proceeded with a series of studies addressing the relative diagnostic yield of three-channel aEEG versus eight-channel LTM (which was standard at the time) (7,8). These were inpatient studies where the same EEG data were bifurcated into the LTM system and the aEEG recorder. In such direct comparisons with simultaneous recordings, aEEG fidelity was completely adequate and detection of abnormalities was consistent with the montage study. In a more practical comparison, 16 to 24  hours of three-channel aEEG (simulating the typical outpatient study) was compared with 24 to 72 hours of eight-channel cable telemetry. Seventyseven percent of those recordings thought to contain epileptiform abnormalities by LTM were correctly identified by aEEG. Moreover, 79% of focal and 100% of generalized EEG abnormalities were identified.

1.  The History of AMBULATORY EEG—A Personal Perspective  •  5

We had developed what seemed to be an optimal, though compromised, montage, and we had proved that even a three-channel EEG recording could detect a high percentage of abnormalities, including those focal, nearly as well as the current LTM setups and far better than routine EEG. A remaining question was how best to review 24 hours of recording with the new paginated video playback device. Turning 24 hours of video pages at the standard rate for examining routine EEGs would be very lengthy. Somehow we would have to learn to take advantage of the rapid replay capabilities and especially the audio transformation of EEG that was available. We quickly learned that during active wakefulness, there was an abundance of movement and muscle artifacts that made confident visual identification of interictal potentials difficult. When listening to the audio reproduction of EEG during active wakefulness played at rapid speeds, one heard only a cacophony of white noise elements. However, seizure rhythms, which are typically regular and evolving in frequency, would produce a clearly identifiable, changing pure tone that stood out even amid the noise of movement and muscle artifact. It became evident that we could safely scan through the 16 or so hours of active wakefulness at the fastest replay, 60×, by listening to the EEG rather than by looking at it. Seizures could easily be detected, and the problem of likely false identification of interictal spikes would be avoided. This meant that most of the recording could be reviewed at a 1 min/hr rate. Sleep on the other hand brought with it two advantages, namely little artifact and a natural increase in spikes. This was the time to identify interictal abnormalities. To do so would require slowing the replay to the 20× rate and looking for spikes. Montages that produced phase reversals for common frontotemporal spikes helped in this perception. If no spikes were found in the first one or two cycles of slow-wave sleep, the replay could be gradually increased in speed to lessen the total review time safely. This was so because the late phases of sleep are usually dominated by REM that characteristically contains fewer spikes. In fact, a later study, in which we attempted to provide statistics for this approach, showed that a careful visual analysis of simply the first hour of sleep would detect 92% of interictal abnormality types (9,10). Accordingly, the most time-efficient analysis, if that were necessary, would involve listening to 23 hours of a 24-hour recording for seizures and closely watching only the first hour of sleep for spikes. It was only a matter of a few years (1983) until the next significant ­development in continuous aEEG, namely the eight-channel recorder and playback unit. This recorder was only slightly larger than the four-channel device, but it allowed the recording of eight channels of EEG and one channel of accurate time and event for 24 hours on the same 1/8-inch cassette tape. This was only possible with a new technology called “blocked analog” (essentially a form of multiplexing), whereby more than one channel was recorded

6  •  Ambulatory EEG Monitoring

by the same recording head. This allowed for a more complex ­montage. There was the temptation to use long-standing eight-channel bipolar montages, but having learned lessons with the three-channel montages, as well as the benefits of the new montage design, we decided to maintain the same overall montage schema and place continued increased emphasis on the temporal and frontal areas (see Figure 1.2). The same channel sequence of left to right with mirror image symmetry continued to make the distinction between sleep complexes and focal spikes easier. The new playback unit also offered a number of improvements. These included digital real time as a separate channel, automatic search to a specific time, up to 64 seconds of memory, so that approximately 30 seconds before and after the present screen can be viewed without tape movement, gain and filter adjustments without tape movement, alphanumeric registry of channel gain and filtering status, and continuous printout of data as well as epoch printout. A new sound system was also provided for listening to the EEG. A channel mixer gave the ability to mix any number of channels into the right and/or left headphone. In so doing, one could listen to the left hemisphere activity in aEEG Montages

F7 F5

F6 F8

T1

F7

F1 F2

F8 T2

F7 T3

T6

T5

3 CHANNELS

T6

T5

4 CHANNELS

F3 C3

F4 C4

F8 T4 T6

T5

8 CHANNELS

1) T5 – F7

1) T5 – T1

A 1) T5 – T3

B 1) F3 – C3

2) F5 – F6

2) F7 – F1

2) T3 – F7

2) T5 – T3

3) F2 – F8 4) T2 – T6

3) F7 – F3

3) T3 – F7

4) F3 – C3 5) C4 – F4

4) F7 – F3 5) F4 – F8

6) F4 – F8

6) F8 – T4

3) F8 – T6

7) F8 – T4

7) T4– T6

8) T4 – T6

8) C4 – F4

FIGURE 1.2  Three-channel, four-channel, and eight-channel aEEG montages that all provide variations of the same frontal and temporal coverage. Channels are arranged to produce a display with mirror-image symmetry. Eight-channel montages are of linked configuration. Three-channel montages have separate electrodes for each channel. Source: From Ref. (15). Ebersole, JS (Ed.): Ambulatory EEG Monitoring. New York, NY: Raven Press, Wolters Kluwer Health; 1989:365.

1.  The History of AMBULATORY EEG—A Personal Perspective  •  7

one ear and the right in the other. This produced a stereophonic perception that made the detection of ictal rhythms easier as well. With a full eight channels, it was safer to use a true linked bipolar montage. However, because the channels were linked, it was essential to listen only to every other channel, so that there was not audio cancellation of any event or rhythm because of phase reversals in adjacent channels (Figure 1.3). The evaluation of partial epilepsies benefited from the introduction of eight-channel aEEG systems. Compared with simultaneous 16-channel cable telemetry records, the standard of the day, both three- and eight-channel aEEG reviews correctly identified 93% of the records as either normal or epileptiform (11). Lateralization of abnormalities was equally good with either cassette system, but more detailed characterization was achieved with eight-channel aEEG. Although 100% of seizures were detected on both systems, there were more false positive errors when only three data channels were available. Better ability to differentiate real abnormalities from artifacts was the most significant advantage of eight-channel over three- to four-channel aEEG. By 1988 and the beginning of the digital era, a PC-based replay system made its debut, and 16-channel continuous EEG recordings became possible by electronically linking two cassette recorders. Within the next decade, further progress was made. In 1996, a 17-channel continuous recorder was developed. This was possible by the introduction of large capacity removable hard disks, originally designed for use in notebook computers. With this new recorder, 16 channels of EEG and one channel of EKG could be recorded for 24 hours (at a 200-Hz sampling rate) onto a miniature hard drive within the device.

FIGURE 1.3  The Oxford Medilog 9000 was the first commercial eight-channel aEEG cassette recorder. Recorded data were reviewed on the Oxford Medilog 9000 rapid video/audio replay unit that offered stereo EEG sound. Source: From Ref. (15). Ebersole, JS (Ed.): Ambulatory EEG Monitoring. New York, NY: Raven Press, Wolters Kluwer Health; 1989:365.

8  •  Ambulatory EEG Monitoring

Montage reformatting became possible during replay. EEG analysis could be performed by rapid video review, audio transformation of signals from selected channels (up to 120× real time), and offline, computer-assisted spike and seizure detection. In addition to on-screen interpretation, sections of EEG containing features of interest could be printed onto individual sheets via laserjet or onto continuous paper via thermal printer. Before the turn of the century, 24- to 32-channel continuous recorders were introduced using flash memory chips for data storage. Various schemes were devised for analyzing the data. Most systems used offline spike and seizure detection software (12,13). The new continuous recording systems offered additional operational features. Originally, it was impossible to archive any EEG data other than the entire tape, since the complex tape recording scheme did not allow the data to be copied. Digital storage of EEG epochs by the new PC-based replay units eliminated this problem. Stored with these files might be patient information, review comments, and the final report, if desired. Direct, online patient monitoring became possible with some systems. Isolation electronics built into the replay unit allowed the ongoing EEG of patients, who were attached to recorders, to be displayed on the replay screen as scrolling waveforms. Alternatively, an optional laptop computer could be configured to serve as a display of ongoing EEG. In either case, the electronic EEG display could be used in lieu of a polygraph to test the quality of an ambulatory recording. Several systems also had the capability of displaying, editing, and analyzing (either manually or automatically) polysomnographic data, including oximetry. DISCONTINUOUS (EPOCH) aEEG RECORDING Historically, aEEG also developed along another line, namely the discontinuous or epoch recorder. The original concept in this evolution was that more channels could be recorded at a higher sampling rate, if only discrete epochs of EEG were recorded rather than continuous data. Ives introduced the first such recorder in 1982 (14). The recorder was like a commercial “Walkman” and used standard tape speed in order to achieve the frequency response necessary to record faithfully 16 multiplexed channels. The recording was done in selectable periodic epochs, such as 15 seconds every 10 minutes over 24 hours or the recorder could also be turned on by a push button, which the patient activated when he experienced a spell. An electronic buffer memory allowed recording of EEG prior to the button push. Approximately 45 minutes of EEG could be recorded on a tape. Both the amplifiers and multiplexing device were incorporated into one small box that was usually worn on the patient’s head and secured by a gauze turban. The 16-channel epoch recorder did not use a video playback device; instead, the recorded epochs were transcribed onto paper in real time. Analysis was like that of standard EEG.

1.  The History of AMBULATORY EEG—A Personal Perspective  •  9

Many of the deficiencies of intermittent and push-button EEG ­sampling were later overcome by linking the epoch recorder to a portable computer. This device monitored the ongoing EEG and used spike and seizure detection programs to identify segments of abnormal EEG that were stored to its hard disk. Although these computers were por­table, they were not truly ambulatory, for they required mains power. They were appropriate for use in a setting where the patient moved only a limited distance, such as from bed to chair. For a number of years, the most common discontinuous systems recorded 16 channels of EEG and two channels of other physiology, such as EKG, electromyogram (EMG), and electrooculogram (EOG). Up to 15 hours of data were recorded on the attached portable computer. This includes push-button actuations (with 2 minutes pre- and postpush), periodic sampling, and spike and seizure detections. The EEG was recorded in one of three bipolar montages, including a standard “double banana” montage. Data were routinely printed out on a laser-jet and reviewed like standard EEG or copied to a CD and reviewed digitally on any computer. These systems could also be configured with more polygraphic channels, in lieu of EEG channels, in order to record polysomnographic data (Figure 1.4). By the mid-1990s, an enhanced version of this 18-channel recorder was developed that contained within the waist worn recorder sufficient computing power to perform the spike and seizure detection. It was no longer necessary to attach the recorder to a portable computer in order to obtain online EEG analysis. Before the turn of the century, discontinuous recorders of 27- and 32-channels were developed. They offered the possibility of remontaging the output using referential reconstruction from bipolar recordings. The 27- and 32-channel systems include all of the standard electrodes in the 10–20 system plus basal temporal electrodes. Built-in, online spike and seizure detection was implemented in these recorders also. Later, a digital video recording system with a wide-angle lens was added to this line of discontinuous recorders. It is synchronized to the recorder to provide online monitoring of behavior just as with inpatient monitoring. Eventually, these two lines of aEEG recording technology converged. As digital storage devices increased in capacity and decreased in size and power consumption, continuous recording of 24 or more hours of 24  to 32 EEG channels became reality. A similar evolution of CPUs allowed simultaneous online spike and seizure detection within the confines of a small, truly ambulatory recorder. With such a device, both detections and the continuous EEG record would be available at the end of a recording session. Furthermore, the distinction between ambulatory and inpatient long-term scalp EEG monitoring is now, for the most part, only the location of the recording. No longer is there a major difference in the number of recording channels, the data sampling rate, or how the data are analyzed. Montages or analysis

10  •  Ambulatory EEG Monitoring

FIGURE 1.4  The Digitrace 1800 was an 18-channel discontinuous aEEG recorder that collected epochs of EEG selected by an event push button or by periodic sampling. These same events plus spike and seizure detections by a software program were stored on the attached Digitrace Home Computer. Source: From Ref. (16). Ebersole, JS. Ambulatory electroencephalographic monitoring. In: Aminoff MJ, ed. Electrodiagnosis in Clinical Neurology, 4th Edition, New York, NY: Churchill Livingstone; 1999:125–148. With permission from Elsevier.

strategies for aEEG no longer differ from those of traditional long-term monitoring. Inpatient monitoring continues to have the advantage, however, of convenient audio/video recording of behavior and the safety of antiepileptic drug reduction in a hospital environment. Ambulatory monitoring offers the convenience and cost saving of outpatient recording without hospitalization. CONCLUSION Unfortunately, in my opinion, one important innovation in the development of aEEG was lost in the evolution of digital EEG technology. When recording and analysis systems were analog, it was technically simple to

1.  The History of AMBULATORY EEG—A Personal Perspective  •  11

port the EEG data to headphones or a speaker. At rapid rates of replay, EEG rhythms became audible, and listening to the EEG became an essential form of analysis, particularly for seizures during active wakefulness. It was easy to hear the “pure tones” of a seizure rhythm despite the obscuring “white noise” of artifacts. When systems became fully digital it became necessary to add digital-to-analog converters to make the EEG audible. This extra cost plus the belief that the new spike and seizure detection programs would provide equal precision and considerable time-savings, marked the end of auditory analysis of EEG. However, this belief has not been borne out. Software detection tends to be either too sensitive, necessitating a fair amount of time to reject numerous false positives, or not sensitive enough, such that subtle transients or seizures are missed. Again, it is my opinion that there is no seizure detection system or program that is as sensitive and accurate as a trained human listening to a rapid replay of EEG. It may be old-fashioned, but the investment of 12 to 24 minutes to listen to 24 hours of recording at 60× to 120× is still worth it. It is my hope that LTM and aEEG equipment manufacturers again offer this form of data analysis, at least as an option.

REFERENCES 1. Holter NJ. New method for heart studies. Science. 1961;134:1214–1220. 2. Marson GB, McKinnon JB. A miniature tape recorder for many applications. Control Instrum. 1972;4:46–47. 3. Ives JR, Woods JF. 4-Channel cassette recorder for long-term EEG monitoring of ambulatory patients. Electroencephalogr Clin Neurophysiol. 1975;39:88–92. 4. Quy RJ. A miniature preamplifier for ambulatory monitoring of the electroencephalogram. J Physiol (London). 1978;284:23–24. 5. Stores G, Hennion T, Quy RJ. EEG ambulatory monitoring system with visual play-back display. In: Wada JA, Penry JK, eds. Advances in Epileptology. The Xth Epilepsy International Symposium. New York, NY: Raven Press; 1980:89–94. 6. Leroy RF, Ebersole JS. An evaluation of ambulatory, cassette EEG monitoring: I. Montage design. Neurology (Cleveland). 1983;33:1–7. 7. Ebersole JS, Leroy RF. An evaluation of ambulatory, cassette EEG monitoring: II. Detection of interictal abnormalities. Neurology (Cleveland). 1983;33:8–18. 8. Ebersole JS, Leroy RF. An evaluation of ambulatory, cassette EEG monitoring: III. Diagnostic accuracy compared to intensive inpatient EEG monitoring. Neurology (Cleveland). 1983;33:853–860. 9. Bridgers SL, Ebersole JS. The clinical utility of ambulatory cassette EEG. Neurology (Cleveland). 1985;35:166–173. 10. Bridgers SL, Ebersole JS. Ambulatory cassette EEG in clinical practice: experience with 500 patients. Neurology. 1985;35:1767. 11. Ebersole JS, Bridgers SL. Direct comparison of 3- and 8-channel ambulatory cassette EEG with intensive inpatient monitoring. Neurology (Cleveland). 1985;35:846–854.

12  •  Ambulatory EEG Monitoring

12. Gotman J, Gloor P. Automatic recognition and quantification of interictal epileptic activity in the human scalp EEG. Electroencephalogr Clin Neurophysiol. 1976;41(5):513–529. 13. Gotman J. Automatic recognition of epileptic seizures in the EEG. Electroencephalogr Clin Neurophysiol. 1982;54(5):530–540. 14. Ives JR. A completely ambulatory 16-channel cassette recording system. In: Stefan H, Burr W, eds. Mobile Long-term EEG Monitoring: Proceedings of the MLE Symposium, Bonn, May 1982. New York, NY: Fischer; 1982:205–217. 15. Ebersole, JS (Ed.): Ambulatory EEG Monitoring. New York, NY: Raven Press; 1989:365. 16. Ebersole, JS. Ambulatory electroencephalographic monitoring. In: Aminoff MJ, ed. Electrodiagnosis in Clinical Neurology, 4th Edition, New York, NY: Churchill Livingstone; 1999:125–148.

CHAPTER 2

Technical Aspects of Ambulatory Electroencephalography J. ANDREW EHRENBERG, bsc, R. EEG T., CNIM and CHARLES M. EPSTEIN, MD

INTRODUCTION Ambulatory electroencephalography (aEEG) refers to portable electroencephalography units, capable of prolonged periods of multichannel recording in an outpatient setting that encompasses normal daily life (1). While routine and sleep deprived EEGs are typically 20 to 30 minutes in duration with some ability to capture abnormalities, a normal routine EEG does not exclude a diagnosis of epilepsy (2). Even when interictal abnormalities are present in the routine scalp EEG, long-term monitoring can be beneficial to provide a greater diagnostic yield (3). While the utility and applicability of aEEG are well described in other portions of this book, this chapter deals with the technical aspects of aEEG. Ambulatory EEG is used as an adjunct to an epilepsy diagnosis; it may also be used to guide treatment in both adult and pediatric patients. Since aEEG will be recorded predominantly outside the controlled environment of a clinical neurophysiology laboratory, it is essential to provide the best initial technical setup possible, because the interpretation and clinical correlation assumes that all technical considerations are optimized during the entirety of the EEG recording time. While prolonged aEEG recordings outside of the laboratory environment (e.g., at home) are a clinically useful tool for a neurologist, these same features make a strong argument for requiring a fundamental understanding of the technological limitations and the underlying concepts of recording even more essential. Computer-assisted ambulatory EEG (CAA-EEG) uses complex computerized microprocessors and electronic components that are designed to provide prolonged EEG recording outside of a sheltered laboratory or hospital environment. Normally, this 13

14  •  Ambulatory EEG Monitoring

would allow technical issues to be immediately recognized and eliminated by qualified personnel. During aEEG recording, important technological challenges may arise. Throughout the history of EEG development, digital technology has replaced and made significant advances over the older analog systems that were initially utilized (4). It is in the field of aEEG monitoring where advances in technology and miniaturization of its components and enhanced memory have made the greatest impact. In fact, modern aEEG systems currently in use bear only a vague similarity to the initial 4-channel systems in the past. Prior to the incorporation of digital aEEG technology, the initial method of acquisition was recording to a cassette tape medium. This method would allow aEEG in addition to the audio equivalent of analog paper EEG. Many do not realize that in general, EEG utilizes some of the essential technological features that are necessary for recording music through translation and reproduction of analog signals. Similar to the initial trends in music development in the 1980s, the desire for a portable format led to the use of portable cassette tape players for music that were subsequently adapted to the same technology to begin aEEG. Similar to the development of portable music devices, the industry evolved from being recorded and played on cassette tape to storage on digital media (e.g., compact disc, MP3 players); CAA-EEG systems also evolved to storage on digital media. While the specifications vary widely from vendor to vendor, most CAA-EEG systems share some common features. It is important to obtain the individual specifications of an aEEG machine that will meet the needs intended for clinical use that is anticipated. Just as the diagnostic question prompting aEEG in the first place must be specific enough to prove useful, so too must the specifications of the machine for the intended use (5). In this chapter, we explore the technical aspects of CAA-EEG recording. This will include the basic technology of a computer-based EEG system concentrating on the technical aspects relative to aEEG and speculation about the technological changes that could impact devices used for ambulatory EEG monitoring in the near future. BASIC TECHNICAL ASPECTS OF aEEG In discussing the technical aspects of aEEG, it is essential to understand the general technical properties that apply to neurodiagnostic procedures that can impact integrity of recording by producing artifact. Functionally, these properties that may produce artifact can be divided into four principal sources during aEEG recording (Figure 2.1): 1. The bioelectrical activity and recording electrodes 2. The differential amplifier

2.  Technical Aspects of Ambulatory Electroencephalography  •  15

Bioelectrical changes–One or more differential amplifiers ***Analog signal/waveforms***

Bioelectric waveforms–one or more differential amplifiers

Internal digital storage

Ambulatory recorder

Pre amp / Diff amp

Patient

AA and A to D

Analog electrical changes–analog to digital converted– ’Machine language’ (i.e., numerics) saved ***Binary data***00110010

Analog to digital conversion into binary data

EEG software and display

[F8-ref]-[T4-ref] Data read via software– remontaged, filtered, etc. ***Reconstructed signal/waveform*** Reconstructed data via software- Remontaged, filtered, displayed

FIGURE 2.1  Basic technical components of an aEEG system.

3. Digitization and storage of the analog electrical waveforms 4. Reconstruction and display of activity for review Bioelectrical Activity and Electrodes Electrode artifact is the most common type of artifact limiting the quality of aEEG monitoring. As an integral clinical neurophysiological study utilized for nearly a century, the long-term EEG and aEEG remain best suited for the evaluation of epilepsy. The basic principles used by Neminsky (6) and later Hans Berger (7) have not significantly changed over the years and still lie at the foundation for most electrophysiological modalities within the field of clinical neurophysiology. The bioelectrical fluctuations of the brain, used to determine normal and abnormal physiological function, are recorded using conductive electrodes. The voltage changes in the brain are attenuated, and to some degree, filtered by the skull and scalp (8,9). As such, the cortically generated activity at the scalp’s surface is significantly smaller than environmental noise. Conductive gel or paste creates an ionic medium between the scalp and the metal of the electrode, creating an “electrical double layer” (Figure 2.2). Most commercially available conductive paste has some adhesive properties and is usually sufficient for routine EEGs in a clinical environment where the patient is not moving and any technical issues can be observed and corrected. As with any types of EEG chosen for clinical use, electrodes composed of silver/silver chloride and gold are preferred (Table 2.1). The use of disposable, single use electrodes represents the best means of providing infection control in clinical practice (10). The initial impedances of the electrodes should be maintained below 10 kΩ (11), balancing minimal skin abrasion with attention to the least interelectrode impedance that can be achieved. This is essential as skin breakdown is a real concern during prolonged aEEG

16  •  Ambulatory EEG Monitoring

e– e– e– e–e– +

+

e– e–

+ + – e– e +

Electrical “double layer” as opposite charges line up + +

e– e–

+

+

+

Conductive fluid +

e– – + e

Skin, keratin, sweat, etc. act as a resistor to the electrical activity

Skull e–

e–

e–

e– e–

Skull acts as resistor to electrical activity

Brain

FIGURE 2.2  The electrical double-layer outlining the separation and resistance to transmitting electrical charge.

TABLE 2.1  Relative Reactivity Based Upon Metallic Substance Conducting an Electrical Charge Substance

Conductivity

Silver/silver chloride (most stable)

6.17 × 107

Copper

5.80 × 107

Gold (inert, less half-cell)

4.10 × 107

Seawater

4

Rubber

10−15

recording sessions where no upkeep or repair can be performed on a regular basis. Impedances should routinely be recorded at the beginning and at the end of the recording session so that the interpreting physician can assess this in the interpretation of the recording. The conductive and adhesive properties of adhesive pastes are short lived and are therefore insufficient for prolonged recording times. It is important to fix the electrodes to the scalp with some form of approved long-acting skin adhesive such as collodion to ensure recording integrity is maintained throughout the aEEG monitoring session, which is then secured by a Kerlix gauze head-wrap. The extra time taken to ensure adequate interelectrode distances and the added care in how they are applied and then secured to the scalp will help to ensure a technically successful aEEG recording. The interpreting electroencephalographer is best served by becoming familiar

2.  Technical Aspects of Ambulatory Electroencephalography  •  17

with the policies and procedures of performance, the technologists involved with conducting the recording, and technical adequacy or lack thereof of the system utilized in the laboratory during the period of connecting, processing, and annotating the aEEG. It is vitally important that the standard practice of measuring and marking electrode derivations on the scalp for electrode application is understood and consistently adhered to for optimal technical recording. A cartoon of the electrode–scalp interface is illustrated in Figure 2.3 and the actual setup in Figure 2.4. The limited portability of the early aEEG systems and the limited internal digital memory storage that was previously available resulted in

1”×2” gauze with collodion Electrode wire Electrode

Cond. gel

Scalp

FIGURE 2.3  Cartoon of the initial electrode application and scalp interface.

FIGURE 2.4  Preparation and application of electrodes in a mannequin and human. (Courtesy of Lindsay Ireland, 2011, with permission.)

18  •  Ambulatory EEG Monitoring

initial 16-channel CAA-EEG recordings. When additional physiological or ­polygraphic channels were required, the additional information came at the cost of sacrificing the number of channels utilized for EEG. With the technological evolution of digital systems, modern aEEG systems are now easily capable of accommodating 21 channels of recording with some systems able to accommodate up to 32 channels including additional input for extracerebral direct current channels for physiological monitoring (e.g., sleep monitoring). Digital systems, particularly in aEEG, commonly utilize a “system reference” or “machine reference” (4). These inactive reference sites usually reflect the proprietary selection of a midline vertex location due to the central position that is less prone to movement and myogenic artifact. Locations in the vertex midline (such as FCz or CPz) may be used, but care must be taken to avoid a salt bridge with nearby electrodes that “cancel” the bioactive signal (12). Differential Amplifiers The discovery that neural activity could be induced by external artificial electrical stimulation marked the start of modern electrophysiology in 1791 (13). Thirty years later, a method to record changes in electrical current through deflection by a needle led to discovery of the “galvanometer” that formed the basis for recording electrophysiological potentials, ultimately resulting in the first human “electrogram” of the heart (2). Spontaneous electrical rhythms of the mammalian brain were first demonstrated in 1870 by the physiologist, Richard Caton (14). Refining recording techniques gave rise to the string galvanometer following the application to recording the heart rhythms with the electrocardiograph in 1902. Hans Berger subsequently used Einthoven’s string galvanometer to study the electrical mechanisms of the brain, which would later become the method of monitoring brain signals that would revolutionize clinical neurology and neuropsychology research. Today, technology has evolved to implement the use of a differential amplifier with new software designs and smaller compact recording devices that are amenable to aEEG (Figure 2.5). Conceptually, amplifiers require two inputs and an earth ground. The output of the electrical current measured is the summated difference between the two inputs. Each input will also contain a significant amount of environmental “noise” representing artifact that can then “contaminate” the tracing. This source of artifact is up to 10,000 times larger than the EEG signal that is being measured. The fluctuating electrocerebral activity recorded from scalp EEG that passes through a differential amplifier is analogous to two different people in two separate boats who are riding on the waves of an ocean. Because both people on their separate boats are equidistant to the bottom of the ocean, nullifying the distance to the ocean permits a more precise measure between the heights of the people. In this example, the ocean is the environmental artifact that is common to both people in their boats, and the distance between them reflects

2.  Technical Aspects of Ambulatory Electroencephalography  •  19

the ­changing electrical potential of interest through common mode r­ ejection (CMR). More recently, active reference configurations are commonly designated by the anachronism “driven right leg.” This averages the commonmode signal representing the entire head and reintroduces its inverse into another (driven) electrode rather than using a ground. In many circumstances, this approach can reduce common-mode “noise” (Figure 2.6). The accuracy with which the electrocerebral signals of interest can be isolated from noise is the signal-to-noise ratio (SNR). The potential for artifact to be generated by movement of the recording electrodes and wires, or from a cable if a connector box is used, has led to development and placement of the amplifier close to the body such as on the top of the head within the head wrap that secures the electrodes. +

–

Input 1 Electrical potential (I1)+ common mode ‘noise’

Output Difference (V) of electrical

‘ground’

potentials (I1+noise)-(I2+noise)=V

Input 2 Electrical potential (I2)+ common mode ‘noise’

FIGURE 2.5  Basic design of a differential amplifier with the cancellation of “noise” noted at the output.

I 1

Sys ref

R

Sys ref

I 2

I 3

Sys ref

FIGURE 2.6  Basic design of an active, “driven right leg” ground.

Common mode from all electrodes, inverted, then ‘injected’ back into head Average

20  •  Ambulatory EEG Monitoring

Just as the electrodes have an impedance to current flow from the scalp to the machine (electrode impedance), so does the input to the amplifier (input impedance). However, input impedances occur in the range of megaohms as opposed to electrodes that are maintained in kiloohms (11) in most modern systems. With an increasing amplifier input impedance, the electrode impedance lessens as a significant factor and thus the SNR improves and accuracy increases (15). Current systems allow data to be manipulated in many different ways, including changes to the montage, sensitivity, filters, and so forth. This is made possible by relying on the system reference (also known as machine reference). This reference is usually a midline parietal or frontal electrode that should be kept separate from the other electrodes of the 10–20 ­electrodes and from the ground. Systems using a reference (Figure  2.7) record the input of the second channel (input 2) of the entire electrode array as the same ­system ­reference electrode (Table 2.2). The system reference and ­recording in this design contributed greatly to the digital revolution in EEG (12).

Amplifier 1–Fp1-SysRef Amplifier 2–Fp2-SysRef Amplifier 3–F7-SysRef

Z Right

Left

Amplifier n – ##-SysRef Amplifier n – ##-SysRef Amplifier n – ##-SysRef Amplifier n – ##-SysRef

R

FIGURE 2.7  Graphic of a system reference.

TABLE 2.2  The Relationship Between a System Reference Montage and a Bipolar Montage Amplifiers

Input1–Input2 G1–G2

Channels (Displayed)

Channels (Actually)

1

Fp1-SysRef

Fp1–F7

(Fp1-SysRef)–(F7-SysRef)

2

F7-SysRef

F7–T3

(F7-SysRef)–(T3-SysRef)

3

T3-SysRef

T3–T5

(T3-SysRef)–(T5-SysRef)

4

T5-SysRef

T5–O1

(T5-SysRef)–(O1-SysRef)

5

O1-SysRef

–

–

2.  Technical Aspects of Ambulatory Electroencephalography  •  21

Digitization and Storage of the Analog Electrical Waveforms Using an analog system, continuous electrical voltage fluctuations were encountered from each electrode from the 10–20 recording sites to the system reference and then conveyed to the pen writing mechanism (Figure 2.8). The step of converting analog to a digital format allowed for EEG storage and visual display by computers and was accomplished by developing the electronic analog to digital converter. This involves a process that is the same technology used in audio recording that records analog sound waves onto digital media. How this is accomplished and why it is recommended to sample at a rate of at least three times the highest frequency requires detail (11,15). The Nyquist–Shannon sampling theorem is fundamental to the field of digital signal processing and has had a significant impact on the practice of clinical neurophysiology. Any parameter that fluctuates can be measured at any specific point in time. This fluctuation may then be compared or plotted against other measures that occur at another point in time from the temperature, to the stock market, to the signals encountered in the EEG. One of the factors that has to be decided is how many sampling points are necessary. With temperature or stock market highs and lows, there are two samples needed per day, the peak and the trough at the highest and the lowest point. Using the analogy of temperature, sampling error may limit precise representation at any time if the sampling is not frequent enough. In the case of temperature, if the average earth high temperature is 98°, it provides little information as to the current temperature outside at a particular point in time. This is the basis of the Nyquist–Shannon sampling theorem, which addresses how many time points are needed to accurately represent and reproduce continuous fluctuation.

Paper

FIGURE 2.8  Paper writing mechanism and principles of construction in analog EEG.

22  •  Ambulatory EEG Monitoring

Most sources require recording at more than twice the highest frequency represented by the data though in EEG; three times is probably the minimum  (11). Nonetheless, it is important to understand why and what the impact is from too low a sampling rate. In a continuous signal, recording discrete voltages over time will demonstrate that two sample points ­peak-to-peak are insufficient and may represent the signal at a false (lower) frequency (also known as aliasing). It is not until about eight times the frequency of the waveform that a visually acceptable reproduction of the original signal is obtained (Figure 2.9). Each sample point for each channel recorded requires a separate sampling rate. With the traditional high frequency, low-pass filter setting of 70 Hz (11), a sampling rate of 256 Hz would provide less than four sample points per waveform, and therefore 500 Hz would be needed for greater than eight points. Adequate A to D conversion and antialiasing sampling rates are essential components to digital aEEG. All current aEEG systems are likely to meet or exceed the threefold sampling rate to satisfy the Nyquist theorem with 16-bit analog to digital converters available with a dynamic range of ±1.638 mV, and therefore, these specifications are no longer critical limitations when purchasing digital EEG systems (15). Modern aEEG has been made possible by the advances in computing technology over the last 50 years. Moore’s law predicts doubling computer capacity every 2 years. It could also be extrapolated that miniaturization of digital components is a corollary. Many contemporary smartphones, for example, have thumbnail sized 64-GB storage capacity, more than double the 30 GB minimum previously available for 24 hours of continuous digital aEEG recording.

FIGURE 2.9  Sample points in the first row of waveforms inadequately represent the signal when the sample points are connected (vertical lines) to produce a signal that is an “alias” of the original signal that is properly represented in the second row by a greater number of data points.

2.  Technical Aspects of Ambulatory Electroencephalography  •  23

The flash storage disk from an aEEG system from 10 years ago can be seen in Figure 2.10 compared with the current 2016 model. The size has not only decreased significantly over time, but the capacity for computer memory storage in the larger, older drive is only 440 MB while the much smaller, current one is 128 GB. The small circuit board (Figure 2.10A) is from an 8-channel EEG preamp in 1996 (test date 1994). With that in mind, using Moore’s law, over the past 20 years, the 8-channel 1996 preamp would be able to have over 4,000 channels preamplified (i.e., 8-16-32-64128-256-512-1024-2048-4096). While the number of EEG channels that are subserved by a single preamp is not the principal limiting factor in aEEG recording, the comparison between technology over time illustrates the impact of computerization within the field of digital development in the computer era. While the recommended minimum is 16 channels of aEEG (with a preference for 21 channels plus ECG), the number of EEG channels was previously a limiting factor for most aEEG systems a decade ago facilitated by portability of the system due to larger size and the limited internal memory storage capacity. Modern aEEG systems easily accommodate a full 21 channels place in accordance with the International 10–20 system of electrode placement with extra inputs for physiological recording. Internal storage capacity continues to increase in size while the housing shrinks in physical size to become lightweight and portable during aEEG recording. In the cases where a proprietary aEEG system provides a limited number of inputs, this situation usually reflects a design that is specifically developed for simplicity and a focused intension to use it for a singular reason. It is not designed that way due to the limitations from additional impact of adding the electronics of a few more channels. A

B

FIGURE 2.10  (A) Memory card (1996) with 440 MB and (B) a flash memory disc (2016) with 128 GB of memory capacity. Note the smaller size in 2016. (Courtesy of Lindsay Ireland, 2016, with permission.)

24  •  Ambulatory EEG Monitoring

The 24 hours of data that are necessarily a minimal amount of memory capacity for storage is based on an average of 30 GB as a standard. This applies to the use of additional electrodes that are included and is not negated by using video technology. Video recording including color and infrared cameras (nighttime) has become standard practice and comprise the larger component necessary for storage. Differing recording parameters and configurations with comparison of resulting file sizes for different scenarios can be seen in Table 2.3. In regard to sampling rate, for files that contain EEG and video, even when abbreviated or “clipped” samples are selected for long term retention, the difference observed between the file sizes is negligible. Even with twice as many EEG channels at twice the sampling rate, the difference between 1 hour of video-EEG, when the EEG is composed of 16 channels with a sampling rate of 256 Hz, and 32 channels of video-EEG sampled at 512 Hz, is less than 4% of the total amount of data accumulated for storage. Reconstruction and Display of Activity for Review Modern aEEG systems are technologically very similar to scalp video-EEG monitoring systems used in an epilepsy monitoring unit (EMU) even for those performing intracranial EEG. The differences between aEEG and EMU systems lie primarily in the procedural aspects of use. Limitations in technical support to “trouble-shoot” problems can arise during aEEG recording

TABLE 2.3  Individual aEEG Recording Scenarios and Resultant Approximate File Sizes for Comparison

Configuration

Sampling Rate

Time Period

Approximate Resulting Total File Size

16 channels

256 Hz

24 hr

200 MB

16 channels

256 Hz

48 hr

400 MB

16 channels

256 Hz

72 hr

600 MB

32 channels

256 Hz

24 hr

400 MB

16 channels

512 Hz

24 hr

400 MB

32 channels

512 Hz

24 hr

800 MB

32 channels and color video

512 Hz

24 hr

10–25 GB

32 channels and color video

512 Hz

1 hr

512 MB–1,228 GB

16 channels and color video

256 Hz

1 hr

494–1,210 GB

2.  Technical Aspects of Ambulatory Electroencephalography  •  25

(and hence interpretation) in an outpatient environment. Compared with an ­inpatient ­scenario, aEEG is unable to offer the safety of antiseizure r­ eduction outside of the hospital setting. However, both long-term EEG systems should technically be able to record until sufficient data have been obtained involving the targeted interictal or ictal activity. In most aEEG recording systems, an independent computerized system is necessary to begin and end the recording session and then download data for analysis. The EEG data that have been acquired post hoc can then be remontaged and filtered as necessary. The basic concepts and impact of this are important to observe and understand. Montages may be modified after EEG is acquired with interpretation based on bipolar (A–B, B–C, C–D) and referential (A-ref, B-ref, C-ref) montages  (Figure  2.11). Special montages including those reformatted into Laplacian or

A

B

16 6

1 2

7

20

12 17

3

8

21

13 18

4

19

2

1

10 11

7

12

13

2

19 23

11

3

12

2 2

8

15 14

7

13

4 4

6 5

1 1

16 16 11 11 12 13 12 13 14 14

6 6 7 78 8 9 9

1 1

3

6

17 18 22

3

9

16

2

4

14

9

1

5

10 1

11

6 6

16 16 17 21 12 17 21 12 13 18 3 8 1314 18 3 4 89 14 19 9 22 19 4 22 10 20 10 20

8

7 27 2

14 15 20 24

2 2

FIGURE 2.11  (A) Bipolar montages displaying: 1. anterior–posterior longitudinal, 2. transverse, and 3. circle montages. (B) Referential montages displaying: 1. ipsilateral ear and 2. vertex references in aEEG.

17 17 18 18 19 19

26  •  Ambulatory EEG Monitoring

weighted averages may also be displayed. Using an initial digital system reference  recording  allows for later remontaging (Figure  2.12A–D) into whichever montage best suits the individual patient’s activity that is being examined. A

B

(continued ) FIGURE 2.12  The same page of EEG of a 20-channel EEG using (A) a system reference montage, (B) anterior to posterior (anatomic) bipolar montage.

2.  Technical Aspects of Ambulatory Electroencephalography  •  27

C

D

FIGURE 2.12  (continued ) (C) transverse bipolar montage, and (D) Cz reference montage.

IMPORTANT CONSIDERATIONS AND FUTURE TRENDS The initial baseline aEEG recording setup is essential to identify any problems with performance and to demonstrate the appearance of common responses of the machine to waveforms (Figure 2.13). By reproducing frequent activities common to everyday life (e.g., eyes open, eyes closed, speaking) and to have the patient mimic individual activities from daily life, the impact

28  •  Ambulatory EEG Monitoring

FIGURE 2.13  Various display factors of a calibration signal during a setup of aEEG.

on the aEEG may be assessed prior to recording. Photic stimulation and hyperventilation are usually not performed prior to aEEG. These activating techniques are typically performed as part of a routine scalp EEG that is in most cases completed prior to aEEG. A 60 Hz (also known as “notch filter”) should not be required during the baseline. The rationale is that if excessive electrical artifact is present in the EEG laboratory setting to require filtering, this would indicate that the aEEG recording is likely to be fraught with artifact once the patient enters the habitual environment for aEEG recording. Ambulatory EEG is often recorded in one (system reference) montage, though during the baseline, montage changes should be recorded to ensure they will be recorded adequately during post hoc analysis for interpretation. Normally during routine scalp EEG, there is a tab on the equipment for noting “movement” or “seizure activity,” one for a change in the level of consciousness, and others that help document the behavior and results of any interactions during seizures and paroxysmal neurological events. This is also able to be accomplished in aEEG through multiple but complementary tools. A patient diary and event log, with clear instructions on what items to

2.  Technical Aspects of Ambulatory Electroencephalography  •  29

note, supplement video recording and EEG to document a behavioral event (although individualized and subjective). Time correlation may be linked if the recording unit has a time display that can be correlated with the patient notes to integrate the diary entry with an event marker identifying a push button activation by the patient or witness. Two drawbacks to event markers, however, are the potential for accidental push button activations and the tendency to focus on events neglecting the recording as a whole. Many patients are unaware of their seizures or mark them only after a considerable delay. Other patients may mark minor sensations and be over-vigilant to nonepileptic events that are irrelevant to the typical symptoms that are associated with epilepsy. Video recording that is time-synchronized to the aEEG recording is an important method that allows objective firsthand visual analysis. However, the use of video recording adds cost and weight to the ambulatory unit, significant increases in resulting memory file sizes needed for storage, and requires that patients ensure they remain in view of the camera. In pediatric aEEG (Chapter 6), there is greater need for additional physiological and polygraphic channels to measure variables beyond EEG that should be readily achievable with modern aEEG systems. In addition to ECG, respiratory monitors, electromyogram (EMG), and electroocculograms (EOGs) are especially helpful in conjunction with synchronized video during aEEG. This not only can help differentiate artifacts (e.g., respiration or ECG), and sleep cycling, but can help to determine the diagnosis when “events” are nonepileptic and instead are physiological for conditions such as altered consciousness, convulsive movements from asystole, and periodic limb movements during sleep or apnea-related arousals. Automatic Computerized Analysis, Autoclipping, and Seizure Detection Automated computerized analysis to identify seizures and spells or interictal abnormalities can be useful as an adjunct. However, all data and video EEG recordings “must be available until reduced by qualified personnel” (10). Technical staff performing the function of data reduction, or what is commonly referred to as “clipping,” should be well versed in what should be selected, preferably supervised by credentialed electroencephalographers, and should always err on the side of caution if in any doubt. In the past, some systems used a combination of automated software for “seizure recognition,” “spike detection,” and interval sampling to address the internal storage limitations of aEEG. The technique of “autoclipping” should not be used with modern systems. The internal high-capacity storage medium has become so commonplace that its use cannot truly be justified. All recorded data should be available for full review after aEEG recording. This does not imply that spike and seizure detection software should not be

30  •  Ambulatory EEG Monitoring

utilized; it is often very helpful, particularly when quantification is needed. However, it should never be considered a replacement for review of all of the data obtained and reduced by qualified personnel. Neither automated seizure recognition, event push buttons, nor the combination of both is adequate to ensure that all seizures are captured. These remain merely adjuncts to the trained human interpreter. Current Cutting Edge and Future Trends Portable high-quality, high-resolution video has recently become much more widely available with aEEG. While this is partially related to smaller and more portable cameras able to record high-quality video, compact digital storage has reached sufficient capacity to make the routine use a reality for aEEG with good-quality video. The application of video is relevant to aEEG for several reasons. First, visual observation may allow significant detail of a behavior to enable adequate assessment for important but minute behavioral changes. Second, correlating behaviors to electrographic changes are able to be synchronized with at least 30 frames per second of video linked to EEG accurately (within 33 ms for some systems). Additionally, some capabilities are available for real-time viewing of the video complement with live streaming to verify that the patient is adequately on the display field/screen to ensure adequacy and extent of the recording area. Finally, including audio/microphone serves as a complement to “real-life” scenarios that are similar to the quality typically used in an EMU inpatient setting. The incorporation of video into aEEG constitutes an important conceptual rather than technological change. One uniquely irreplaceable feature of aEEG, when compared with the hospital-based EMU, is the ability to study the patient in his/her normal environment. This is in contradiction to the environment where the patient is constrained to a bed spending many hours a day in front of a camera. In the later case, normal behavior is disrupted, and the habitual events may fail to occur and be recorded for interpretation. Proper use of an event push button is essential with video, perhaps moreso than with EEG, as visual scanning of the video for an entire 48- to 72-hour record is impractical. Wireless networks have become commonplace. They are present in many homes to facilitate aEEG and have sufficient data transfer speeds to make aEEG in real-time possible through remote access. It is important to denote in advance the expectations and ability of the patient or caregiver to comply with the aEEG monitoring instructions. Similarly, it is essential to convey the importance that aEEG is not real-time continuous monitoring and the absence of a qualified individual supervising the monitoring. Data transfer should function at the minimum digital transfer time delay that is required for real-time EEG interpretation. While DSL (1–3 Mb/s) allows for real-time EEG review, it is important to note that a T1 line level of speed (>1.54 Mb/s)

2.  Technical Aspects of Ambulatory Electroencephalography  •  31

would be needed for real-time EEG with video (4). Consideration needs to be maintained relative to patient privacy, securing a connection when two-way interactivity is used during continuous aEEG. Emergency instructions and remote intervention beyond notifying 911 is not yet available, and remote troubleshooting during aEEG monitoring is improving. However, the technological advances for aEEG with the advent of chronic intracranial aEEG and the development of bioelectric and chemodelivery feedback methods have promising implications for future developments in performing aEEG. REFERENCES 1. Gilliam F, Kuzniecky R, Fought E. Ambulatory EEG monitoring. J Clin Neurophysiol. 1999;16(2):111–115. 2. Smith SJM. EEG in the diagnosis, classification, and management of patients with epilepsy. J Neurol Neuro & Psych. 2005;76(Suppl 2):ii2–ii7. doi:10.1136/ jnnp.2005.069245 3. Wyllie E. The Treatment of Epilepsy: Principles and Practice. 3rd ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2001. 4. Maus D, Epstein CM, Herman ST. Digital EEG. In: Schomer DL, Lopes da Silva FH, eds. Neidermyer’s Electroencephalography. Philadelphia, PA: Lippincott Williams and Wolters Kluwer business; 2011: 119–142. 5. Schomer DL. Ambulatory EEG telemetry: how good is it?. J Clin Neurophysiol. 2006;23:294–305. 6. Pravdich-Neminsky VV. Ein Versuch der Registrierung der elektrischen Gehirnerscheinungen. Zbl Physiol. 1912;27:951–960. 7. Berger, H. Über das Elektrenkephalogramm des Menschen (On the human electroencephalogram). Archiv f. Psychiatrie u. Nervenkrankheiten. 1929;87:527–570. 8. Fuchs M, Wagner M, Kastner J. Development of volume conductor and source models to localize epileptic foci. J Clin Neurophysiol. 2007;24(2):101–119. 9. Krauss GL. Digital EEG. In: Neidermyer E, Lopes da Silva F, eds. Electroencephalography. Philadelphia, PA: Lippincott Williams & Wilkins; 1999:781–796. 10. ACNS. Guideline 12: guideline for long term monitoring for epilepsy. Am J Electroneurodiagnostic Technol. 2008;48(4):265–268. 11. Sinha SR, Sullivan L, Sabau D, et al. ACNS Guideline 1: minimum technical requirements for performing clinical electroencephalography. J Clin Neurophysiology. 2016;33(4):303–307. 12. Acharya J, Hani A, Cheek J, Thirumala P, Tsuchida T. ACNS Guideline 2: guidelines for standard electrode position nomenclature. J Clin Neurophysiol. 2016;33(4):308–311. 13. Galvani L (1791). De Viribus Electricitatis in Motu Musculari Commentarius (Commentary on the Effect of Electricity on Muscular Motion). 14. Millett D. Hans Berger: from psychic energy to the EEG. Prospect Biol Med. 2001;44(4):522–542. 15. Halford JJ, Sabau D, Drislane FW, Tsuchida TN, Sinha SR. American Clinical Neurophysiology Society Guideline 4: recording clinical EEG on digital media. J Clin Neurophysiol. 2016;33(4):317–319.

CHAPTER 3

INSTRUMENTATION AND POLYGRAPHIC EEG ELIZABETH WATERHOUSE, MD

INTRODUCTION Advances in digital EEG technology have improved ambulatory EEG (aEEG) recordings in numerous ways: expanding the number of channels available, lengthening the recording time, allowing recording of multiple physiological parameters, and reducing the size and weight of the equipment for easier portability. Some aEEG systems now include a portable video camera that can be set up in the patient’s home to record video-EEG. INITIAL aEEG SYSTEMS The first commercially available aEEG relied on analog technology to record 24 hours of up to four EEG channels on a cassette tape. ­Single-channel preamplifiers were attached to the scalp close to the electrodes, in an effort to reduce movement artifacts (1). Experienced electroencephalographers may recall devices of the 1980s, when signal multiplexing allowed expansion to 16 channels, improving localization capability and spatial resolution. Due to limitations in cassette tape storage capacity, data reduction was necessary. Combining the recorder with a portable computer enabled ­real-time ­processing. Rather than continuous EEG, stored data included spike and ­seizure detections, EEG sampled at regular intervals, and time sequences surrounding patient event marker activations (2). Over subsequent decades, instrumentation included digital technology that has overcome the technical and quality challenges of early aEEG systems. Current aEEG recordings are comparable to those recorded onsite at an EEG laboratory or video-EEG monitoring unit. Various commercially available systems now offer high-quality lightweight multifunctional s­ ystems for monitoring EEG and polysomnography at home. 33

34  •  Ambulatory EEG Monitoring

CURRENT aEEG SYSTEMS Currently available aEEG devices allow for 24 to 34 channels, and thus have the capacity to record at least as many channels as are routinely recorded in the hospital EEG laboratory and video-EEG monitoring unit. Studies indicate that 16-channel recordings with computer-assisted seizure ­detection are better at capturing ictal events than older systems with eight or fewer channels (3,4). Ambulatory EEG montages should include a minimum of 19 ­standard scalp electrodes and one channel for EKG. Additional electrodes that are desirable include ear electrodes and electrodes to monitor eye movements. Polysomnogram recording requires fewer scalp electrodes and uses ­additional channels for limb movements, electromyogram (EMG), eye movements, oximetry, thoracic respiratory effort, and nasal air flow. RECORDING THE aEEG Patient Instructions The technologist should ensure the appropriate care of the patient and aEEG device. Patients should not bathe with the device or expose it to water. They should avoid chewing gum, which causes prolonged intervals of phasic myogenic artifacts during chewing. The technologist should demonstrate and test the use of the event marker (“push button”) and advise the patient how to document routine activities as well as seizures or clinical events on the activity diary. If video is used, the patient must know how to turn the camera on and off. Ambulatory EEG systems record and store a minimum of 24 hours, with some models now offering up to 96 hours of continuous recording. Several factors limit recording time. The integrity of the electrode–scalp interface degrades over time as the electrode paste dries out, leading to decreased conductance and increased number of artifacts. Battery life is a second potentially limiting factor. Patients may need to be instructed in changing or recharging b ­ atteries. Those undergoing prolonged studies may need to return to the laboratory for electrode maintenance and a battery change. Scalp Electrodes In most cases, a standard 10–20 array of electrodes is applied using c­ ollodion, an electrode cap, or other secure technique. Collodion technique is followed by wrapping the scalp with gauze. To reduce traction on the scalp ­electrodes, the lead wires are gathered and tacked. Home polysomnography systems utilize scalp electrodes and chest strain gauges held in place with adjustable elastic straps. An aEEG is frequently obtained after a routine or sleep-deprived EEG has failed to capture the desired diagnostic information. The prolonged recording

3.  INSTRUMENTATION AND POLYGRAPHIC EEG  •  35

period of an aEEG increases the diagnostic yield, and the addition of special electrodes may further aid in diagnosis and localization. True anterior temporal electrodes are placed 1 cm above the point at 1/3 of the distance between the outer canthus of the eye and the preauricular point. Subtemporal electrodes may also be used (5). Additional options for specific localization include ­zygomatic or sphenoidal electrodes, although these are not routinely used. Eye Movements In addition to scalp EEG, other parameters may be monitored. Eye leads are used to differentiate artifacts related to eye movements from frontal EEG activity and to identify drowsiness and rapid eye movement sleep. They also help to confirm lambda waves, surface-positive occipital potentials that occur in the setting of visual scanning. A corticoretinal potential exists in the eyeball, with the cornea relatively electropositive compared with the retina. This potential allows eye movements to be recorded by electrodes. The first eye lead is placed lateral to and above the outer canthus of one eye, while the second eye lead is placed lateral to and below the outer canthus of the other eye. When constructing a montage, each lead may be referenced to the same electrode (usually an ear electrode). This allows conjugate eye movements to appear out of phase, while frontal activity of brain origin will appear in phase. EMG and Body Movements A prolonged EEG in the home setting offers advantages in terms of cost, ­setting, and the patient’s freedom to carry out his or her usual activities. These advantages are counterbalanced by artifacts from movements and muscle that may obscure the EEG signal. During wakefulness, motions involved in tooth brushing, scratching, patting, or bicycling are often r­ hythmic and may be differentiated from seizure activity if recorded by noncerebral electrodes as well as those on the scalp. A limb electrode is helpful in recording tremor. Review of the patient’s activity log, or associated video, provides collateral information. During sleep, muscle and activity-related artifacts tend to attenuate. Polysomnogram recordings include submental and limb EMG channels to aid in sleep staging and to identify periodic limb movements. EKG A chest lead that records heart rate allows the interpreter to identify EKG artifacts on the EEG and may yield diagnostic information. A study of over 800 neurology patients tested with EEG and EKG found that 4% had neurological symptoms related to a cardiac etiology, and 14% had interictal abnormalities of a cardiac rhythm (see Figure 3.1). The EKG lead can provide additional

36  •  Ambulatory EEG Monitoring

FIGURE 3.1  The EKG lead recording shows bradycardia and cardiac pauses during sleep in this patient with symptomatic nocturnal clinical events of cardiogenic origin.

clues regarding etiology; the heart rate tends to rise higher and more abruptly ­during an epileptic seizure than during a nonepileptic event (6). SYSTEM COMPONENTS Ambulatory EEG devices continue to evolve and improve, becoming more portable, with an expanded number of channels and with additional recording options for polygraphic recording. Figure 3.2 depicts the hardware components of a typical aEEG system. Amplifier/Connection Unit Most systems today are designed for a dual role and can be configured for aEEG or home polysomnography. They include 24 to 32 referential channels, as well as a limited number of differential, direct current (DC), and oximetry channels. Electrode wires are connected to inputs in the amplifier, which is worn strapped to the body (Figure 3.3). Because frequent movements are anticipated with aEEG, the amplifier is typically worn close to the head to reduce artifacts by minimizing the distance that the EEG signal must travel from electrode to amplifier. Common mode rejection (CMR) for aEEG systems is typically greater than 120 dB, meaning that a common-mode input signal is reduced by a factor of 1 ­million. Thus, artifacts from large movements will be greatly attenuated by

3.  INSTRUMENTATION AND POLYGRAPHIC EEG  •  37

FIGURE 3.2  The components of an ambulatory EEG system. 1 = 32-channel amplifier, 2 = ambulatory recorder, 3 = photic device (for use in the laboratory), 4 = mobile camera. Not shown: battery pack, which connects to the recorder. Source: Courtesy of Cadwell.

FIGURE 3.3  The ambulatory EEG system as worn by a child. EEG leads are connected to the amplifier, which is worn below the right shoulder. The recorder and battery are in the pouch at the waist. The video camera is placed nearby. Source: Courtesy of Cadwell.

38  •  Ambulatory EEG Monitoring

CMR. Despite these measures, however, aEEG recordings are still frequently beset by artifacts that arise because the noise signal varies at least somewhat at different sites. Impedance problems due to suboptimal electrode application are not improved by CMR. Recorder The digital EEG recorder should have a minimum sampling frequency of 256  Hz, with a resolution of 12 bits for 32 electrodes, and should have the capacity to store a minimum of 24 hours of EEG signal. Currently available aEEG systems include a lightweight recorder and batteries, together weighing approximately 500 g (about 1 lb). Data are stored on compact flash memory cards. The power source is one or more conventional or rechargeable ­batteries. Duration of battery capacity depends on several factors, including media size, number of channels recorded, sampling rate, and quality of the batteries used. A prolonged or data-intensive study over days of recording may require changing or recharging the batteries to complete the monitoring session. The recording device is usually belted to the waist. Some recorders have a small display indicating the time of day, elapsed recording time, battery status, recording status, and amount of data storage available. Ambulatory EEG systems include an event marker that the patient or observer can activate for identifying a clinical event, resulting in a time label on the aEEG recording. Patients or caregivers keep an activity log, documenting the times of seizures or spells, as well as daily activities. This documentation allows the reviewer to correlate reported symptoms with aEEG findings and to identify activity-related artifacts. As video becomes increasingly integrated into most proprietary aEEG systems, patient record keeping will play a diminishing role in clinical correlation. Contemporary systems tend to record continuous EEG, and offer options for postacquisition sampling, event highlighting, and compressed array review. Ambulatory EEG systems that intermittently record events, rather than continuous EEG, remain commercially available. These recorders are activated by the patient or an observer and store EEG for several minutes prior to, during, and following an event. In addition, they use seizure-detection algorithms to detect clinically subtle or asymptomatic seizures and record EEG samples at regular intervals. Video Home video that is synchronized with the aEEG allows accurate correlation of observed behaviors. The addition of video to aEEG offers advantages over patient-reported activities and provides a lower cost alternative to inpatient video-EEG monitoring. The aEEG video camera must be moved with the

3.  INSTRUMENTATION AND POLYGRAPHIC EEG  •  39

subject and positioned so that he or she is visible in the frame, which is more easily accomplished with sedentary indoor activities than outdoor pursuits. Cameras also have an infrared mode for capturing video at night. Review When the patient returns to the laboratory for device removal, the technologist should review the final moments of the EEG to ensure that signal recording was maintained throughout the desired time period. Ambulatory EEG companies offer various software options for EEG processing and review. Once the EEG is downloaded or transmitted to a review station or server, the electroencephalographer reviews the study, with special attention to event marker activations and times of reported clinical symptoms. Postacquisition processing may include automatic artifact identification and/or removal and trend analysis. Seizure-detection algorithms label rhythmic activity and spikes. Although helpful, such programs may miss subtle, atypical, or very focal seizures. Using a rapid scrolling rate, an experienced electroencephalographer can review an uncomplicated 24-hour study in under 20 minutes. The activity log or video is a crucial part of aEEG interpretation. Clinical events that occur without electrographic ictal correlate are likely to be nonepileptic but may also occur with brief extratemporal seizures. Electrographic seizures that occur without clinical correlate are often either subtle or ­subclinical and otherwise go undetected. CONCLUSION State-of-the-art aEEG recording today bears little resemblance to the ­pioneering devices of the 20th century. Current systems allow aEEG to be obtained in the patient’s home with the same recording technology as used in the EEG laboratory or video-EEG monitoring unit in the hospital. What cannot be replicated at present, however, is the expertise of the technologist to continuously monitor the EEG during recording and to troubleshoot artifacts and technical problems in real time. The next decade will likely bring further changes in aEEG recording instruments and technique, such as longer lasting electrode gel, wireless transmission of EEG signal from the scalp, hardware miniaturization, and remote real-time monitoring.

REFERENCES 1. Ives JR, Woods J. 4-Channel 24 hour cassette recorded for long-term EEG monitoring of ambulatory patients. Electroencephalogr Clin Neurolphysiol. 1975;39:88–92.

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2. Ives JR. A completely ambulatory 16-channel cassette recording system. In: Stefan H, Burr W, eds. Mobile Long-Term EEG Monitoring: Proceedings of the MLE Symposium. New York, NY: Fischer; 1982:205–217. 3. Morris GL, Galezowska J, Leroy R, North R. The results of computerassisted ambulatory 16-channel EEG. Electroencephalogr Clin Neurophysiol. 1994;91:229–231. 4. Morris GL. The clinical utility of computer-assisted ambulatory 16 channel EEG. J Med Eng Technol. 1997;21:47–52. 5. Feravich SM, Keller CM. Overview of using T1/T2 and 10-10 subtemporal ­electrode chains for localizing EEG abnormalities. Neurodiagn J. 2013;53(1):27–45. 6. Nousiainen U, Mervaala E, Ylinen A, Uusitupa M, Riekkinen P. The importance of the electrocardiogram in ambulatory electroencephalographic recordings. Arch Neurol. 1989;46:1171–1174.

CHAPTER 4

ARTIFACT AND AMBULATORY EEG WILLIAM O. TATUM, IV, DO

INTRODUCTION Ambulatory EEG (aEEG) is an important diagnostic tool for patients with ­epilepsy and is well suited for assessing patients with seizures and seizurelike episodes in the outpatient environment. There are many conditions that can be evaluated with different forms of EEG recording including scalp-based EEG, intracranial EEG, video-EEG, and continuous EEG monitoring in addition to aEEG for adult and pediatric patients. Similar to other forms of EEG, aEEG serves as an adjunct to clinical diagnosis. Like routine scalp EEG, common problems may arise during recording, including artifact (1). The most specific use of aEEG is for neurophysiological identification, classification, and localization of epileptiform discharges (EDs) in patients with seizures (2). When compared with routine EEG, aEEG demonstrated a higher yield and diagnostic sensitivity (3). Like inpatient video-EEG monitoring (VEM), aEEG can provide support for the clinical diagnosis of epilepsy, assist in classifying the underlying epilepsy syndrome, quantify EDs and seizures, and be used, albeit rarely, to characterize seizures for epilepsy surgery (4–6). Greater convenience, lower cost, improved access and a greater sampling of natural sleep and circadian rhythms are advantages of aEEG that can provide a high yield (Table 4.1). One major advantage of aEEG is the ability to prolong EEG recording in the natural environment beyond a 20- to 30-minute routine scalp EEG. Most systems are not cumbersome but instead are light and easily worn. Some patients find aEEG socially embarrassing when performing activities of daily life; however, others do not find it limiting (Figure 4.1). Artifact is present in virtually every type of EEG recording and becomes increasingly more likely as the duration of the recording increases (7). Outside the controlled environment of a hospital neurophysiology laboratory, multiple 41

42  •  Ambulatory EEG Monitoring

TABLE 4.1  Ambulatory EEG: Advantages and Disadvantages Diagnostic Advantages

Disadvantages

Diagnosis when seizures are suspected by the clinical history, but routine EEG is nondiagnostic. Distinguish between nonepileptic events and seizures, including those with unique environmental triggers or exposure. Classify seizure type for selecting an appropriate ASD in people with epilepsy. Quantify the number and duration of epileptiform abnormalities, subclinical seizures, and seizures without awareness.

Inappropriate for diagnosis of seizure emergencies, including serial seizures and status epilepticus. May be inconclusive for a definitive diagnosis of psychogenic nonepileptic attacks. Unable to be used for antiseizure drug manipulation in patients where the risk of seizure precipitation would require immediate medical care by trained hospital personnel. Incomplete behavioral analysis and ancillary testing for a standard presurgical evaluation to optimize seizure monitoring.

Technical Advantages

Disadvantages

Convenience and freedom to move about in the patient’s normal home environment for natural recording. Systems may provide continuous aEEG recording with automated software addition for spike and seizure detection. Reliable technical support exists to troubleshoot or repair problems often able to be identified through computer remote monitoring. Able to be recorded to gain exposure to unique environmental situations that may trigger an event (eg, sunlight in Jeavon’s syndrome).

Movement and routine activities of daily living introduce excessive artifact limiting interpretation. Unreliable, noncompliant patients may be uncooperative and lose or damage equipment. Personnel are absent to identify and intervene to ensure optimal integrity of the recording in real time. Some facilities may provide limited technical support to ensure proper management of equipment. Absence of video in some recording systems to help discern artifact and apparent waveform abnormality.

FIGURE 4.1  A 72-year-old female with “personalized” computerassisted ambulatory EEG monitoring being performed while shopping at Bealls department store.

4.  ARTIFACT AND AMBULATORY EEG  •  43

physiological and nonphysiological sources of artifact exist and may contaminate the record (Figure 4.2). Recording very small signals (microvolts) in aEEG systems typically have fewer electrodes and reduced spatial resolution. In addition, there is less control over external sources. This can challenge the interpreter by the greater degree of interference from lower s­ ignal-to-noise ratios present in the ambulatory setting leading to a greater number of artifacts. When artifact appears in the record, it may be subtle or obvious and may challenge the reader to differentiate physiological waveforms from nonphysiological ones. The importance of artifact in the aEEG is reflected by the greater frequency of occurrence associated with prolonged unsupervised recording time. Incorrect recognition of artifact as an abnormality can lead to overinterpretation and adversely affect treatment. Artifact impacts patient care when it is misinterpreted as waveforms that mimic EDs. The development of automated spike and seizure detection algorithms was instrumental in seizure identification when clinical signs are absent, subtle, or occur without the patient’s awareness. Prior to automated seizure detection programs, subclinical seizures were only identifiable with complete manual review of all the data accumulated. Algorithms designed to detect interictal EDs and seizures have allowed for time-efficient seizure detection at any point during the recording session and in clinical research (8). However, most systems are designed

60 Hz

Movement

Electrode

Muscle

ECG

FIGURE 4.2  Multiple artifacts including 60 Hz, electrode, myogenic, ECG, and movement artifact. Note the appearance of spike-like electrode artifact that could be misinterpreted as epileptiform discharges.

44  •  Ambulatory EEG Monitoring

FIGURE 4.3  Spike detections by software that are due to artifact. Note concomitant ECG artifact.

to overidentify suspicious waveforms leading to false positives (Figure 4.3). Artifact may be detected as an ED at the expense of false n ­ egative detections that miss true EDs. The accuracy of computer-based detection programs that falsely detect artifact as EDs (Figure 4.3) are inherent in most proprietary ­computer-assisted aEEG (CAA-EEG) systems. The basis for detection algorithms use human identification of EDs as the gold standard for comparison to ensure adequate presence, frequency, and duration (Figure 4.4) have been properly represented. The use of detection algorithms occur with less than 100% accuracy to identify spikes and seizures. In one study, correct identification of abnormalities was found in less than 30% of aEEGs without additional review (9). Intermittent EEG abnormalities including nonepileptiform (Figure 4.5) changes and EDs (Figure 4.6) may appear in the recording so infrequently they limit the usefulness of routine scalp EEG to detect abnormalities. The effect of natural sleep, circadian rhythm variation, and the natural lifestyle of the patient is able to be duplicated during prolonged aEEG. The influence of circadian rhythms associated with EDs extends beyond N2 sleep, and hence, the yield of prolonged aEEG recording is therefore not limited by time (10). Circadian rhythms in genetic generalized epilepsy were seen to influence the yield of aEEG; Fittipaldi et al. (11) found that EDs were identified within the

4.  ARTIFACT AND AMBULATORY EEG  •  45

FIGURE 4.4  Incomplete representation of a burst of generalized spike and waves (GSWs) during aEEG. Note the line that interrupts the burst of GSW from myogenic artifact created by intermittent sampling.

FIGURE 4.5  Spike detection algorithm detecting a generalized rhythmic burst of delta during the first 2 seconds. Note the “spike” detected in the second detection in a channel determined to contain single-electrode artifact (arrow).

46  •  Ambulatory EEG Monitoring

FIGURE 4.6  GSW and polyspike-and-wave EDs in the fourth and fifth spike detections. Also note the C3 single-electrode artifact detected by the algorithm in the first through third detection.

first few waking hours that otherwise would be missed without overnight EEG recording. In another study, Koepp et al (12) found EDs appearing exclusively during sleep in 83% of patients evaluated with aEEG that would not have been as readily detected by a routine scalp EEG recording. Prolonged video-EEG is now able to be performed in and out of the hospital environment to allow full mobilization of patients during recording sessions (13). Ambulatory EEG as an outpatient has emerged as a cost-effective alternative to inpatient VEM in the diagnosis of epilepsy and paroxysmal events (10). In the past several decades, aEEG has evolved from a rudimentary beginning as a “Holter monitor for the brain” to a highly sophisticated, engineered, computer-assisted technology incorporating video with high-fidelity synchronized with EEG for home monitoring. In selected cases, aEEG has been used to evaluate patients for epilepsy surgery (14). ARTIFACT AND aEEG Potentials that occur in the aEEG during recording that do not possess the appropriate polarity and electrophysiological field that is generated by the

4.  ARTIFACT AND AMBULATORY EEG  •  47

brain reflect the features of an extracerebral signal. It is incumbent on the aEEG interpreter to identify the mismatch between potentials generated by the brain from electrical activity that does not conform to a realistic head model; this underlies the skill to recognize artifact. There are multiple types of artifact that can arise from a variety of extracerebral sources present in the outpatient environment that may coexist with EDs, making temporal distinction difficult (Figure 4.7). It may become a clinical problem due to diagnostic error if the EEG is not clear or if artifact is misinterpreted as an abnormality (15). The essential technique to identify artifact is recognizing a mismatch between activities that does not conform to a realistic head model. Pathological spikes generated by the brain may become difficult to separate from spikes due to artifact (Figures 4.6 and 4.8). Artifacts that occur with increasing frequency mimic evolution and simulate a seizure (Figure  4.9) during the process of recording long-term EEG (16). In the ambulatory environment, the potential for artifacts to become introduced into the EEG is ubiquitous. Multiple sources can contaminate the recording (Figure 4.2) and obscure interpretation of an abnormality (false negative) or beguile the interpreter into misidentifying waveforms that simulate EDs (false positive). Pathological EDs may be difficult to separate from physiological (e.g., myogenic) sources or nonphysiological sources (e.g., electrode “pop”) of artifact. Spikes and sharp waves are defined by duration rather than the pathological nature of the source. They also do not portend the capability to generate seizures. Combining the use of video and EEG has significantly enhanced our ability to differentiate cerebral and extracerebral sources by correlating behavior that is time locked to the electrophysiological potentials seen on the

FIGURE 4.7  Single-electrode artifact that mimicked occipital spike-and-waves on aEEG that led to treatment for focal epilepsy.

48  •  Ambulatory EEG Monitoring

FIGURE 4.8  Spike detection by the computer amidst myogenic artifact in the same region raising the issue of an abnormality due to the presence of a field of an artifact challenging to the interpreter.

EEG (17). When video recording is not used, this poses a significant challenge for identifying artifact, and differentiating them from EDs may become difficult. The importance of separating nonepileptic behavioral episodes with aEEG that contains video is crucial to assist in identifying artifact manifested by pseudoepileptiform patterns derived from nonepileptic sources. Common artifacts (Figure 4.10) may be misinterpreted during any form of continuous EEG that would routinely be identified with the addition of extracerebral monitors or noted by a skilled technologist. During aEEG recordings, technologists, nursing, and medical support staff who would otherwise ensure appropriate acquisition of the EEG before final interpretation are absent (Figure  4.11). Recognizing artifact, identifying the source, and eliminating nonessential nonphysiological activity are key roles of the technologist that are notably absent (Figure 4.12) during aEEG recording (18). Guidelines for prolonged EEG monitoring have been developed (19); however, despite pitfalls that are known to exist in hospitalized patients during long-term EEG, using similar standards seems reasonable for aEEG. Artifact may occupy the majority of the tracing to contaminate the aEEG by such a degree that it obstructs visual analysis and limits the ability to perform a meaningful interpretation.

4.  ARTIFACT AND AMBULATORY EEG  •  49

FIGURE 4.9  Rhythmic artifact detected by the computer software as a focal seizure. Paroxysmal “seizure” termination ended during the end of a movement artifact (arrow). No video was associated to identify the source. Note the nonphysiological spatial distribution and similar involvement of the ECG (oval) to indicate an extracerebral source.

FIGURE 4.10  Repetitive vertical eye blink artifact that may be misconstrued as frontal intermittent rhythmic delta activity without eye movement monitors as in this case.

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FIGURE 4.11  Sweat sway artifact during aEEG.

FIGURE 4.12  Chewing artifact present on CAA-EEG. Note the bitemporal electrographic maximum and superimposed myogenic polyphasia during chewing motions.

In  this case, the EEG may be considered technically limited. Identifying artifact first requires identification among other suspicious waveforms (20). These include atypical variations of normal background activity, benign variants of uncertain significance, and artifacts that mimic epileptiform activity.

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Similarly, while there are several types of physiological spikes or sharp waves, ­particularly during drowsiness and light sleep, the presence of an electropositive spike or sharp wave in an adult should raise the suspicion for an artifact in the absence of a skull defect. TECHNICAL CONSIDERATIONS The era of computer-based aEEG has allowed for quality recording of EEG signals on digital media outside the hospital setting (21). Recording, analyzing, and storing large quantities of information are now easily facilitated for several days of aEEG recording with a variety of montages, filters, and display speeds available. Computer-assisted EEG has advanced high-quality, high-fidelity acquisition of brain signals similar to inpatient VEM systems, overcoming many limitations previously imposed by recording EEG on paper media (22). The combination of video with aEEG has improved the technical ability to separate artifacts in the outpatient setting, such as eating (Figure 4.13), from cerebral potentials by coupling alterations in behavior with similar corresponding frequencies identified on aEEG (17). Still, technical problems remain the limiting factor in aEEG recording. Electrode artifact due to deterioration of the electrode paste is common with prolonged aEEG recordings stemming from faulty contact of the electrodes. Other types of instrumental artifacts may alter the recorded signal and be mistaken for abnormalities (Figure 4.14). By observing out of phase deflections, the lack of a credible physiological field can validate the presence of artifact and help the interpreter separate the waveform(s) in question from abnormality (Figure 4.15). The majority of artifact in the EEG

FIGURE 4.13  CAA-EEG demonstrating vertical eye blink artifact and chewing artifact while eating.

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FIGURE 4.14  Electrostatic artifact during cable manipulation by the technologist during trouble­ shooting to correct the ECG channel. Note the similarity to atypical spike and waves.

FIGURE 4.15  Artifact in aEEG that is manifest as clear double phase reversals of opposite positive and negative polarity in adjacent electrodes signifying a nonphysiological field for cerebral activity.

appears as extraneous high-frequency noise. Even single-electrode artifact may saturate the amplifiers to limit the usefulness of an aEEG unless the channel is “hidden” from view. Post hoc filtering and montage manipulation are now possible with most aEEG systems to clarify polarity and electrical fields that may suggest an extracerebral source. In addition, the technical specifications of most commercially available aEEG systems typically have adequate

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TABLE 4.2  Common Sources of EEG Artifact Nonphysiological Sources

Physiological Sources

Electrodes

Normal

 Pop

  Eye movements

  Impedance mismatch

 Cardiac

Lead wires

 Myogenic

  Simple sway

  Bone defects

  Head movement

  Mastication and deglutition

Machine and connections

Abnormal

 Aliasing

 Tremor

 Jackbox

 Myoclonus

60 Hz

  Complex movement

Static electricity Implanted electrical devices

analog–digital converters to limit aliasing that will misrepresent the signal. The physical and functional components of EEG are represented by a few critical parameters of recording (Table 4.2). The initial limitations in the number of aEEG channels available to record and in the limited storage capability first present for long-term EEG (23), have been overcome in modern day recording systems (24). In the last two decades, digital technology has evolved to the point that CAA-EEG can record up to 32 channels, including video and software applications utilizing spike and seizure detection. Aliasing based on undersampling a signal in time due to low sample rates or in space due to a small number of electrodes falsely recording the true signal is now unlikely with the present-day specifications of most aEEG amplifiers. A minimum of 16 channels including a single channel dedicated to ECG should be used. Pushbutton activation aids in identification of events observed by witnesses or caregivers similar to inpatient VEM. Minimum guidelines exist for performing EEG through the American Clinical Neurophysiology Society (25). Recording is tailored to meet the needs of the individual patient, though is usually performed from hours to several days and, therefore, requires daily technical review to ensure optimal integrity of the recording system.

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SOURCES OF ARTIFACT The multiple sources capable of generating artifact are a major limitation of aEEG. Many internal and environmental sources for artifact exist and commonly intervene intermittently throughout a single aEEG monitoring session. A notable limitation of using aEEG is that artifacts are not only common but a frequent feature that remain uncorrected unless frequent online assessment or direct technical intervention is possible. Artifact may arise from any location between the patient–electrode interface and the recording device. Artifacts generated by electrodes are ­perhaps the most common source, though myogenic artifact and movement artifacts challenge obtaining quality recordings for interpretation. During aEEG, a controlled setting is absent, patients are frequently moving, and immediate intervention to eliminate artifact is not possible. Patients may be able to maintain a quiet sedentary lifestyle during the aEEG recording session; although, electrode contact with the scalp may become compromised (Figure 4.16), and troubleshooting by a technologist to minimize the amount of artifact in the aEEG is not immediately available. Without the ability to intervene when artifact is recognizable, the likelihood of it being continuously recorded in the aEEG and subsequently obscuring the tracing at any point during the recording is high (Figure 4.17). Even when a source is unable to be identified, the lack of a credible electrophysiological field, location, or an atypical polarity will lead to the recognition of a waveform or series of waveforms as artifact.

Nonphysiological Sources Nonphysiological sources of artifact commonly include ones that can be generated by the instruments of recording: electrodes, wires, jackbox, and machine itself. With the exception of the initial hook-up and reassessment, regelling, and downloading EEG usually occurs on a daily basis (less frequently if the patient travels from a distance). Electrodes are usually the primary source of artifact in aEEG monitoring. Deterioration of the scalp– electrode interface by patient movement during aEEG monitoring also continuously challenges the integrity of recording outside the controlled setting of the EEG laboratory. After the electrodes are applied, the conductive gel used to secure electrodes to the scalp deteriorates over time with exposure to the air. Collodion should be utilized, periodically refilling the disc electrode with gel at least every day of recording. Deterioration of the scalp-electrode interface predisposes them to high impedance, which makes electrodes more prone to artifact and skin infections and leads to compromise in the quality of the aEEG (21). Impedance testing is performed initially during the hookup and during reassessment thereafter to ensure impedances are kept below

4.  ARTIFACT AND AMBULATORY EEG  •  55

FIGURE 4.16  Single-electrode artifact at FP1 due to lost contact with the scalp when it became dislodged during aEEG recording. Fronto-polar electrodes are prone to artifact due to their position.

FIGURE 4.17  Obvious artifact not confused with an electrographic seizure at the beginning of an aEEG recording session that was not identified until daily assessment was made almost a full day later.

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FIGURE 4.18  Impedance testing with the corresponding artifact that is readily identifiable.

5,000 Ω to minimize introducing artifact into the aEEG (Figure 4.18). Routine assessment of aEEG is made daily as an outpatient procedure, but access is often restricted by transportation limitations to prompt intervention if artifact is suspected during the recording. Supervision is possible with some aEEG systems that utilize web-based technical support, and caregivers can be instructed to assist in regelling the electrodes with online remote access (26). Novel electrodes used for chronic ambulatory recording are developing. A dry interface, subdermal systems, and wireless wearables are evolving to enhance long-term aEEG recording and help to minimize the problem of electrode-generated artifact (27). Electrical and magnetic fields inherent in the metals of the electrode attached to the head and body may add to artifact contaminating the s­ ignal generated by the brain though is usually recognized based on its distinctive appearance. Electrical artifact in aEEG recordings is also commonly caused by 60-Hz alternating current. Many environmental electrical and electromagnetic sources from nearby power supplies, devices, and outlets are capable of introducing 60-Hz artifact into the aEEG, though the characteristic frequency makes it easily recognizable (Figure 4.19). As electrodes become incompletely secured to the scalp, this increases the likelihood that artifact will arise in proportion to longer durations of recording. Nonphysiological sources of artifact frequently include electrodes, but the wires, jackbox, and machine may also serve as common sources. Instrumental artifact may occur in locations such as the temporal region where abnormalities are common or evolve in frequency or amplitude to deceive the interpreter unless other characteristics suggesting artifact is present (Figure 4.20). Interference due

4.  ARTIFACT AND AMBULATORY EEG  •  57

to biologically active electromagnetic fields may intermittently appear in the aEEG from implanted devices including cardiac pacemakers, pain pumps, and neurostimulators. Isolated grounds attenuate 60-Hz frequencies like a notched filter to minimize noise in the environment. However, intermittent artifact may provide a false interpretation that a waveform or series of waveforms is paroxysmal to simulate abnormality (Figure 4.21). Like certain areas in the hospital, some areas in the home, and in the surrounding environment

FIGURE 4.19  60-Hz artifact from high-impedance electrodes maximal at P4.

FIGURE 4.20  An instrumental artifact that “evolves.”

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FIGURE 4.21  Artifact that is intermittently present when patient places their hand on a laptop computer. Arrows indicate on and off.

FIGURE 4.22  Generalized periodic “discharges” due to filtered myogenic artifact with the application of a 20-Hz high-frequency, low-pass filter. The aEEG subsequently became obscured by continuous machine artifact during outpatient dialysis.

4.  ARTIFACT AND AMBULATORY EEG  •  59

are electrically complex and ­predispose to artifacts. Both intrinsic and extrinsic sources producing artifact may intervene to obscure the EEG (Figure 4.22). Multiple technical problems may arise during the course of prolonged aEEG recording and combinations of nonphysiological and physiological artifact may intervene separately or occur together at different times during the recording during activities that are associated with normal daily living. This may serve to limit the ability to provide a definitive interpretation of the monitoring session (28). Physiological Sources Most biological tissues have inherent current dipoles that produce ­electrophysiological gradients. Eye movement, muscular contractions, oral and pharyngeal motion, cardiac sources, and patient movement are among the primary physiological sources of artifact in the aEEG. These are often persistent when they occur due to the unsupervised nature of aEEG and, therefore, may repeat throughout monitoring. While aEEG can be performed as an inpatient procedure, it is typically performed in the outpatient setting. Therefore, eye movement artifact is present in virtually every individual who is awake, conscious, and ambulatory. Eye movement artifact is generated by a dipole produced by the relative electropositive cornea compared with the electronegative retina. Vertical eye movement artifact is represented by the frontal electrode derivations and has the characteristic frontopolar electropositive sharp-wave appearance on aEEG similar to routine scalp EEG. Vertical eye blink may occasionally mimic a pathological frontal-frontopolar sharp wave without careful inspection or without the presence of an eye movement monitor that is often absent during aEEG. Horizontal eye movement artifact measured at the anterior temporal (e.g., F7/F8) electrode derivations typically appears as eye movements of opposite polarity. Quick lateral eye movements can produce artifact in the form of spikes generated by the lateral rectus muscles that may be confused as abnormal EDs by aEEG interpreters. Eye movement monitors that are able to verify the ocular origin of questionable features in the aEEG are rarely employed due to difficulty maintaining infraorbital electrodes over prolonged periods of time. Activities of daily living often involve repetitive movements (e.g., chewing, swallowing, tooth brushing). Myogenic artifact may compromise the integrity of the recording and mar the recording to limit the interpretation of the aEEG. Like eye movements, myogenic artifact may be intermittent and results in muscle spikes that may mimic abnormality in the aEEG. The heart is another important source of artifact. The ECG is generated by myocardium with electrical conduction of the left ventricle generating a QRS complex with a vector that may become introduced into the aEEG mimicking left EDs. Changes in the ECG may be observed that

60  •  Ambulatory EEG Monitoring

coincide with the EEG in a benign (Figure 4.23) manner or may be more malignant such as a cardiac arrhythmia that compromises cortical blood flow. Movement artifact is a major limitation of aEEG. Movement may be especially problematic when it produces artifact that defies interpretation by obscuring the underlying cortical activity. What constitutes a technically limited EEG is incompletely defined. For this author, when more than 50% of

FIGURE 4.23  Change in ECG artifact commensurate with a state change identified on simultaneous EEG. Note the inverted ECG waveform and pacemaker “spikes” at the transition.

FIGURE 4.24  Complex physiological artifact due to the patient scratching his dog causing owner’s head to produce tremor artifact in the aEEG. Note the adjacent electrodes with positive and negative phase reversals depicting a nonphysiological field.

4.  ARTIFACT AND AMBULATORY EEG  •  61

the channels and more than 50% of the tracing are obscured by artifact, then the aEEG is considered technically limited. When artifact is episodic, especially without ­simultaneous video recording, it may be difficult to identify. For example, tremulousness composed of repetitive movement produces a rhythmic alternating electrical depolarization that may contain a field that suggests a cerebral generator (Figure 4.24). This may appear as a pseudoictal pattern and present a significant challenge for the interpreter to differentiate between artifact and epileptiform activity (10). ARTIFACT SIGNIFICANCE Although some aEEG artifacts provide useful information about vigilance and behavioral correlates, they often lead to misinterpretation of the EEG as abnormality due to EDs (29). However, artifacts that are misinterpreted as EDs are known to occur (Figure 4.25). In one series of patients undergoing long-term VEM for newly diagnosed psychogenic nonepileptic attacks (PNEA), up to 32% had epileptiform abnormalities misinterpreted reported on a prior EEG that upon review were due to overinterpreted waveforms (29). There are some guidelines in EEG interpretation that exist to identify spikes and sharp waves. These have only duration (e.g., 20–70 msec/spike and 70–200 msec/sharp) that have been established to distinguish them (Figure 4.26). The overall features to characterize an abnormal spike or sharp wave, however, are less specific (30). Identifying artifact first requires identification and differentiation of normal variations, benign variants (Figure 4.27), and artifact from suspicious waveforms (20). Even nonepileptiform patterns may lead to a misinterpreted EEG (Figure 4.28). Epileptiform patterns (Figure 4.29) may be simulated by artifacts and be falsely identified. The importance of separating nonepileptic behavioral episodes using aEEG that create movement artifact manifesting as pseudoepileptiform patterns is crucial. Evolution in frequency may be encountered (Figure 4.30), but the activity must occupy both temporal and spatial dispersion to suggest an ictal event. Babies are artifact-prone during EEG due to frequent movement without quiet or sedated sleep, and this makes aEEG more challenging in this age group without technical support. Artifact can mimic both focal and generalized EDs. Physiological sources of artifact are usually the main reason that artifacts are mistaken as EDs (17,30). Unless remontaging is available to demonstrate the electropositive characteristic of artifact, or eye movement monitors are used, misinterpretation of eye movements as frontal sharp waves can occur. Myogenic artifact associated with chewing may create a burst of high-­ amplitude bilateral myogenic polyphasia that may imitate abnormal generalized polyspike and waves (Figure 4.12). Generalized high-amplitude bursts of myogenic artifact due to myoclonus may be misinterpreted to support a

62  •  Ambulatory EEG Monitoring

FIGURE 4.25  A run of “occipital spikes” representing misinterpreted artifact.

FIGURE 4.26  Abnormal left anterior temporal spike-and-wave with a regional temporal field (arrow) amidst other detections of artifact by spike-detection software.

4.  ARTIFACT AND AMBULATORY EEG  •  63

FIGURE 4.27  Left temporal rhythmic midtemporal theta burst of drowsiness during aEEG.

FIGURE 4.28  GSWs in the first 2 seconds of spike detection with occipital intermittent rhythmic delta activity due to artifact in the 3rd second.

64  •  Ambulatory EEG Monitoring

FIGURE 4.29  Spikes and sharp waves at P3 due to artifact produced by movement artifact. Note the disruption of the ECG.

diagnosis of juvenile myoclonic epilepsy and carry long-term r­amifications for chronic antiseizure drug (ASD) treatment (personal observation WOT 8/21/2010) (31). Cardiac sources may also mimic EDs during review of an aEEG when asymmetric left temporal diphasic waveforms created by the electrical ­vector of the QRS complex are conducted from the left ventricle. The gold standard for a definitive diagnosis of epilepsy is documentation of an epileptiform pattern on ictal EEG. However, repetitive body movements during PNEA on aEEG may mimic electrographic seizures. Clues to a nonepileptic origin of a behavioral event during aEEG includes a prolonged duration, extracerebral polarity, and temporal and spatial distribution atypical for a cerebral generator (Figure 4.31). When artifact is frequent, bilateral, and rhythmic, it can act as a pitfall and be mistaken for a seizure. Video improves the accuracy of EEG for a seizure diagnosis (10,31). The manifestations of PNEA are greatly variable and often resemble those of epileptic seizures. Similarly, unilateral or asymmetric movement or tremor may produce artifact that is ipsilateral to the side involved with the movement. Varying frequencies produce evolution of varying amplitude and frequencies that parallel the movements of the

4.  ARTIFACT AND AMBULATORY EEG  •  65

FIGURE 4.30  Evolution in frequency but none in field of distribution for P4 single-electrode artifact.

FIGURE 4.31  Rhythmic activity in the left parasagittal leads due to the electrodes being dislodged from the scalp.

66  •  Ambulatory EEG Monitoring

body. These movements of PNEA may generate rhythmic artifacts on aEEG that mimic electrographic seizures (32). These seizure mimics challenge even the most experienced electroencephalographer to correctly identify artifact without a thorough clinical history or video accompaniment (33). Furthermore, even the presence of a small amount of artifact may limit the ability to identify epileptic seizures when they have subtle changes in the EEG (Figure 4.32). Convulsive PNEA displays a characteristic pattern on time–frequency mapping on the EEG. Artifact that is a stable nonevolving frequency is distinguished from the evolving pattern seen during an epileptic seizure, and the presence of frequency ­variability in a crescendo–decrescendo manner may differentiate the progression based on artifact (34). Newer artifact-based algorithms have been developed based on surface electromyography artifact to differentiate between generalized tonic–clonic seizures and PNEA with 95% accuracy (35). The significance of monitoring artifact in non-EEG systems may lie in development of devices that detect seizures during sleep to help reduce the risk of injury and sudden unexpected death in epilepsy (36). Video review alone is able to render a confident diagnosis of epilepsy or PNEA in about one third of cases (37,38). However, movements may be identified on video (e.g., myoclonus) that serve as a pitfall to interpretation unless EEG is recorded simultaneously (Figure 4.33). Therefore, because aEEG is especially prone to artifact, video, EEG, and a trained electroencephalographer are crucial to prevent overinterpretation of artifact as a seizure, which would lead to diagnosis and treatment of the patient for epilepsy. Overcoming Artifact Obtaining an aEEG that is not contaminated by artifact is challenging, if not impossible. Most current aEEG systems are very similar to those used for inpatient VEM with digital filters and flexible parameters that permit post hoc analysis (39). The home environment significantly differs from the EEG laboratory or epilepsy monitoring unit because of the presence of numerous electrical generals that may produce unusual signals that are difficult to recognize without experience. Many unique sources of extracerebral signals exist outside of the hospital that can interfere with the cerebral activity during aEEG recording. Denoising an aEEG is a challenge prior to EEG processing and analysis. Post hoc filtering and montage manipulation are available and may help, but unless the type of artifact is readily apparent, some false electrocerebral waveforms may occupy a field that appears real to the interpreter. It is tempting to aggressively use digital filters to visualize what is beneath artifact; however, overly aggressive use of high-frequency, low-pass filters may alter myogenic artifact, making it appear deceptively spiky (40).

4.  ARTIFACT AND AMBULATORY EEG  •  67

FIGURE 4.32  CAA-EEG with a right temporal seizure intermixed with frequent artifact.

FIGURE 4.33  Ambulatory EEG of a patient referred for evaluation of juvenile myoclonic epilepsy (JME) with single-generalized tonic–clonic seizures and jerks at night. Arousal was noted without epileptiform discharges to refute the jerks as an epileptic phenomenon, and therefore long-term treatment with antiseizure drugs was not required. Jerks were later identified as periodic limb movement in sleep.

Newer techniques that use blind source separation have led to development of software algorithms that use independent component analysis, canonical correlation analysis, wavelet transform, and empirical mode decomposition and may have an application in aEEG to limit some common artifacts (41–43). Others have performed channel-specific

68  •  Ambulatory EEG Monitoring

independent component-based artifact rejection to minimize the effects of dynamic motor actions using a template regression procedure to address artifact incurred by mobility (44). A baseline EEG performed prior to aEEG may serve as a baseline to identify subtle abnormalities and EDs but also to address commonly identified artifacts that may confound aEEG interpretation when no supervision is available (39). The digital capabilities of microprocessing EEG enables acquisition, analysis, management, transfer, and storage of information that permit prolonged monitoring with aEEG (45). The post hoc interpretation of EEG is often compromised if visual analysis relies solely on pattern recognition (16). Nevertheless, some waveforms on aEEG should raise suspicion of an artifactual source as in the common s­ituation where single-electrode artifact becomes apparent (Figure 4.34). Newer algorithms are able to eliminate this type of artifact in the aEEG (Figure 4.35) that would otherwise contaminate recordings throughout the review (Figure 4.36). Specificity for both fast-frequency and slow-frequency artifact (Figure 4.37) reduction is available to minimize artifact if they begin to interfere with interpretation. These new techniques will improve the ability to enhance the underlying cortical signals when patterns deviate from normal physiological fields and polarity (Table  4.3). Details contained within the diary of activities and video recording simultaneously will be helpful to correlate suspicious waveforms for the reader to minimize the risk for traps and pitfalls that may lead to overinterpretation of the aEEG.

FIGURE 4.34  Single-electrode artifact saturating the amplifier with high-frequency noise contaminating adjacent channels of the EEG. Note the artifact reduction software is turned “Off” (arrow).

4.  ARTIFACT AND AMBULATORY EEG  •  69

FIGURE 4.35  Same aEEG as Figure 4.34 with the artifact reduction software program turned “On” (arrow) demonstrating elimination of single-electrode artifact.

FIGURE 4.36  Artifact reduction software reduces “interference” but is not applied to slow wave artifact removal in this example.

70  •  Ambulatory EEG Monitoring

FIGURE 4.37  Same example as Figure 4.36 with slow waveforms eliminated. Note the box that is checked for “Slow Wave” and Interference in the right lower quadrant of this example. TABLE 4.3  Features That Suggest Artifact in aEEG The sharpest or highest voltage is surface positive Multiple double- and triple-adjacent phase reversals Activity confined to a single channel A highly stereotyped monomorphic pattern Lack of a physiological or credible electrical field Patterns that have precise periodicity Video demonstrated movement with identical frequency on aEEG Intermittent artifact in the same channels preceding an abnormality Similar frequencies that appear in the aEEG and ECG

CONCLUSION Although aEEG recordings have been commonly performed for more than 40 years, the recent advances of microprocessor technology have permitted greater computer storage and implementation of software signal

4.  ARTIFACT AND AMBULATORY EEG  •  71

analysis. Approximately 30% of patients admitted to epilepsy m ­ onitoring units do not have epilepsy, and for patients with the limited access to VEM, aEEG with video may be a way to obtain event monitoring in a similar, more expeditious manner. Visual inspection continues to be the conventional means of identifying artifact in aEEG. The prevalence of artifact on aEEG leading to a misdiagnosis is unknown, though the impact of misinterpreting artifact as an abnormality serves as a pitfall to treatment. Newer techniques are emerging to eliminate common types of artifact during aEEG recordings. As wearable aEEG systems emerge, there will be significant technical challenges to overcome artifacts of recording. However, the ultimate means of separating essential from nonessential artifact will reside with the interpreter.

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13. Cascino GD. Video-EEG monitoring in adults. Epilepsia. 2002;43(suppl 3):80–93. 14. Schomer DL, Ives JR, Schachter SC. The role of ambulatory EEG in the evaluation of patients for epilepsy surgery. J Clin Neurophysiol. 1999;16:116–129. 15. Tatum WO. How not to read an EEG: introductory statements. Neurology. 2013(suppl 1);80(1):S1–S3. 16. Tatum WO, Dworetzky B, Freeman WD, et al. Artifact: recording EEG in special care units. J Clin Neurophysiol. 2011;28(3):264–277. 17. Tatum WO, Dworetzky B, Schomer D. Artifact and recording concepts in EEG. J Clin Neurophysiol. 2011;28(3):252–263. 18. Tatum WO, Reinsberger C, Dworetzky B. Artifact. Chapter 7. In Schomer DL, Lopes da Silva FH, eds. Niedermeyer’s Electroencephalography: Basic Principles, Clinical Application, and Related Fields. 7th ed. Philadelphia, PA: Oxford; 2016. 19. Brenner R, Drislane FA, Ebersole JS. Guideline twelve: guidelines for long-term monitoring for epilepsy. J Clin Neurophysiol. 2008;48(4):265–286. 20. Tatum WO. Normal “Suspicious” EEG. Neurology. 2013;80(suppl 1):S4–S11. 21. Ferree TC, Luu P, Russell GS, et al. Scalp electrode impedance, infection risk, and EEG data quality. Clin Neurophysiol. 2001;112:536–544. 22. Foley CM, Legido A, Miles DK, et al. Long-term computer-assisted outpatient electroencephalogram monitoring in children and adolescents. J Child Neurol. 2000;15:4–55. 23. Ebersole JS. Ambulatory cassette EEG. J Clin Neurophysiol. 1985;2:397–418. 24. Gilliam F, Kuzniecky R, Faught E. Ambulatory EEG monitoring. J Clin Neurophysiol. 1999;16:111–115. 25. Sinha SR, Sullivan L, Sabau D, et al. American Clinical Neurophysiology Society  Guideline 1: minimum technical requirements for performing clinical electroencephalography. J Clin Neurophysiol. 2016;33:303–307. 26. Casson A, Yates D, Smith S, et al. Wearable electroencephalography. What is it, why is it needed, and what does it entail? IEEE Eng Med Biol Mag. 2010;29:44–56. 27. Liu NH, Chiang CY, Hsu HM. Improving driver alertness through music selection using a mobile EEG to detect brainwaves. Sensors. 2013;13(7):8199–8221. 28. Shih JJ, Tatum WO. Computer-assisted ambulatory electroencephalography. Chapter 10. In: Rubin DI, Daube JR, eds. Clinical Neurophysiology. 4th ed. New York, NY: Oxford University Press; 2016;167–175. 29. Benbadis SR, Tatum WO. Overinterpretation of EEGs and misdiagnosis of epilepsy. J Clin Neurophysiol. 2003;20(1):42–44. 30. Maulsby RL. Some guidelines for assessment of spikes and sharp waves in EEG tracings. Am J EEG Technol. 1971;11(1):3–16. 31. Tatum WO. Artifact-related epilepsy. Neurology. 2013;80(suppl 1):S12–S25. 32. Benbadis SR. The EEG of nonepileptic seizures. J Clin Neurophysiol. 2006;23(4):340–352. 33. Benbadis SR, LaFrance WC Jr, Papandonatos GD, et al. Interrater reliability of EEG-video monitoring. Neurology. 2009;73:843–846. 34. Vinton A, Carino J, Vogrin S, et al. “Convulsive” nonepileptic seizures have a characteristic pattern of rhythmic artifact distinguishing them from convulsive epileptic seizures. Epilepsia. 2004;45(11):1344–1350. 35. Beniczky S, Conradsen I, Moldovan M, et al. Automated differentiation between epileptic nonepileptic convulsive seizures. Ann Neurol. 2015;77:348–351.

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36. Patterson AL, Mudigoudar B, Fulton S, et al. SmartWatch by SmartMonitor: assessment of seizure detection efficacy for various seizure types in children, a large prospective single-center study. Pediatr Neurol. 2015;53:309–311. 37. Erba G, Giussani G, Juersivich A, et al. The semiology of psychogenic nonepileptic seizures revisited: can video alone predict the diagnosis? Preliminary data from a prospective feasibility study. Epilepsia. 2016;57(5):777–785. 38. Benbadis SR, Allen Hauser W. An estimate of the prevalence of psychogenic non-epileptic seizures. Seizure. 2000;9:280–281. 39. Drislane FW. The clinical use of ambulatory EEG. Chapter 3. In: Chang BS, Schachter SC, Schomer DL, eds. Atlas of Ambulatory EEG. Boston, MA: Elsevier Academic Press; 2005:17–25. 40. Gaspard N, Hirsch LJ. Pitfalls in ictal EEG interpretation: critical care and intracranial recordings. Neurology. 2013;80(suppl 1):S26–S42. 41. Safieddine D, Kachenoura A, Albera L, et al. Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches. J Adv Signal Process. 2012;127:1–15. 42. De Clercq W, Vergult A, Vanrumste B, et al. A new muscle artifact removal technique to improve the interpretation of the ictal scalp electroencephalogram. Conf Proc IEEE Eng Med Biol Soc. 2005;1:994–997. 43. Woestenburg JC, Verbaten MN, Slangen JL. The removal of the eye-movement artifact from the EEG by regression analysis in the frequency domain. Biol Psychol. 1983;16(1):127–147. 44. Gwin JT, Gramann K, Makeig S, et al. Removal of movement artifact from high-density EEG recorded during walking and running. J Neurophysiol. 2010;103(6):3526–3534. 45. American Clinical Neurophysiology Society. Guideline 8: guidelines for recording EEG on digital media. J Clin Neurophysiol. 2006;23:122–124.

CHAPTER 5

CLINICAL USE OF AMBULATORY EEG IN ADULTS DONALD L. SCHOMER, MD

INTRODUCTION The use of ambulatory EEG (aEEG) should be thought of as a part of a ­comprehensive clinical neurophysiology laboratory’s available technologies. This technology is complementary to the inpatient epilepsy monitoring unit (EMU) and should be integrated with it. These techniques have evolved as an essential part of the diagnostic and treatment programs for people suffering from seizures and/or epilepsy. Clinicians wanting to use either of these technologies should ask themselves a series of questions before ordering their patient to undergo either procedure. A logical approach, summarized in Table 5.1, is discussed in the following. The field of aEEG monitoring has evolved tremendously over the last 10 to 15 years secondary to the significant technological advances in EEG monitoring in general (see Chapters 1–3) (1,2), and the demand for its use has grown even more rapidly secondary to the ever significantly increasing costs related to inpatient evaluations. There is also a reasonable and somewhat rational approach to the dilemma of, “Should I use aEEG or go for an inpatient (EMU) workup?” (Table 5.2). Since enhanced technology has brought inpatient and outpatient aEEG capabilities to be on fairly equal footing, the choice relates more to the individual patient’s related idiosyncratic concerns and the requesting physician’s goals. I discuss the pros and cons of inpatient versus outpatient EEG telemetry in more detail in the following since the choice of technologies is more realistically based on the clinician’s intended goal, modified by specific patient needs and requirements, than it is on costs for services or on technological sophistication. If used thoughtfully, aEEG can be used successfully for both diagnostic and treatment or management purposes. As a diagnostic tool, aEEG can certainly be used in the differential diagnosis of behavioral events. There is also a significant role for its 75

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TABLE 5.1  The Clinician’s Approach to EEG Monitoring: Think First! If it is epilepsy, where in the brain can it originate?   Will the electrode array that I chose be able to “see” those regions?   Will the machines that I chose be up to the job I need?   Will I need to add any special electrodes?   Will I settle for indirect evidence or do I need to see actual symptomatic events? How often do the events occur?   Do the events cycle and should I time the recording to the subject’s cycle?   Are there other things that I can do to increase the likelihood that an event will occur?    e.g., sleep deprivation, medication withdrawal What else could it be?    Are there other parameters that I should monitor?   Is this condition or this patient dangerous during or following an event?   Be aware of the anticonvulsant properties on hospital admission

TABLE 5.2  Inpatient (EMU) Versus Outpatient (aEEG) EEG Monitoring Inpatient Preferred   Patient needs to have medication withdrawn   Patient needs to have invasive electrodes   The behavior during events puts patient or equipment at risk and needs nursing staff supervision   Inpatient system is more sophisticated and these advantages are needed   Ancillary testing needs to be performed (i.e., single-photon emission computed tomography) Outpatient Preferred   Young child   Patient is in a chronic care facility   Patient has frequent and nondisabling or dangerous events   Has family or significant others who will be helpful   Has a history of “hospitalization anticonvulsant” effect

use in determining seizure classification or identifying a specific epilepsy syndrome in a patient with known epilepsy. It may be used successfully as a part of a comprehensive evaluation of patients for possible epilepsy surgery, when inpatient (EMU) studies may have failed for reasons noted in the following. In the long-term treatment/management of patients with epilepsy, aEEG is useful in reassessing patients with changes in behavior or cognition to see whether seizures are contributing to their more newly acquired

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TABLE 5.3  Role for EEG Telemetry Diagnosis   Record EEG of the patient during an “event”   Document an ictal pattern on EEG   Help with epilepsy syndrome identification  Lead to other investigations or treatment approaches, i.e., cardiac, sleep disorder, behavioral disorder Treatment/Management   Drug Treatment: Short or Intermediate Term    New behaviors, changes in behavior: are they epileptic?    Changes in cognitive function: are there unrecognized seizures or change in spike frequency    Altered sleep or cardiac/pulmonary function   Long-Term Management    Changes in EEG background activity    Increase in subtle or unrecognized seizures    Following a patient undergoing antiepileptic drug (AED) withdrawal   Presurgical—Origin of Onset    Video–audio telemetry using scalp electrodes (Phase I investigation)    Invasive electrodes (Phase II investigation)—grids, strips, depths    EEG correlation with SPECT, PET, fMRI    Drug withdrawal under observation   Sleep deprivation fMRI, functional magnetic resonance imaging; PET, positron emission tomography; SPECT, single-photon emission computed tomography.

problem. Also, aEEG is a reasonable method of identifying whether or not drug withdrawal causes an increase in either subtle sustained EEG events or in the frequency of interictal epileptic activity. Such knowledge may be critical in assessing whether or not to continue a drug taper or to evaluate if the patient is at high risk for recurrences of clinically symptomatic seizures. In like fashion, aEEG has become a more frequently considered method for identifying ictal events during either clinical or experimental drug trials. Each of these areas will be dealt with separately (Table 5.3). DIFFERENTIAL DIAGNOSIS When approaching a patient with a possible diagnosis of epilepsy and based on a history of events and likelihood that those events might have an epileptic origin, clinicians must ask themselves several questions before

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considering either aEEG or inpatient EMU-based video-EEG monitoring (VEM). First, if the patient’s symptoms are epileptic in origin, where is it likely to have origin? If the answer is that it is likely a focal onset seizure, the follow-up question should be where in the brain might that be? If the answer is from a site where routine EEG electrode arrays will see it electrically if it does have an epileptic origin, then aEEG might be helpful. If it is from deeper regions of the brain or areas that are difficult to see using the routine 10–20 electrode arrays, for example, hippocampi, insula, or mesial frontal regions, are there other electrodes that could be incorporated into the design of the recording montage that would address those specific needs? For example, it is reasonable to design a montage that records from parasagittal and midline regions where a mesial frontal or mesial parietal focus is under suspicion clinically or use a subtemporal electrode or chain of subtemporal electrodes where a mesial or inferior temporal focus is considered. An equally important question, relevant for either the inpatient EMU or outpatient aEEG evaluation is, “How often and under what circumstances do the symptoms occur?” If symptoms occur in relationship to others events, for example, the menstrual cycle, or following sleep deprivation, one needs to consider the timing of such external events to maximize the possibility of recording a patient’s symptoms. The clinical behavior associated with the patient’s events may also need special consideration too, if one is going to successfully record them. For example, if the patient becomes violent or aggressive during or following an event, perhaps he or she should be studied under a more controlled condition such as in an EMU capable of handling such behaviors. The proper decision provides for both patient safety and good recording conditions. On the other hand, some events, such as those associated with life stressors, are more likely to be recorded in the outpatient environment due to the “anticonvulsive” effects of hospitalization (3). A somewhat related concern regards exactly what level of evidence is acceptable to the clinician in order to satisfy the clinical diagnosis and hence the therapeutic approach to the patient. Will the clinician require that the patient have an “actual” event or will indirect evidence suffice, for e­ xample, the presence of focal or generalized interictal epileptiform discharges, periods of rhythmic focal slowing, or bursts of generalized frontal-central predominant rhythmic delta activity? If events occur relatively rarely or unpredictably, then more indirect evidence may not only be sufficient but may be the best one could get. An equally important question to ask is, “What else could this be?” If the differential diagnosis includes cardiac or respiratory disorders, then is the recording system that one is using capable of doing good ECG monitoring or linking nighttime audio/video recording with SaO2 monitors to assess sleep and snoring for potential sleep-related disorders and apnea? As we adapt more devices for monitoring other physiological variables to the basic EEG

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recorder systems (see Chapter 10), we are increasing our abilities to study and diagnose other possible disease states that may mimic seizures and epilepsy. Generalized Seizures/Epilepsy In the EEG workup of someone suspected of “absence seizures,” one would anticipate that the aEEG would be an ideal instrument for many reasons. The EEG discharges seen in this disorder are frequent and very distinctive. The disorder usually presents in childhood or early adolescence. For many reasons, this age group prefers an outpatient workup where appropriate. In fact, the vast majority of such patients with absence epilepsy can be diagnosed by the astute clinician during a routine clinical exam or by routine scalp EEG where a classical interictal 3-Hz spike-and-wave discharge can often be provoked (see Figure 5.1A). Ambulatory EEG still offers additional information that neither of the earlier two procedures can provide. With aEEG, one is able to quantify the frequency of events over the course of the day- or nighttime and, if coupled with take home audio/video recording, assess how often these brief discharges appear to be symptomatic. As shown in Figure 5.1B, the child was clinically diagnosed with absence epilepsy, and the treating A

1 Fp1 – F3 2 F3 – C3 3 C3 – P3 4 P3 – O1 5 Fp2 – F4 6 F4 – C4 7 C4 – P4 8 P4 – O2 9 Fp1 – F7 10 F7 – T3 11 T3 – T5 12 T5 – O1 13 Fp2 – F8 14 F8 – T4 15 T4 – T6 16 T6 – O2 17 AuxA 18 AuxB O uV 1 Sec.

09:36:29

09:36:34

09:36:39

(continued ) FIGURE 5.1  (A) This is a classic 3-Hz generalized spike-and-wave discharge and is the interictal marker for absence epilepsy.

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B

FIGURE 5.1  (continued )  (B) This was a “captured” seizure from the seizure detection algorithm on the patient where the mother was very attentive but yet still missed a symptomatic event.

physician had started her on appropriate medication. However, the family felt the young child was still ­having events. The mother was in attendance during long portions of this aEEG study and, in fact, was proven correct when she activated the recorder with a time locked audio/video recording multiple times for virtually every subtle event. She proved that she was an excellent observer who could recognize even minor events. However, during the recording session, the event shown in Figure 5.1B occurred when the mother looked away to sip some coffee, and the seizure detection algorithm captured this brief event and the clinical behavior, which strongly supported that the child was symptomatic during it. Thus, continuous aEEG monitoring detected and quantified the events that occurred independently of the parent’s close observations. This child had further medication adjustments based on these findings. In a related disorder, juvenile myoclonic epilepsy (JME), which usually presents in adolescence, routine EEG often does not capture the classical interictal markers, that is, generalized 4- to 6-Hz polyspike and wave

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(Figure 5.2A). For similar reasons to the absence seizure disorder mentioned previously, aEEG is more likely to capture at least the interictal discharges, which tend to occur more often at night while sleeping. Since clinically symptomatic seizure and myoclonus tend to occur during the first several hours following waking, aEEG coupled with home audio/video recording can often see evidence for subtle myoclonic jerks that have a distinctive EEG correlate but are often subconsciously incorporated into movements by the patient as noted in Figure 5.2B. Evidence for generalized tonic or tonic–clonic or arousal associated tonic–clonic seizures are even less often recorded on a routine EEG. Again, aEEG is a reasonable method to record such events which, as noted clinically, are more likely to occur at home than in the hospital environment and more often as the patient is arousing from sleep (Figure 5.3). In some generalized epilepsies where both developmental delays and different or multiple types of clinical seizures occur, most patients are reasonably well served with routine EEG, which can assess background abnormalities, look for focal or multifocal structural abnormalities, and identify interictal and ictal abnormalities. Ambulatory EEG still offers the c­ linician information regarding the frequency of probable ictal (symptomatic) events or a more positive correlation of EEG changes associated with certain unusual behaviors. A

(continued ) FIGURE 5.2  (A) This shows the classic interictal epileptiform discharges seen in “juvenile myoclonic epilepsy (JME).” This is occurring during drowsiness and shows 4- to 5-Hz generalized polyspike and wave discharges.

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B

PUSH BUTTON

FIGURE 5.2  (continued )  (B) This patient has JME and had just awakened and became symptomatic with myoclonic jerks noted by the push-button event marker.

FIGURE 5.3  This patient has nocturnal generalized tonic–clonic seizures. They occur frequently during sleep or upon awakening. The event recorded here is occurring at 19:00 hours just as the patient falls asleep. Note the “herald” spike followed by the gradual buildup of generalized seizure activity.

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Focal Onset Seizure The use of aEEG in diagnosing focal onset seizures is limited by the same ­constraints as is seen on inpatient-based EMUs. The EEG study, whether as an aEEG or an EMU-based procedure, needs to provide sufficient EEG coverage related to the suspected seizure onset site, if the goal is to capture a symptomatic event. Basal electrodes may be needed if a mesial or inferior frontal temporal lobe onset is suspected clinically. Likewise, midline coverage using electrodes such as Fz, Cz, Pz, or Oz may be needed for supplementary motor or mesial frontal, parietal, or occipital seizure onsets. Focal seizures without impairment of consciousness (a.k.a. simple partial seizures) may have little to no EEG correlate but may have more indirect evidence for their presence such as an increase in focal slowing associated with the “event.” There are many areas of the brain where seizures may have origin and we do not have good EEG techniques to see those regions using standard electrode placements. We can still rely on finding other EEG abnormalities if those events tend to spread to the more superficial cortical regions. Focal onset seizures from these “hidden areas” may have EEG correlates if they secondarily generalize or if they go on to a more classical complex partial seizure with loss of consciousness. Figure 5.4 shows a very classic focal onset symptomatic seizure of occipital origin. Figure 5.5 shows a left basal occipital-temporal onset seizure associated

FIGURE 5.4  This is an example of an aEEG captured left occipital onset seizure related to the patient having a visual hallucination in the right visual hemifield.

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with profoundly disturbing visual hallucinations, proven by dynamic tests (i.e., ictal single-photon emission computed tomography [SPECT]), but the only evidence on EEG was rhythmic ipsilateral attenuation of EEG in the left occipital region and ipsilateral left frontal slowing secondary to spread via the dorsal occipital to frontal pathway. Classification of Seizures and Identification of Seizure Syndromes Foley et al (4) reported a group of 49 children with a diagnosis of medically refractory epilepsy, based on clinical histories and multiple routine EEGs, who underwent aEEG for 1 to 3 days had recordable seizures in 73% of them. About half of those seizures were found through an automated seizure detection algorithm (5). A generalized seizure or epilepsy syndrome was the likely diagnosis in 75% of the children initially, but in only 18% of them was this diagnosis confirmed. Sixteen patients of 49 were incorrectly

FIGURE 5.5  This is an inpatient EMU study that followed a negative recording from aEEG. This patient had complex visual hallucinations and arrests of behavior during which time the aEEG did not show an appreciable change. The patient had a deep left occipital-temporal lesion that enhanced considerably with SPECT scanning when symptomatic and showed decreased activity regionally when he was asymptomatic. The EMU study shown here shows a significant attenuation in the left posterior quadrant at the onset along with rhythmic slowing over the left frontal region that occur associated with the arrest of behavior suggestive of a spread from the occipital pole to the frontal pole. In retrospect, the aEEG showed similar changes but the reader suspected that the changes were an artifact.

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diagnosed, and when more appropriate medications for their specific seizure type were instituted, most had a significant clinical improvement in control. Six of the patients were found to be good candidates for further presurgical planning. As a further example of how aEEG can be used in making a proper diagnosis of the seizure and of an epilepsy syndrome, Figure 5.6 shows a child with the clinical diagnosis of “absence epilepsy.” She had a relatively normal routine EEG and went home with an aEEG with audio/ video recording. Mother witnessed numerous events while the child was outside. Mother had been aware that her daughter seemed more likely to have an absence while outside on bright sunny days. In this case, the mother documented a relationship of eye closure and eyelid twitching with absence signs and made the case for Jeavon’s syndrome. In a second group of patients in the same Foley study (3) where epilepsy was considered a part of the differential diagnosis, prior to instituting specific treatment, the aEEG was successful in 86% in terms of making a

FIGURE 5.6  This child was initially diagnosed with absence epilepsy but with aEEG was found to have myoclonus associated with eye closure while outside on a bright sunny day. This was carefully documented with simultaneous video recording. The push buttons marked times when she closed her eyes and had the provoked generalized discharges suggestive of “Jeavon’s syndrome.”

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definitive diagnosis and did so with considerable cost savings over ­inpatient VEM in an EMU. Ambulatory EEG appears to be not only better at identifying interictal epileptiform abnormalities than multiple routine EEGs in the diagnosis of seizures but is more cost-effective (6,7). Other Differential Diagnostic Possibilities Occult cardiac arrhythmias may masquerade as a seizure disorder. Based on years of clinical experience dating back to the very early years of EEG, neurophysiologists have insisted on monitoring the ECG as well as EEG to look for associations between arrhythmias and seizures (8). Every EMU has had the experience of seeing symptomatic arrhythmias while recording EEG for seizures. The relationship between these two events, that is, seizure versus arrhythmia, are complicated and beyond the scope of this review. Suffice it to say, aEEG should always monitor cardiac rhythms as well as the EEG. While ictal tachycardia is the most commonly seen change in cardiac rhythm associated with an ictal event, they are seldom symptomatic on their own. On the other hand, severe bradycardias or periods of cardiac asystole, while infrequent as a coincident ictal event, are far more often symptomatic in their own right (Figure 5.7). In this case, the patient had a long history of temporal lobe onset seizures but now had events that

FIGURE 5.7  This is from a patient with a change in seizure semiology. There are two “push-button” markers where his wife noted a probable seizure onset. The time lines are for 5 seconds each so the total duration of the image is about 2 minutes. He has a period where he developed a tachycardia at the onset of his seizure but then has an ictal asystole for about 40 seconds during which time he has generalized muscle activity followed by generalized EEG flattening.

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occurred primarily at night. He was described as having an increase in nighttime movements with what were felt to be seizure-like activity. He was then more difficult to wake up. The monitoring showed a focal seizure, which then was associated with a prolonged asystole that, in turn, was associated with a period of cerebral anoxia, during which time he developed the wild movements as described by his wife. This study led to both placement of a cardiac ­pacemaker as well as a change in his antiseizure drugs (ASDs). Severe respiratory distress may also occur as an ictal or postictal phenomenon. With the advent of sophisticated ambulatory SaO2 monitors that can be time locked to the EEG acquisition, this physiological parameter can also be used and be useful in the differential diagnosis of patients having clinically suspicious ictal or postictal respiratory difficulties (Figures 5.8 and 5.9). In the first example, this young man had suffered several “minor” concussions playing high school football. His parents felt

FIGURE 5.8  This young man had a negative evaluation for a postconcussive sleep disorder and underwent aEEG to see if seizures might be playing a role. On the first night, he had several generalized seizures. During the one shown here, he developed significant hypoxia. These events were suspected by the parents, but the patient preferred to sleep with his door locked and they were never able to see or witness this behavior. Note this is a compressed playback.

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that he was h ­ aving some difficulty in sleeping because they heard unusual noises coming from his room and difficulty then waking up in the mornings. He had a one night unremarkable sleep study, and this was followed by a take home aEEG with audio/video recording. He had several major ­secondary generalized seizures with what appeared on video to be severe postictal hypoxia, which was later confirmed by inpatient (EMU) study with SaO2 monitoring. His frequent nocturnal seizures and severe hypoxia warranted urgent administration of AEDs. The second example (Figure 5.9) occurred in a patient with focal seizures without impaired consciousness studied in our EMU who had a marked suppression of respiratory function, which was a totally unexpected finding. Jasper and Penfield describe

FIGURE 5.9  This tracing represents part of a continuous EEG recording done with additional equipment to monitor blood-oxygenation level. This file is compressed for better display of the SaO2 data. The page shown here represents approximately 15 minutes of recording. We know that this patient experienced a sensation of a partial seizure at approximately 10:22:36. He never lost consciousness with this event but, as demonstrated by the SaO2 monitor, there was a significant drop in his blood oxygen level that lasted for several minutes. The dotted line (see arrow) represents 90% O2 saturation level. Before the seizure, the patient’s oxygen levels were normal, but after this simple partial seizure the levels dropped to the approximate 70% range. The patient was seemingly unaware of this drop. It took several minutes for the patient to reset his respiratory drive and return to normal levels.

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respiratory arrest with cortical ­stimulation in both ­temporal and frontal regions, which perhaps explains why this specific patient seemed unaffected by such a significant and prolonged hypoxia. Penfield and Jasper found that when stimulating these areas there was an arrest of respiration, a drop in peripheral blood oxygenation, and the patient was often unaware. These observations are described in their now famous textbook on “Functional Anatomy of the Brain.” However, how often either focal seizures or generalized seizures cause dramatic and prolonged drops in SaO2 remains an unknown question. With the advent of ambulatory monitors for SaO2 that can be linked to aEEG devices, this is potentially an answerable question. In children, there is often some confusion between seizures and epilepsy and a sleep disorder. More commonly in children for whom this is an issue, the differential diagnosis centers around parasomnias. Since there is also a well-known first-night effect in sleep lab studies and since these are occurring in children, aEEG has been used successfully in such differential diagnostic cases. The following is a tracing (Figure 5.10A and B) of a child A

(continued ) FIGURE 5.10  (A) and (B) These two recordings represent a continuous playback of a child with suspected nocturnal seizures who, instead, was experiencing night terrors. He is symptomatic during slow-wave sleep, shown here, with excellent audio/video demonstration of classical behaviors.

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B

FIGURE 5.10  (continued )

who appeared to his mother to wake up in the middle of the night having been incontinent of urine and usually in a very distressed state. He had been studied in a pediatric sleep lab for several nights without experiencing any typical events. On the first night at home on aEEG with video, he had an event with the EEG showing he was in stage N3 sleep throughout the entire event, and on video he appeared to be experiencing extreme and uncontrollable fear. Sleep labs also experience what they call the “first-night” effect, which is what presumably occurred here and was avoided by doing the study in the patient’s home environment. TREATMENT/MANAGEMENT Often it becomes clinically important to document the patient’s seizure frequency. Most often clinicians rely on the “seizure diary,” a log of symptomatic events as experienced by the patient or as witnessed by family, to accomplish this task. However, in a study designed to test whether or not patients recognized when they had a seizure and whether those who felt that they always knew when they had experienced a seizure were correct, the authors were surprised (9). Only four of 28 patients were always aware of their seizures. Focal seizures, with impairment or loss of consciousness,

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were the seizure types that were most often unrecognized by patients. Those patients who reported the lowest frequency of seizures had the ­highest ­number of unrecognized and clinically relevant events. In a somewhat related follow-up study by Tatum et al (10), they recorded 502 patients with suspected focal seizures. During the first 24 hours of recording, there were 47 out of the 502 patients who had seizures. Of the 47 seizures recorded, 18 were unrecognized by the patient. Eleven of those 18 seizures were recognized only via the automated detection algorithm (5). Ambulatory EEG can and often does incorporate software algorithms running online and parallel to EEG acquisition or used post hoc to improve detection for interictal epileptiform discharges and for sustained ictal events that occur frequently (11–13). These advances in aEEG technology also mimic inpatient EMU software programs with newer ­algorithms that are in constant evolution. Drug Treatments and Long-Term Management As a result of the earlier observations, we need to reconsider how we decide whether or not a patient has good seizure control. This may become particularly important when considering withdrawing AEDs in patients who claim to be long-term seizure free. In such cases, aEEG is now often used for 24 hours with the previously noted software techniques for seizure and interictal spike detection looking for unrecognized seizures and/or the presence of interictal activity prior to making a final decision regarding whether or not to withdraw drug treatment. If there are unrecognized seizures, most clinicians would not take medicine away. If there are interictal discharges, the physician may still withdraw a drug but follow up, postdrug withdrawal, with another aEEG study to evaluate whether or not the epileptiform activity increased or whether actual ictal events were present. An analogous clinical issue relates to drug trials and whether or not the effectiveness of any given AED under investigation actually performs as it is suggested by published reports. If the trials are dependent on patient reporting only, we should be suspicious of the results for the reasons noted earlier. Clinical drug trials where the investigators decide that they needed to have more direct evidence for clinical seizure activity, that is, the presence of interictal discharges and frequency of ictal events both before and during the experimental drug exposure would need to have actual prolonged EEG recordings. In such studies, aEEG is certainly one method that can be used efficiently and as shown in the study by Coppela (14) effectively. Since aEEG is less expensive then EMU studies, aEEG becomes a reasonable technique. The drug trial noted earlier was performed using aEEG in patients with absence epilepsy. From previous work with this form of generalized seizures, it was clear that aEEG could deliver excellent data that was more reliable than the information that they could obtain from

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even well-kept patient diaries (Figure 5.1B). In this study, clinicians used continuous EEG data recorded through an aEEG device and documented not only the p ­ resence of seizure activity but also determined the duration of each event and were able to calculate a “seizure load.” This use of aEEG has been incorporated into subsequent numerous drug trials and is now under consideration as a standard for future trials by the U.S. Food and Drug Administration. Another clinical dilemma exists in cases where nonepileptic events are suspected. It is often extremely helpful to capture these events clinically and on EEG, and aEEG offers several advantages. If the “symptomatic” behavior does not sound injurious, aEEG can be undertaken safely. In such a case, the clinician avoids the reinforcement of this behavior that often accompanies hospitalizations where the patient learns to behave “like a sick person.” Importantly, one also gets to see the family dynamics that go along with the triggers for these behaviors, and one can make some judgments regarding what psychological approaches may be helpful for the subject as well as for family members (Figure 5.11).

FIGURE 5.11  The tracing here shows one of many “events” captured at home in this individual suspected of having nonepileptic events. The EEG and video recording during the events confirmed the nonepileptic behavior but also gave the treating physician valuable information about the interpersonal dynamics in the family that lead to a successful psychological intervention.

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On the long-term basis, aEEG may be a useful technique in p ­ redicating seizure recurrences in patients who have been seizure free for long ­periods of time and want to withdraw from their AEDs, as noted earlier. It is also a reasonable test for those individuals who do not qualify for AEDs withdrawal but are developing long-term changes in behavior, mood, or cognition. It is currently felt that some patients with poorly controlled seizures develop cognitive difficulties that often mimic a progressive dementia. In such cases, aEEG can be useful in looking for focal or multifocal slowing as one would see in vascular dementia or in Alzheimer disease. Background slowing and changes in sleep cycling would support that this may be AED complications and lead to changing the medications used for control. Also, the presence of unexpected brief seizures might signify a need to alter medication. Specific EEG patterns, such as triphasic waves, would suggest the presence of liver, renal, and other toxicmetabolic dysfunction, which could also be related to some medications, particularly ­valproic acid. Presurgical/Surgical Evaluations Epilepsy centers across the United States and Europe have often regarded aEEG as a standard option available either initially or as part of a Phase I evaluation (15–17). Some insurers now mandate that aEEG be done prior to an EMU admission to prove that the patient’s events are of an epileptic nature. Some centers may use aEEG in this way but for different reasons. Many centers, especially in Europe where EMU bed availabilities are more limited and controlled than in the United States, tend to do initial screening for seizure surgery through the use of aEEG (personal communication with Franz Brunnhuber, MD, from King’s College, London). This is even more common in financially strapped parts of the world. Doing the evaluation this way also avoids the antiseizure effect of the hospitalization (2) and may rule out some individuals who are having nonepileptic events from further EMU investigations. Major centers (15,17) will often send patients home with the device and with time-locked audio/video recording when they have exceeded the allowed time for their EMU stay, based on prior approvals by insurance companies or when the patient just needs a break from the hospital routines. Care must be taken to provide urgent or even emergent care if the subject is leaving, having gone through medication withdrawal (Figure 5.12). This was a patient who had been withdrawn from AEDs and was hospitalized for about 10 days in the EMU without a seizure. At home, during the first 24 hours recording, she had two well-documented seizures with a focal temporal onset. Most epilepsy centers have not used aEEG in patients going home with invasive electrodes. However, in a recent study (18), King-Stephens et al have

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FIGURE 5.12  This woman with known seizures was discharged from an EMU after an extended stay without any clinical events. The tracing shown here shows one of several seizures arising from the left temporal region that occurred on the first night at home. This demonstrates the ability of aEEG to extend the workup of a patient for surgery who also has the “antiseizure” effect of a hospitalization.

successfully sent patients with invasive electrodes home with aEEG equipment and audio/video recording. Additionally, the responsive neural stimulator (RNS) should be considered the ultimate chronic intracranial aEEG device that not only records epileptic events, it recognizes them and provides responsive stimulation to block seizure progression (19,20). The decision to use an RNS device usually follows extensive inpatient-based invasive EEG in search of a single and possibly surgically operable site. Instead, either two sites that are distant from each other are found or the focus is in a nonoperable site. Those patients may then be considered for an RNS implant. Such an implant is capable of identifying the onset of seizure activity, from two sites, and provides a stimulus using an electrical current that acts in a similar fashion to the

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vagus nerve stimulator except that the RNS provides stimulation directly to the seizure onset zone. Patients are then sent home with this device, which monitors and “treats” seizures from that time forward for many years. As clinicians become more familiar with such technological advances in aEEG ­monitoring, relationships between seizures, interictal activity, and environmental or physical events will come under renewed clinical interest. Such examples include developing methodologies to predict a seizure occurrence (21) or understanding the relationship between interictal discharge frequency and the menstrual cycle (22). The Role of aEEG in the Study of the “Ecology of Epilepsy” All of these both realized and speculative concepts are a part of what could be called an “Understanding of the Ecology of Epilepsy.” This concept was recently described by Dr. Franz Brunnhuber from Kings College, London, during an exchange regarding the future of aEEG. At his facility in London, aEEG has been fully integrated into the scheme of their evaluation of patients who are either suspected of having epilepsy or who may be under consideration for a surgical approach, having failed several reasonable drug trials. He, however, wanted to expand on the concepts described many years earlier by the postmodern French historian and philosopher Paul-Michel Foucault. Foucault was profoundly interested in how modern medicine has evolved from the time of the French revolution to current times and more specifically what has been gained and what has been lost in the process of building the modern medical center. He described these in great detail in two major publications (23,24). Dr. Brunnhuber expanded the concepts relative to epilepsy as written about in the British medical literature in the mid1920s (25,26). Certainly, it seems almost intuitive that having aEEG devices equipped with audio/video capabilities in patient’s homes, will allow an astute clinician to go back to a previous age where home visits yielded so much more information about how a disease existed within the greater community and how the community dealt with the affected individual. There is so much more to learn about how the environment interacts with the patient with epilepsy that will and can have a profound effect on their outcomes. The reverse issues are equally important and now able to be observed and studied, that is, how does the patient with epilepsy affect those around him or those trying to give supports to him? This was all diagramed by Dr. Brunnhuber (see Figure 5.13). We are on the brink of a major change in our approaches to people with epilepsy from aspects of care. These include diagnostic methods, treatment modalities, and a better understanding of how this disorder is a part of the patient’s greater ecology.

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Habitual stress & relaxation

Domestic risks

Habitual interactions

Relational impact

Ecology of epilepsyseizures

Occupational impact

Circadian rhythms

Triggers

Domestic routines

FIGURE 5.13  Brunnhuber’s picture.

REFERENCES 1. Morris GL, Jalezowska J, Leroy R, et al. The results of computer-assisted ambulatory 16-channel EEG. Electroencephalogr Clin Neurophysiol. 1994;91:229–231. 2. Schomer DL. Ambulatory EEG telemetry: how good is it? J Clin Neurophysiol. 2006;23(4):294–305. 3. Riley T, Porter RJ, White BG, et al. The hospital experience and seizure control. Neurology. 1981;31:912–915. 4. Foley CM, Legido A, Miles DK, et al. Long-term computer-assisted outpatient electroencephalogram monitoring in children and adolescents. J Child Neurol. 2000;15:49–55. 5. Gotman J. Automatic recognition of epileptic seizures in the EEG. Electroencephalogr Clin Neurophysiol. 1982;54:530–540. 6. Liporace J, Tatum WO, Morris GL, et al. Clinical utility of sleep-deprived versus computer-assisted ambulatory 16-channel EEG in epilepsy patients: a multicenter study. Epilepsy Res. 1998;32:357–362. 7. Bridgers SL, Ebersole J. Ambulatory cassette EEG in clinical practice: experience with 500 patients. Neurology. 1985;35(12):1167–1168. 8. Velis D, Plouin P, Gotman J, et al. Recommendations regarding the requirements and applications for long-term recordings in epilepsy. Epilepsia. 2007;48(2):379–384. 9. Blum DE, Eskola J, Bortz JJ, et al. Patient awareness of seizures. Neurology. 1966;47:260–264. 10. Tatum WO IV, Winters L, Gieron M, et al. Outpatient seizure identification: results of 502 patients using computer-assisted ambulatory EEG. J Clin Neurophysiol. 2001;18:14–19. 11. Saab ME, Gotman J. A system to detect the onset of epileptic seizures in scalp EEG. Clin Neurophysiol. 2005;116:427–442. 12. Wilson SB, Scheuer ML, Emerson RG, et al. Seizure detection: evaluation of the reveal algorithm. Clin Neurophysiol. 2004;115:2280–2291.

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13. De Clercq W, Vergult A, Vanrumste B, et al. Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram. IEEE Trans Biomed Eng. 2006;53:2583–2587. 14. Coppola G, Auricchio G, Federico R, et al. Lamotrigine versus valproic acid as first-line monotherapy in newly diagnosed typical absence seizures: an openlabel, randomized, parallel-group study. Epilepsia. 2004;45(9):1049–1053. 15. Nuwer MR, Engel J Jr, Sutherling WW, et al. Monitoring at the University of California, Los Angeles. Electroencephalogr Clin Neurophysiol. 1985;37:s385–s402. 16. Schomer DL, Ives JR, Schachter SC. The role of ambulatory EEG in the evaluation of patients for epilepsy surgery. J Clin Neurophysiol. 1999;16(2):116–129. 17. Chang BS, Ives JR, Schomer DL, et al. Outpatient EEG monitoring in the presurgical evaluation of patients with refractory temporal lobe epilepsy. J Clin Neurophysiol. 2002;19(2):152–156. 18. King-Stephens D, Mirro E, Weber PB, et al. Lateralization of mesial temporal lobe epilepsy with chronic ambulatory electrocorticography. Epilepsia. 2015;56(6):959–967. 19. Morrell M. Responsive cortical stimulation for treatment of medically intractable partial epilepsy. Neurology. 2011;77:1295–1304. 20. Fischell RE, Rischell DR. Inventors integrated system for EEG monitoring and electrical stimulation with a multiplicity of electrodes. United States Patent No. 6,320,049 B1. 2001. 21. Cook M, O’Brien T, Berkovic F, et al. Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study. Lancet Neurol. 2013;12:563–571. 22. Spanaki M, Smith B, Burdette D, et al. Chronic measurement of increased epileptiform activity during menses using the responsive neurostimulator system (RNS) in a patient with catamenial seizures; 2005. Available at: http://www .neuropace.com/resources/publications/0512.html#8. Accessed January 22, 2009. 23. Foucault PM. The Birth of the Clinic: An Archaeology of Medical Perceptions. New York, NY: Vintage; 1963. 24. Foucault PM. The Order of Things: An Archaeology of Human Sciences. New York, NY: Vintage; 1966. 25. Forsyth CW. The ecology of epilepsy. BJM. 1923;69:512–516. 26. Davenport CB. The ecology of epilepsy: racial and geographic distribution. Arch Neurol Psychiatry. 1923;9(5):554–556.

CHAPTER 6

CLINICAL USE OF Ambulatory EEG IN PEDIATRICS ADRIANA ULATE-CAMPOS, MD and TOBIAS LODDENKEMPER, MD

INTRODUCTION The list of differential diagnoses for paroxysmal events in pediatrics is broad and varies according to the specific age group. For example, in new onset paroxysmal events presenting with eye and arm movements in a healthy infant, clinicians might consider behavioral episodes, stereotypes, ­spasmus nutans, or epileptic spasms, with considerable differences in terms of ­management and prognosis between these entities. One of the greatest challenges in pediatrics is the difference in obtaining a seizure history, that is, the inability to interrogate an infant as to what happened. We rely solely on the history provided by the parents and caretakers who are sometimes too nervous or worried to recall all details and the time sequence of the event. In older children and adolescents, alternative diagnoses such as tics, behavioral events, daydreaming, attention deficit disorders, migraine, syncope, cardiac arrhythmias, parasomnias, and seizures are the most frequent alternatives in the differential diagnosis of paroxysmal events (1–3). If after a detailed history and neurological examination the clinician is still uncertain if the event in question is a seizure, then an electroencephalogram (EEG) may be helpful to support the diagnosis and to provide further details regarding the presentation, since an accurate diagnosis is mandatory for an appropriate management. Inpatient video-EEG monitoring is the gold standard for the diagnosis of epilepsy, but it is expensive due to the high technical and personnel demand required (4,5). The suspected event is rarely captured on the first routine video-EEG monitoring (6). Repeated EEG recordings increase the possibility 99

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of capturing epileptiform activity, from 50% during the first record to 92% during the fourth one (7). Therefore, prolonged continuous EEG recordings may also enhance the probability of capturing the suspected event as well as interictal epileptiform discharges. Ambulatory electroencephalography (aEEG) consists of usually continuous monitoring for 24 hours or more, often without video recording (8,9), although technological advances now also facilitate home video-EEG monitoring. Ambulatory EEG implies less ­disruption of a child’s routine as the patient and family can stay home, and this may also increase the possibility of capturing the desired event. If aEEG is not ­informative or cannot answer the underlying question, video-EEG or long-term monitoring (LTM) may provide additional diagnostic options (6). The main indications for aEEG in pediatrics are differentiating ­epileptic from nonepileptic events, studying sleep-triggered EEG abnormalities, determining seizure/epileptiform discharge frequency, and determination of seizure localization. The referring physician neurologist should provide information regarding the reason for performing the aEEG, a brief description of the events in question, and ideally one or two provisional diagnoses to permit interpretation of the EEG in the setting of the clinical question (4). The clinical information and detailed description of the event may also provide the opportunity to adapt the technical methodology of the study to the patient, for example, adding polygraphic parameters or repeating h ­ yperventilation testing if staring episodes are under investigation (10,11). We strongly recommend to give the family written instructions along w ­ ith verbal instructions, as well as an event diary log to write down the events. Not every child may be a candidate for an aEEG, and in the following we provide some thoughts on how to select the patients to increase the aEEG utility in this population. The main advantages of aEEG are recording in the habitual ­environment of the child, which may allow the child to better tolerate the procedure, and which may permit recording of habitual sleep patterns, as well as overall lower cost than inpatient EEG. Its main disadvantages are the lack of video in most cases, the inability to evaluate the child during the event, the incapacity to reduce antiepileptic drugs (AEDs) as an outpatient due to safety concerns and the inability to interfere and repair electrodes that may develop artifacts during the recording. TECHNICAL DETAILS OF PEDIATRIC aEEG EEG electrode placement in pediatric patients requires specially trained and skilled technicians (12). The lab atmosphere must be comfortable, quiet, and child-friendly, with decoration and toys suitable for children (12). At least one parent or caregiver should be present in the lab during the electrode placement to calm the child and to explain what is being done (13), and in behaviorally challenging patients, a child life specialist can be of huge help to prepare and perform for the electrode placement and recording.

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Newer systems according to the 10–20 standard of electrode ­placement have overcome the problem that was noted during the early four-to eightchannel cassettes, in which highly focal seizures or asymmetries could go undetected, and artifacts may have been more easily confused with ­epileptiform abnormalities (14–16). Electrode placement according to the standard 10–20 system is now considered standard of care (2,6,16–18). In the neonate, the system has to be modified, with at least eight electrodes positioned according to the 10–20 system, but most neonates tolerate a 10- to 20-electrode placement of 16 electrodes well (10). Electrodes could be attached to the scalp with collodion, fixed onto a head cap or inserted into a soft cap to increase tolerability (10). If electrodes are attached, the head may be wrapped to lessen the chance of electrode misplacement, especially in active kids (4). Neonates and infants require close care of the skin, since the neonatal scalp is more fragile; collodion may be changed for another paste that is easier to remove and has a lower incidence of scalp lesions and scalp irritation (10,13). Extreme cases of energetic children may need sedation for electrode placement and closer parental supervision during the recording because electrodes could be pulled, bent, or unplugged (17). These situations can often be overcome without sedation by preparation with a child life ­specialist, and based on this strategy, we did not require sedation in any of our pediatric cases in past years. In order to prevent scalp lesions and improve the quality of recordings, we may include scheduling of daily visits to the EEG laboratory to check, replace, or gel electrodes as needed (18), and to recognize skin breakdown early. But this may not be feasible if the family lives far away from the EEG laboratory, in which case at times caregivers may be trained to gel electrodes with electrode paste (18). Additional channels for electromyography (EMG), oximetry, and EKG may enhance the system detection capacity in selected cases (18). Some patients may benefit from eye or chin leads, or a respiratory belt and oximetry, if sleep patterns and respiratory patterns, that is, apneas, are of importance. For example, an EKG channel provides a lot of information when studying episodes of “fainting” or syncope that could potentially have cardiac origin. EMG may assist with the differentiation of abnormal movements and may also provide improved EEG time r­ elationships and correlation for motor seizures, such as myoclonus, ­epileptic spasms, and tonic seizure, in particular when no video is available. At least an EKG channel is recommended in pediatric aEEG, and the combination of other channels may be adapted according to the episode in ­question as described by the ordering clinician. Since records are now usually electronic, the montage, amplitude, ­display speed, and filters may be modified as needed when the aEEG is evaluated by the neurologist to obtain most information from the recording. Another option is computer assisted outpatient EEG which may improve

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the seizure detection in aEEGs (2). But visual analysis by a knowledgeable person remains the gold standard (35). Activation procedures may be performed immediately after EEG lead placement, during a baseline EEG recording in the EEG laboratory. This will also permit a quick visualization of the tracings by the technician, including tentative lead repairs and including impedance checks (2,12). If there is a clear asymmetry, the distance between the involved electrodes could be checked and replaced, if needed. This baseline recording usually includes eye opening and closure, as well as activation procedures such as hyperventilation and intermittent light stimulation and as appropriate mental stimulation (ie, animal naming or counting) to enhance the information provided by the aEEG. Hyperventilation can at times be performed around 2 to 3 years of age. Most children with normal development around the age of 4 to 5 may be able to comply with blowing a pinwheel, but in younger children sobbing or crying may have a similar hyperventilatory effect (6,12). Video recordings are often useful since viewing the semiology of the event makes it easier to interpret the concomitant electrical record. In a cohort of adult and pediatric patients, home video aided in the i­ nterpretation of the aEEG in approximately one third of cases (19). In cases in which aEEG has video, this compromises mobility, since the child either may move out of the camera range and might not be visible during the suspected event, or ­ caregivers must constantly monitor and tentatively reposition the camera (18). Most available aEEG systems can store data up to 72 hours at 256 Hertz for 26 to 32 channels (18). Ambulatory EEG equipment often requires a battery change or charge every 24 hours. If the family is able to visit the lab for an electrode check every 24 hours, then batteries may also be changed by the technologist. RECOMMENDATIONS FOR THE FAMILY Up to 98% of recordings in pediatrics are technically satisfactory (2), but to achieve this, counseling of the families, that is, by means of a counseling session, a sheet, a video, or a combination of these techniques to train caregivers regarding instructions, is crucial. An example is provided in Table 6.1. On the day of the scheduled appointment, the child should have the regular meals and receive the usual medications. For the equipment to be placed, the child should come with clean hair and without any ointments or creams in the area of electrode placement, to allow the electrodes to adhere properly. Shirts with front buttons are more practical to avoid tearing the electrodes when changing a garment over the child’s head. The

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child may attend school, but this must be planned ahead with the school staff; teacher and student counseling may need to be provided. We usually recommend that the child is supervised by an adult at all times to watch the equipment, but also to push the button and write down events in the log (Table 6.1). We suggest that the family maintains the usual daily routine i­ncluding medications, eating, and habitual sleeping hours. Vigorous physical activity should be limited, in particular contact sports and swimming (Figures 6.1 and 6.2) (2). The child is also asked to limit baths and showers to prevent interference with equipment function and recording quality (18). Snacking and gum chewing should ideally not be excessive during the recording to reduce chewing artifact (Figure 6.3). Brushing the hair and scratching the head should be minimized if possible. The family is also asked to try to keep electrical equipment such as cell phones, computers, and other portable TABLE 6.1  Recommendations for Caregivers: Ambulatory Electroencephalogram Follow usual routines (medications, eating and sleeping hours) Snacking and gum chewing should not be excessive Limit vigorous physical activity Baths and showers should be avoided Minimize hair brushing and scratching the head Electrical equipment should be kept away from the headbox Batteries should be changed every 24 hours Push the button every time an event is witnessed Keep a diary of events (description, time, and duration of the event)

Figure 6.1  Rocking artifact in an autistic patient.

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Figure 6.2  Movement artifact in an autistic patient.

Figure 6.3  Chewing artifact.

devices away from the headbox. Depending on battery life, the family is also instructed to change the battery regularly, with our current system ideally every 24 hours to permit functioning at different temperatures and for the duration of prolonged aEEGs. Caregivers are asked to push the event button every time an event is witnessed. It is crucial for families to keep a diary of events, with a detailed description, the time of day, and the duration of the event. In the experience of Foley et al, up to 30% of aEEG studies

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are returned without a written diary or accurate time log, which may reduce the yield of the study (6). IDEAL PEDIATRIC CANDIDATE FOR aEEG A higher frequency of events at baseline may increase the diagnostic yield. A history of paroxysmal events occurring at least three times per week provides a very reasonable likelihood of recording events during a 24- to 48-hour recording (6,17), and chances are even better when events occur every 24 hours (11). In patients in whom an aEEG was performed to differentiate between epileptic and nonepileptic events, a lower frequency of events at baseline tended to correlate with a lower chance of capturing an event during the aEEG (4). Events were captured in 70% of children who had a baseline frequency of daily events or more, 53% in those with a frequency of less than daily to weekly, and 0% in the small group (two patients) with events less than weekly (4). An aEEG should ideally be ordered by a pediatric neurologist. The event in question needs to be well defined by the parents to permit recognition of events. If the family cannot identify events, the yield of a study may be decreased as the event marker may not be pushed and as nonepileptic events that do not present with an associated EEG change from baseline may be missed, unless these are marked by the family (17). Patient age is another factor that needs to be taken into consideration when ordering an aEEG, as studies seem to have a higher diagnostic yield in smaller children. As demonstrated by Foley et al in a study of outpatient video-EEG, the success rate was 100% in infants, 82% to 84% in children from 1 to 11 years, and 64% to 86% in children from 11 to 17 years, but the frequency distribution was not statistically different (6). Some really hyperactive children, within the autistic spectrum or with behavioral problems, may not tolerate the evaluation and might rip the electrodes off. In this group, careful preparation with a child life specialist may permit recording of a couple of hours of video-EEG and provide a higher quality EEG recording and higher diagnostic yield over an aEEG. Therefore, the ideal pediatric candidate according to limited pediatric studies is a younger child without major behavioral challenges and with frequent, well defined and circumscribed events. INDICATIONS OF aEEG IN THE PEDIATRIC POPULATION Characterization of Paroxysmal Events (Differentiating Epileptic From Nonepileptic Events) This is the most frequent reason for performing an aEEG in pediatric patients (4,11,17). The differential diagnosis of paroxysmal events in pediatrics is very broad and age-specific. Seizure diagnosis in this population is

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challenging due to variable semiology, at times subjective symptoms, and some ­children’s age or development related inability to fully participate in the ­history ­taking (3,17). Differentiation between epileptic and nonepileptic events will avoid unnecessary ASDs and will permit adjustment of tentative therapy to the correct diagnosis (4). Approximately 3% to 43% of children admitted for monitoring with video-EEG have ­nonepileptic ­paroxysmal events (20,21). This number may be lower if an initial aEEG was performed to differentiate between both entities, with implied tentatively favorable therapeutic and economic advantages. Staring events are the most frequent paroxysmal events that trigger the indication for an aEEG in children (17). Other events are fainting spells, vomiting, nausea, episodic fixed gaze, consciousness disturbances, body or limb movements, sudden impulsive behavior, respiratory changes, sudden sensations, and nocturnal awakening (6,15,17). In one series of 44 patients performed to differentiate seizures from nonepileptic events, 61% had an informative result, including 26% with seizures and 78% with nonepileptic events (one patient had both epileptic and nonepileptic events) (4). When the clinician suspected that the event was epileptic, the aEEG confirmed this impression in 50%, whereas when the ­clinician suspected the event was nonepileptic, this was confirmed by aEEG in 83% (4). In another pediatric study of 36 untreated patients with persistent episodic symptoms, the events were classified as epileptic in 22%, nonepileptic in 67%, and were not captured in 11%. Overall, the aEEG was helpful in clinical management in 89% (6). Of note, not all seizures always cause a detectable change on scalp EEG, and therefore a highly focal seizure might be missed if a limited number of electrodes are used and the EEG pattern may be obscured by movement or muscle artifact (14). Therefore, some authors suggest that the main role of aEEG is actually to exclude a nonepileptic event by demonstrating an electrographic seizure during the suspected episode (14). The aEEG can also help by tentatively detecting interictal epileptiform activity, but if the aEEG is normal it provides no information regarding recurrence of the episodes in question. Ambulatory EEG is also useful in differentiating the subgroup of patients with ­epilepsy who develop new events that might be nonepileptic, for example, falls, myoclonus, episodic fixed gaze, and headache (6). Around 10% to 13% of patients with paroxysmal nonepileptic events also have epileptic s­ eizures. In 35 outpatient video-EEG studies performed with this objective, the studied event was epileptic in 37%, nonepileptic in 46%, and it could not be recorded in 17% (6). Based on this study, aEEG is useful to determine management in well controlled epileptic patients who present with new onset events and apparent resistance to antiseizure medication because they might be nonepileptic in nature (22).

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Sleep-Triggered EEG Abnormalities Regular aEEG offers the advantage of recording natural sleep in its h ­ abitual setting, without all distractions and changes in routine involved for the child when he or she is hospitalized. Ambulatory EEG might provide useful information regarding interictal epileptiform discharges, sleep, and circadian and wake/sleep patterns of epilepsy (18). Ambulatory polysomnography has also been performed in children, and this was very well tolerated (23). Home ­polysomnography is believed to be of good quality, results in better sleep, and is less intrusive for the family function (23). Information regarding this indication for aEEG is scarce (18). In the study by Saravanan et al, 60% of the kids with episodes that occurred in sleep only experienced an episode, and in 40% the result was considered useful (11). In another report of six children suspected of having continuous spike and wave in sleep, the diagnosis was confirmed in only one (4). Finally, of five children who had aEEG for suspicion of epileptic aphasia, none had epileptiform discharges recorded (17). Determining Seizure and Epileptiform Discharge Frequency Since patients may not be aware of up to 60% of seizures, aEEG might be used to determine seizure frequency and response to treatment (24). In ­earlier studies, aEEG was rarely used to determine seizure frequency in pediatric patients, with numbers ranging from 1.3% (2/157) in the series by Olson et al and in 3.7% (2/54) in the series by Saravanan et al (11,17). In a more recent study by Wirrell et al, all aEEG studies were performed for this indication, and all studies provided useful information (4). Seizures were recorded three times more frequently in patients with epilepsy, as compared with those with suspected seizures (2). In one study, 24% (33/140) of the children with recorded events had EEG seizures that were not marked by an event button marker or written in the log. This suggests that aEEG may be used to determine the actual seizure frequency, also potentially detecting subclinical or clinically not noticed seizures, and results may influence management (17). Determination of Seizure Localization Ambulatory EEG is rarely used in pediatric patients to determine seizure localization. Saravanan et al investigated only one patient (1/54) for this indication, and Olson et al investigated two patients (2/157), while Wirrell et al evaluated four patients (4/64) with this objective (4,11,17). However, aEEG may give important clues whether a single seizure focus can be demonstrated and whether the patient may be a candidate for a presurgical

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inpatient EEG evaluation. Monitoring in an LTM unit will allow for safe ­antiseizure ­medication withdrawal to record more events and concomitant video for s­ emiology and clinical evaluation during and after the event that provide ­valuable information. SUCCESS RATES AND IMPLICATIONS FOR CLINICAL MANAGEMENT Seneviratne et al concluded that the yield of aEEG depends on the time of the recording, technical aspects, and patient selection criteria (18). Few ­studies have ascertained the overall utility of aEEG in pediatrics. In children aEEG was useful for the diagnosis in 31% to 84% of cases (2,4,6,11,17). In one study, aEEG recorded an event in 57% of studies, and this lead to implications for management in 31% (11). Regarding the duration of the recording, Faulkner et al found that in patients ranging from 12 to 79 years of age, events were recorded in 58% of studies after 24 hours, in 78% within 48 hours, 87% within 72 hours, and 100% within 96 hours (5). Of note, the latency to recording a seizure is shorter in generalized epilepsies (5). In a strictly pediatric population, ­tolerability must also be taken into consideration. One study focused on the potential benefit of performing video-EEG after an aEEG that did not provide diagnostic information (25). In this series, aEEG was nondiagnostic in 33% of studies, and in 70% of these cases, videoEEG subsequently recorded an event. The combination of both techniques recorded an event in 90% of children (25). Limitations of this particular study included tentative interim medication changes, and therefore frequency of attacks may have also changed (25). However, the approach to utilize aEEG as an initial screening tool when trying to characterize paroxysmal events, and then, in case aEEG is unsuccessful, follow-up with inpatient video-EEG may be overall helpful and could tentatively decrease the number of inpatient EEG admissions and therefore save costs and time while providing similar diagnostic information in most patients. Advantages Recording EEG in the setting in which events regularly occur might increase the likelihood of events happening while on EEG (1,9). Of note, hospital admissions have been shown to often transiently reduce the frequency of epileptic events (26). Therefore, aEEG recordings may have a higher ­likelihood of capturing the child’s cerebral activity in her or his habitual environment (15). Ambulatory EEG is in general tolerated and accepted well by children and families alike as they are able to remain in the comfort of their home without disrupting their usual routine (4,6,11). Since it is better tolerated, it often allows for prolonged recordings. Ambulatory EEG is convenient

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as it causes less disruption to the whole family’s routine compared with ­inpatient video-EEG, and it also provides a cost effective alternative to inpatient video-EEG monitoring. Overnight aEEG also has the great advantage of recording spontaneous, natural sleep in its habitual setting. This may provide valuable information regarding interictal epileptiform discharges in sleep and regarding day/night and circadian patterns (18). Additionally, aEEG setting recordings might also have an opportunity to uncover certain abnormalities that are only present in non-REM sleep (12), without necessarily a “first night effect,” as often seen during the first night or first few nights of inpatient video-EEG, with disrupted sleep patterns. Another advantage of aEEG recordings is that it is not linked to the ­availability of an inpatient hospital bed, and since there is no need to ­hospitalize the child, it may allow for the study to be performed sooner than waiting for a spot on the inpatient waiting list (9). Furthermore, aEEG causes lower cost as compared with inpatient ­video-EEG. The cost has been estimated to be at least 50% to 65% lower than inpatient video-EEG (2,6). Other studies in the adult population ascertained that LTM costs between 1,000 and 3,000 dollars per day; outpatient ambulatory monitoring costs between 300 and 900 dollars less (27). A third study demonstrated that inpatient EEG is four times more expensive than aEEG (28). Disadvantages The main disadvantage of aEEG is the absence of video-EEG correlation. Visualizing the behavior during the paroxysmal event may help the ­pediatric neurologist to evaluate an event better, even in the absence of an EEG correlate (17). This could allow for easier differentiation between epileptic and nonepileptic events. In particular, brief myoclonic events or epileptic spasms with variable EEG correlation may be difficult to solely diagnose on aEEG, if the EEG pattern is not clear, as the time to log correlation may not be ­sufficiently precise, in particular in patients who present with a lot of e­ pileptiform discharges on EEG at baseline. This limitation may be ­overcome by newer systems that include video recording concomitant to the aEEG. Additionally, it is possible that the seizure focus is not well seen on scalp EEG (14). This may be seen with frontal lobe seizures (29). In a study of adults with temporal lobe epilepsy, the authors were not able to find equivocal rhythmic epileptic discharges in 11% of the symptomatic temporal lobe seizures (30). Another limitation is that the electrocerebral activity may be obscured by movement or muscle artifact (14). In two larger pediatric ­studies, artifact did affect the interpretation of the study (4,17).

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If there is a technical problem, parents may not be able to fix it, like replace or relubricate electrodes, and therefore the remaining record might be difficult to interpret. If the tracing is monitored remotely, then one could contact the family to fix the problem or maybe the technician could visit the house and fix the issue (Figure 6.4) (9). Inability to test the child during and after the event is another ­disadvantage of aEEG, since this testing provides useful information for localization and lateralization (18). Yield from language, motor, memory, and responsiveness testing (among other tasks) may be lower if not performed by a trained professional and if not documented on synchronized video recordings. If the aEEG provides evidence that suggests that the patient may be a ­potential candidate for epilepsy surgery, then inpatient long-term video-EEG ­monitoring may be scheduled. Inability to withdraw ASDs as a seizure activation procedure is another disadvantage of aEEG (6). There is an understandable reluctance of physicians to taper ASDs in nonhospitalized children due to safety concerns (18), as response to status epilepticus and seizure clusters in an unattended and unmonitored recording may be suboptimal. An additional relative disadvantage is the lack of cooperation by ­caregivers to fill out the log and push the button (9). This challenge could be overcome by involving caregivers further in the procedure, including ­education and clear explanations. Cosmetic issues and concerns about stigmatization may also be a ­limitation of aEEG. Some children and adolescents may refuse to be seen by their peers with electrodes on the scalp. Lighter and easier to wear systems are under development, including a portable, wireless system ­ with two ­electrodes that uploads the data to a smartphone and waterproof,

Figure 6.4  Electrode impedance artifact with sharp appearance at F4.

6.  CLINICAL USE OF AMBULATORY EEG IN PEDIATRICS  •  111

two-­electrode EEG monitoring devices that track seizures for up to seven days (31–34), and these may help with some aspects of cosmetic concerns. However, l­imited EEG recordings require that the electrodes need to be placed over suspected epileptogenic areas to enhance the possibility of capturing an event. The feasibility of these systems and their effectiveness compared with systems with more channels is still under investigation. In order to address adolescent concerns about stigmatization at school, we at times adjust to these requests by scheduling aEEG over the weekend or have child life specialists and social workers connect with the school, t­eachers, and peers to provide education in advance of the procedure. Lastly, an important aspect to take into consideration is that reviewing an aEEG is time consuming for the neurologist, as the recording is often 24 to 96 hours long. Even with the aid of a log or diary the entire recording needs to be reviewed and evaluated. PARENTAL AND CHILD PERCEPTION AND TOLERABILITY OF aEEG Ambulatory EEG is generally well tolerated by children. In a pediatric series, no patient discontinued the procedure earlier than expected (17). On a Likert scale (with 5 points being equivalent to “most satisfied”), children rated the aEEG at 3.2 out of 5. Parental satisfaction with the aEEG was higher. On a Client Satisfaction Questionnaire (with 4 relating to “high satisfaction”), aEEG was rated 3.6 out of 4. Using another Likert scale, aEEG was rated 4.3/5 by 46 families (4). Approximately 43% of parents believed that their children had problems tolerating the aEEG, mostly due to discomfort and embarrassment (4). Parents conveyed that 67% of children restricted their activity due to aEEG recordings, and 57% of parents restricted their activities (not their childs) to be able to supervise and observe better (4). CONCLUSION Ambulatory EEG is a useful and effective tool to diagnose many pediatric seizure types and to evaluate frequent paroxysmal events in children. Ambulatory EEG is often most practical in children who are behaviorally normal and are able to tolerate the procedure. Event frequency should be ideally two to three times per week. Families will need detailed counseling on the need for observation and the need for a detailed and precise event log, if ambulatory synchronized video recordings are not available. Instructions should imply that the family needs to help with the assessment by pushing the event button, and reporting the  events on the diary, since this information is crucial to the interpretation of the study. The parent also needs to indicate whether the recorded event was the event in question that triggered the ordering of the aEEG. Further studies are needed

112  •  Ambulatory EEG Monitoring

to ascertain the role of aEEG in the workup of pediatric patients with paroxysmal events, spells, and new onset seizure types, as well as nocturnal spike burden. We envision aEEG as a cost effective, high-yield screening tool for most pediatric patients who do not require inpatient evaluation or monitoring, to determine whether video-EEG may be helpful or needed.

REFERENCES 1. Ebersole JS. Ambulatory cassette EEG. J Clin Neurophysiol. October 1985;2(4):397–418. 2. Foley CM, Legido A, Miles DK, Chandler DA, Grover WD. Long-term computer-assisted outpatient electroencephalogram monitoring in children ­ and adolescents. J Child Neurol. January 2000;15(1):49–55. 3. Kutluay E, Selwa L, Minecan D, Edwards J, Beydoun A. Nonepileptic paroxysmal events in a pediatric population. Epilepsy Behav. February 2010;17(2):272–275. 4. Wirrell E, Kozlik S, Tellez J, Wiebe S, Hamiwka L. Ambulatory electroencephalography (EEG) in children: diagnostic yield and tolerability. J Child Neurol. June 2008;23(6):655–662. 5. Faulkner HJ, Arima H, Mohamed A. The utility of prolonged outpatient ­ambulatory EEG. Seizure. September 2012;21(7):491–495. 6. Foley CM, Legido A, Miles DK, Grover WD. Diagnostic value of pediatric ­outpatient video-EEG. Pediatr Neurol. February 1995;12(2):120–124. 7. Salinsky M, Kanter R, Dasheiff RM. Effectiveness of multiple EEGs in ­supporting the diagnosis of epilepsy: an operational curve. Epilepsia. July– August 1987;28(4):331–334. 8. Montavont A, Kaminska A, Soufflet C, Taussig D. Long-term EEG in children. Neurophysiol Clin. March 2015;45(1):81–85. 9. Benbadis SR. What type of EEG (or EEG-video) does your patient need? Expert Rev Neurother. May 2015;15(5):461–464. 10. André-Obadia N, Lamblin MD, Sauleau P. French recommendations on ­electroencephalography. Neurophysiol Clin. March 2015;45(1):1–17. 11. Saravanan K,  Acomb B,  Beirne M,  Appleton R. An  audit  of ambulatory  cassette EEG monitoring in children. Seizure. December 2001;10(8):579–582. 12. Kaminska A, Cheliout-Heraut F, Eisermann M, Touzery de Villepin A, Lamblin  MD. EEG in children, in the laboratory or at the patient’s bedside. Neurophysiol Clin. March 2015;45(1):65–74. 13. Velis D, Plouin P, Gotman J, da Silva FL, and the ILAE DMC Subcommittee on Neurophysiology. Recommendations regarding the requirements and applications for long-term recordings in epilepsy. Epilepsia. February 2007; ­ 48(2):379–384. 14. Aminoff MJ, Goodin DS, Berg BO, Compton MN. Ambulatory EEG recordings in epileptic and nonepileptic children. Neurology. April 1988;38(4):558–562. 15. Batho KM,  Leary PM,  Arens L. The ambulatory electro-encephalogram as a diagnostic tool in a children’s hospital. S Afr Med J. 1986; 27;70(7):428–430. 16. Morris GL 3rd, Galezowska J, Leroy R, North R. The results of computer-assisted ambulatory 16-channel EEG. Electroencephalogr Clin Neurophysiol. September 1994;91(3):229–231.

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17. Olson DM. Success of ambulatory EEG in children. J Clin Neurophysiol. March 2001;18(2):158–161. 18. Seneviratne U, Cook M, D’Sousa W. The electroencephalogram of idiopathic generalized epilepsy. Epilepsia. 2012;53:234–248. 19. Goodwin E, Kandler RH, Alix JJ. The value of home video with ambulatory EEG: a prospective service review. Seizure. June 2014;23(6):480–482. 20. Patel H, Scott E, Dunn D, Garg B. Nonepileptic seizures in children. Epilepsia. November 2007;48(11):2086–2092. 21. Bye AM, Kok DJ, Ferenschild FT, Vles JS. Paroxysmal non-epileptic events in children: a retrospective study over a period of 10 years. J Paediatr Child Health. June 2000;36(3):244–248. 22. Seneviratne U, Reutens D, D’Souza W. Stereotypy of psychogenic nonepileptic ­ ­ seizures: insights from video-EEG monitoring. Epilepsia. July 2010;51(7):1159–1168. 23. Marcus CL, Traylor J, Biggs SN, et al. Feasibility of comprehensive, unattended ambulatory polysomnography in school-aged children. J Clin Sleep Med. August 15, 2014;10(8):913–918. 24. Blum DE, Eskola J, Bortz JJ, Fisher RS. Patient awareness of seizures. Neurology. July 1996;47(1):260–264. 25. Alix JJ, Kandler RH, Mordekar SR. The value of long term EEG monitoring in children: a comparison of ambulatory EEG and video telemetry. Seizure. September 2014;23(8):662–665. 26. Riley TL, Porter RJ, White BG, Penry JK. The hospital experience and seizure control. Neurology. July 1981;31(7):912–915. 27. Thompson JL, Ebersole JS. Long-term inpatient audiovisual scalp EEG monitoring. J Clin Neurophysiol. 1999;16(2):91–99. 28. Faulkner HJ,  Arima H,  Mohamed A. Latency  to  first  interictal  epileptiform  discharge  in epilepsy with  outpatient  ambulatory  EEG. Clin Neurophysiol. 2012;123(9):1732–1735. 29. Williamson PD. Frontal lobe seizures. Problems of diagnosis and classification. Adv Neurol. 1992;57:289–309. 30. Blumhardt LD, Smith PE, Owen L. Electrocardiographic accompaniments of temporal lobe epileptic seizures. Lancet. May 10, 1986;1(8489):1051–1056. 31. Waterhouse E. New horizons in ambulatory electroencephalography. IEEE Eng Med Biol Mag. 2003;22:74–80. 32. Luan B, Sun M. A simulation study on a single-unit wireless EEG sensor. In Proceedings of the IEEE Annual Northeast Bioengineering Conference, April 2015. 33. Do Valle BG, Cash SS, Sodini CG. Wireless behind-the-ear EEG recording device with wireless interface to a mobile device (iPhone/iPod touch). Conf Proc IEEE Eng Med Biol Soc. 2014;2014:5952–5955. 34. Lehmkuhle M, Elwood M, Wheeler J, Fisher F, Dudek E. Development of a discrete, wearable, EEG device for counting seizures (abstract). In 69th Annual Meeting of the American Epilepsy Society. Philadelphia, PA: AES; December 4–8, 2015. Abstract nr 2158 2015. 35. Seneviratne U,  Mohamed A,  Cook M,  D’Souza W. The  utility  of  ambulatory  electroencephalography  in  routine  clinical practice: a  critical review. Epilepsy Res. July 2013;105(1–2):1–12.

CHAPTER 7

SHORT-TERM AMBULATORY EEG JASON L. SIEGEL, MD and WILLIAM O. TATUM, IV, DO

INTRODUCTION Ambulatory EEG (aEEG) is an essential tool in the management of patients with paroxysmal neurological disorders. Identifying both ictal and interictal abnormalities has direct effects on treatment, but the question for aEEG remains, “How long should the patient undergo the procedure?” Prolonging aEEG incurs higher cost, more patient inconvenience, and may be unnecessary, depending on the type of information the clinician is trying to obtain. Though aEEG can be performed for several days, short-term ambulatory EEG (ST-aEEG) can serve as a cost effective and accurate test. We define ST-aEEG as an aEEG that lasts 24 hours or less. The benefit of a ST-aEEG is that it captures at least one night of sleep during prolonged aEEG recording. The earliest aEEG technology was limited by cassette tape recording, with no method to reformat or filter after the electroencephalography (EEG) had been recorded. There were also limitations in battery capacity, and therefore it was initially implemented for use over a 24-hour time period (1,2). Since then, these limitations have improved as technology has advanced, and ST-aEEG has more advanced applications and practical uses for the clinician. RATIONALE FOR 24-HOUR MONITORING Neurologists consider many factors when deciding on the most appropriate type of EEG for their patient. Important factors include diagnostic yield and duration of the recording time, but patient availability, environment, artifact burden, cost, accessibility, and reimbursement also merit consideration (3) (Table 7.1). 115

116  •  Ambulatory EEG Monitoring

TABLE 7.1  Relative Comparison of EEG Methods Used in the Evaluation of Paroxysmal Episodes Routine Scalp EEG

aEEG

ST-aEEG

Inpatient VEM

Time

+

++

+/++

+++

Yield

+

++/+++

++/+++

+++

Patient availability

+++

+/++

+/++

+

Natural environment

++

+++

+++

+

Artifact

+

++/+++

++

+/++

Cost

+

++/+++

++

+++

Accessibility

+++

++

++

+

Reimbursement

+

++/+++

++/+++

+++

Note: Ratings are identified with + to +++ based on the lowest to highest association with the feature. Source: Adapted with permission from Ref. (3). Rubin DI, Daube JR. Clinical Neurophysiology. 4th ed. New York, NY: Oxford University Press; 2016.

EEG techniques vary, and the “best” test depends on what question the neurologist is trying to answer. The gold-standard method of event diagnosis, classification, and characterization is inpatient video-EEG ­monitoring. This method, however, is expensive, takes patients out of the natural ­environment in which they normally have events, and may not be accessible due to local expertise, insurance coverage, or geography. The most common EEG method is the 20- to 30-minute routine scalp EEG obtained as an outpatient. While readily accessible, inexpensive, easy to perform, and quickly interpretable, it has a lower yield when compared with long-term EEG monitoring. Furthermore, seizures are rarely captured during an outpatient routine scalp EEG recording. The added benefit from ST-aEEG has been ­demonstrated to provide a significantly higher yield of capturing a seizure ­during aEEG monitoring compared with a routine scalp EEG performed as an outpatient (4). In order for ST-aEEG to be useful, there must be an abnormality that is readily detected in less than 24 hours. Patients with paroxysmal events that occur during sleep and those with frequent daily events are the best candidates for ST-aEEG (Figure 7.1). Other practical reasons for ST-aEEG include assessment of persistent epileptiform discharges (EDs) prior to considering a trial of weaning antiseizure drugs (ASDs) from patients with prolonged seizure freedom. One practical concern involves addressing the EEG prior

7.  SHORT-TERM AMBULATORY EEG  •  117

FIGURE 7.1  Repetitive right temporal sharp waves present on CAA-EEG during sleep.

to considering a trial of tapering ASDs before determining if a patient is an acceptable risk to operate a motor vehicle and be released to driving. The yield of 24-hour continuous EEG (cEEG) monitoring varies depending on the patient population. In critically ill patients, cEEG monitoring found that seizures were detected within the first 24 hours of cEEG monitoring in 88% of all patients who would eventually have seizures (5). Additional ­hospital-based studies have shown that within 30 minutes, EEG in the intensive care unit (ICU) identifies 2% to 3% of patients who had seizures, with 18% to 34% of patients having epileptic discharges (6). Extending the EEG to 16 to 24 hours, 14% of patients had new or additional EDs and 6% of patients had new or additional seizures (6). These patients are critically ill and may have multiple reasons to have provoked seizures, limiting the generalizability of these studies to patients in the home, ambulatory setting. Similar to critically ill patients, however, the yield of identifying seizures is greatest for ambulatory patients within the first 24 hours of recording (7). IDENTIFICATION OF SEIZURES The earliest studies of aEEG were conducted with four channels without video capabilities. Despite using limited channels during aEEG, some studies found a high concordance with respect to recording seizures (1). Though ST-aEEG can reliably identify electrographic seizures (8,9), if used routinely to identify ictal events it has a low probability to detect an event in a given 24-hour period. When looked at over a 5-day period, one aEEG

118  •  Ambulatory EEG Monitoring

study found that 58% of typical ictal events (seizure or nonepileptic attack [NEA]) occurred within 24 hours, which improved to 78% after 72 hours and reached 100% by 96 hours (8). Still, 58% of ST-aEEG demonstrated efficacy in the majority of patients. In patients without a known diagnosis of epilepsy, events occur less frequently. In this population, 8.5% to 17.4% had epileptiform abnormalities or seizures within 24 hours (4,9,10). There was an even lower yield (5.1%) in aEEGs when they were ordered by nonneurologists. Also of concern was that not all clinical events that were suspicious for seizures had EEG changes (4,9,10). The low yield raises a question of effectiveness when ordering an ST-aEEG as a diagnostic tool in the evaluation process of an undiagnosed patient. Children pose unique difficulties in completing even a 24-hour aEEG due to movement and intolerance of wearing the EEG. In children with clinically definite seizures, a 24-hour aEEG identified seizures in 55% of epilepsy patients with a clinical event, and 95.5% of these events had some detectable ictal EEG changes (11). Though the electrographic evidence of ictal activity had high concordance with the clinical episodes, the efficacy of the overall utility for patients with infrequent events is dubious if only 55% had conclusive results after an ST-aEEG. However, absence seizures recur multiple times daily and may be so brief that the patient is unable to quantify them and remain unaware of them (7,11). Quantifying seizures through ST-aEEG in this case may be helpful in optimizing ASD therapy. Observing electrographic ictal changes on EEG is the gold standard in differentiating epileptic seizures from NEAs. Most NEAs occur during daytime, waking hours. The frequency of NEAs is similar compared to epileptic seizure events. Only about half of NEAs are identified within 24 hours, which improves to nearly 100% by 96 hours (7,11). Despite the low occurrence of seizures over a given 24-hour period, ST-aEEG may play a significant clinical role in being able to identify subclinical focal seizures when patients report that they are seizure-free. In one large study of 502 patients evaluated with computer-assisted aEEG (CAA-EEG), about 23.4% of events were unidentified by the patient via push-button activation or bystander observation, though they were detected by the ­computer algorithm utilized for seizure detection (10). Thus, for quantification, CAA-EEG demonstrated significant benefit for seizures without awareness (Case Report on page 124). An ST-aEEG may also be used to classify the type of epilepsy. Seizures have a focal or generalized onset suspect based on interictal or ictal abnormalities identified on ST-aEEG (Figure 7.2). This is essential for providing electroclinical characterization of focal seizures for treatment. Patients with epilepsy will have a range of seizure frequencies. Rarely, patients have an event within a 24-hour period though this is highly dependent on the seizure

7.  SHORT-TERM AMBULATORY EEG  •  119

FIGURE 7.2  CAA-EEG with an event of “staring” identified by a caretaker during an absence seizure.

type and epilepsy syndrome. Therefore, ST-aEEG may be an e­ ffective tool in diagnosing “spells” or “events,” classifying seizures for treatment, and quantifying seizures (such as absence and myoclonic seizures). IDENTIFICATION OF INTERICTAL EDs The presence of interictal epileptiform discharges (IEDs) in the aEEG serves as an indirect biomarker for ­seizures and is the hallmark of epilepsy. Neurologists may be able to arrive at conclusions about patients’ events by identifying IEDs on ST-aEEG. Recognizing normal variations, benign variants, and artifact is the first crucial step to distinguish abnormal IEDs given the ability of some waveforms on EEG to mimic IEDs (12). Often confused with abnormal IEDs, normal fluctuations of background activity during light sleep and benign variants (such as wicket spikes) may prove to be a challenge when interpreting EEG (13). In addition, the presence of a “spike” due to artifact may be misinterpreted as abnormal and lead to misdiagnosis and mistreatment (14). After

120  •  Ambulatory EEG Monitoring

excluding normal waveforms and artifact on aEEG, overall the ­concordance with ­routine scalp EEG in detecting epileptiform abnormality is about 77%: 79% of focal IEDs and 100% of generalized IEDs (1). As opposed to the low yield of recording seizures during a 24-hour period of ST-aEEG monitoring, recording IEDs occurs more frequently. The mean latency to detection of the first IEDs on ST-aEEG is under 6 hours. Up to approximately one-half of patients (range 24%–45.5%) will have an appearance of IEDs within 20 minutes (15), where a wide range of recovery has been found within 24 hours ranging from 33% to 85% (7,15,16). Patients who reported paroxysmal events at least once a week have been demonstrated to have a higher yield of diagnostic cEEGs compared with those who have more infrequent episodes (16). Generalized epilepsies have shorter latency for recording an IED compared with those with focal epilepsy, with the median latency for recovering focal IEDs occurring at a mean of about 4.5 hours, and the latency of recovering generalized IEDs within 1 hour (7). Identifying IEDs is a major use for ST-aEEG and can raise the s­ uspicion or support the diagnosis of epilepsy. Importantly, ST-aEEG can help with seizure classification to determine the mechanism for seizure onset and assist in ASD selection. ST-aEEG has a higher yield for detection of IEDs compared with a routine scalp EEG, which has a recording time of 20 ­minutes (7). This is especially true in patients with the generalized epilepsies, where the mean latency of a generalized IED occurs in under an hour but more than the 20 minutes of recording time that is typically allocated to a routine scalp EEG recording. Though ST-aEEG is less specific than the gold standard of inpatient video-EEG monitoring, it seems to be a reasonable next step in diagnosing epilepsy after routine outpatient 20 min EEG (10). PAROXYSMAL EVENTS DURING SLEEP ST-aEEG may also be a clinically useful study in diagnosing paroxysmal events during sleep. Simply increasing the duration of an outpatient routine scalp EEG from 20 to 40 minutes provides an incremental increase in the overall yield of recovering an abnormality by about 11% (17). However, N3 sleep is not commonly captured on outpatient routine scalp EEG (sometimes even N1 or N2 are not attained) despite the fact that about half of the abnormalities that occur do so during this stage of sleep (17). Several nights of monitoring may not be necessary, therefore capturing one night of sleep may be helpful in distinguishing different causes of events that frequently arise from sleep. Some epilepsy syndromes activate the EEG during sleep (eg, Benign Childhood Epilepsy with Centrotemporal Spikes or Electrical Status Epilepticus of Slow Sleep), which is unobtainable with routine scalp EEG recording. The use of ST-aEEG is therefore invaluable in assessing an overnight EEG that contains deeper stages of sleep in these clinical situations.

7.  SHORT-TERM AMBULATORY EEG  •  121

Events that occur during sleep are often difficult to diagnose on c­ linical grounds because the patient is either unaware or returns to sleep and is often unable to provide an adequate history. Bed partners may awaken late in the course of the event or be unable to fully witness events when they occur in the dark, further complicating reports. Additionally, there is overlap between the semiology of epileptic and nonepileptic events that occur from sleep. Focal epilepsy originating from the frontal lobe is often activated by sleep and characterized by hypermotor behavior. Brain MRI and routine scalp EEG are frequently normal, but recording an event may be diagnostic if ST-aEEG demonstrates electrographic seizures during review of the video and EEG. Nocturnal seizures may result in sleep arousal. Though these arousals are not ictal manifestations, and thus do not demonstrate a suggestive semiology for a seizure; 72% of focal seizures arising from sleep were followed by an arousal or awakening that led to sleep interruption. These sleep interruptions lasted significantly longer than the seizures themselves, especially if they occurred between 4:00 and 7:00 a.m. (18). In patients with nocturnal frontal lobe epilepsy (NFLE), minor motor events composed of brief stereotyped movements are frequently seen during video review (15). Review of the EEG has revealed these events as a motor expression of frontal lobe epilepsy and may or may not occur in direct relationship to epileptiform activity. The relationship may be variable without evidence provided by EEG to link subtle motor events as an expression of frontal lobe seizures to epileptiform activity (18). Therefore, the absence of ictal recording during minor motor events should prompt clinicians to take this into consideration when using aEEG to optimize epilepsy management. It can be particularly difficult to distinguish NFLE from parasomnias occurring as arousal disorders from nonrapid eye movement (NREM) sleep, rapid eye movement (REM) sleep disorders (eg, REM behavior disorder), and nocturnal panic attacks. This is especially true given the absence of EEG changes, the technical limits of aEEG, and the brief nature of NFLE. Various types of nocturnal seizures emanating from the frontal lobe or extratemporal cortices include paroxysmal arousals, paroxysmal nocturnal dystonia, and seizures manifest as epileptic nocturnal wandering; most patients have more than one of these types of seizure manifestations (19,20). The clinical features are often helpful but may overlap. The video component of an ST-aEEG can help identify key semiology and characteristic ictal signs that may be more specific and more useful in making the diagnosis of epilepsy versus parasomnia or other nonepileptic events (19,21) (Table 7.2). Many parasomnias occur infrequently, which may limit the utility of a single overnight ST-aEEG. However, seizures associated with NFLE typically reoccur 20 to 40 times per month which increases the nightly yield, especially if the pretest probability for NFLE is high. In addition, there is

122  •  Ambulatory EEG Monitoring

TABLE 7.2  Comparison of Clinical and Video-EEG in Epilepsy, Polysomnographic Features in Nocturnal Frontal Lobe Epilepsy, Non-Rapid Eye Movement Parasomnias, Rapid Eye Movement Behavioral Disorder, and Nocturnal Panic Attacks Nocturnal Panic Attacks

NFLE

Parasomnia

RBD

Age at onset

Variable, childhood/ adolescence

Usually 30 kg/m2, and mild daytime sleepiness • Comorbid congestive heart failure, atrial fibrillation, treatment-resistant systemic hypertension, diabetes mellitus type 2, ischemic stroke, TIA, nocturnal cardiac dysrhythmias, pulmonary hypertension • History of an accident or near miss at work or when driving that could relate to sleepiness • Commercial drivers with large neck size or obesity • Considering surgery for snoring, mandibular advancement splint, and bariatric surgery Source: Adapted from Ref. (5). Collop NA, Anderson WM, Boehlecke B, et al. Clinical guidelines for the use of unattended portable monitors in the diagnosis of obstructive sleep apnea in adult patients. Portable Monitoring Task Force of the American Academy of Sleep Medicine. J Clin Sleep Med. 2007;3(7):737–747.

TABLE 8.2  AASM Clinical Guidelines for HSAT • HSAT should be performed on patients with a high pretest probability of moderate-to-severe obstructive sleep apnea (OSA) and without significant comorbid medical conditions which require more extensive polysomnographic monitoring. • May be indicated to diagnose OSA in patients for whom in-laboratory PSG is not possible by virtue of immobility, safety, or critical illness. • Could be used to monitor response to non-CPAP treatments for OSA (eg, oral appliance or positional devices). • The HSAT device should record a minimum of three signals (airflow, respiratory effort, and blood oxygenation). • The sensors used to record HSAT should be the same as those used in level 1 PSG. • Allow display of raw data capable of manual scoring or editing of automated scoring by a qualified sleep technologist.

• HSAT should be done in conjunction with a comprehensive sleep evaluation, be recorded in an AASM-accredited sleep center, and read by an AASM board-certified (or eligible) sleep specialist. • An experienced sleep technologist must apply the sensors and educate the patient in sensor application. • The HSAT test raw data should be available for display to assess their quality. The study should be reviewed and interpreted by a board-certified or -eligible sleep specialist using scoring criteria consistent with published AASM standards. • Patients undergoing HSAT should have a follow-up visit with a sleep specialist to discuss the test results. • Two negative or technically inadequate HSAT tests in patients with a high pretest probability of moderate to severe OSA should prompt performing a level 1 PSG. Source: Adapted from Ref. (5). Collop NA, Anderson WM, Boehlecke B, et al. Clinical guidelines for the use of unattended portable monitors in the diagnosis of obstructive sleep apnea in adult patients. Portable Monitoring Task Force of the American Academy of Sleep Medicine. J Clin Sleep Med. 2007;3(7):737–747.

8.  AMBULATORY SLEEP MONITORING  •  135

TABLE 8.3  Comorbid Conditions That Warrant In-Laboratory Polysomnography • BMI >50 • Obesity-hypoventilation syndrome (paCO2 awake >45 and BMI >30) • Chronic obstructive pulmonary disease (COPD) with a FEV1 50 years of age) (26). HSAT technical failures can be reduced by choosing an HSAT device that is simple to use, instructing patients on how to attach the sensors, giving the patient a handout with written instructions and a picture of the setup, and a phone number for the patient to call if problems arise. Advantages of HSAT include: can be recorded at home in the patient’s natural sleep environment for more than one night if needed, easier access, reduced wait times for diagnosis and treatment, lower labor costs, and patient preference for home recordings (15,27). HSAT is far less expensive (often a tenth that of a level 1 PSG), preferred by patients, and associated with equal CPAP adherence outcomes compared with level 1 PSG (14,27,28). Disadvantages of HSAT are many and include (a) a myriad of devices with many different sensors to choose; (b) most devices do not measure

140  •  Ambulatory EEG Monitoring

sleep; (c) absence of a trained sleep technologist to identify and fix artifacts, equipment adjustments, or intervene in medically unstable patients; (d) potential to misinterpret the results because of limited data, and (e) higher technical failure rate than PSG and greater potential for data distortion or loss (15). A recent study performed a multicenter randomized controlled trial comparing home-based versus laboratory-based treatment pathways in 373 subjects at high risk for moderate to severe OSA (29). The laboratory-based pathway cost was $1,840 compared with $1,575 for the home-based pathway ($264 less when recorded at home). Per patient costs for the laboratory arm were $40 higher than the home arm ($1,697 vs. $1,736). The operating margin was $142 in the laboratory arm, compared with a loss of −$161 in the home arm. A home-based diagnostic pathway for OSA for third-party payers incurs fewer costs than a laboratory-based pathway (29). However, costs for the sleep center are comparable if not higher, resulting in a negative operating margin. ACTIGRAPHY IDENTIFY SLEEP/WAKE PATTERNS BASED ON BODY MOVEMENTS Actigraphy identifies sleep and wakefulness based on the tenet that body movements when awake are frequent and large, and absent or small during sleep (30,31). Actigraphs are compact, lightweight, computerized piezoelectric accelerometer-based microelectromechanical systems (MEMS), which use proprietary algorithm-based processing of the MEMS signal (Figure 8.4). Usually worn on the nondominant wrist, actigraphs are capable of storing digitized data for extended periods, permitting the data to be transferred from the device to computer for scoring, analysis, and reporting. Wrist actigraphy provides information about the day-to-day timing, duration, and continuity of sleep in the natural sleep environment over long time periods (typically 1–2 weeks, sometimes months). The International Classification of Sleep Disorders version 3 (ICSD-3) recommends actigraphy (a) to identify different sleep/wake patterns suggestive of circadian rhythm disorders, idiopathic hypersomnia, or insufficient sleep; (b) when evaluating patients for suspected narcolepsy for at least one week prior to multiple sleep latency testing to confirm the patient has obtained sufficient sleep over the week prior to ensure the results are valid; and (c) in patients with insomnia or circadian rhythm disorders to first characterize the sleep disorder and then evaluate the effects of treatment strategies. Actigraphy can provide objective evidence of sleep/wake activity in patients who provide poor sleep histories or sleep/wake complaints without clear explanation. The Society of Behavioral Sleep Medicine recently published a scoring and instructional manual to assist clinicians and inform researchers in the

8.  AMBULATORY SLEEP MONITORING  •  141

12:00 PM

5:00 PM

12:00 AM

5:00 AM

12:00 PM

Sunday 12/6/2015 Day 8

Monday 12/7/2015 Day 9

Tuesday 12/8/2015 Day 10

Wednesday 12/9/2015 Day 11

Thursday 12/10/2015 Day 12

Friday 12/11/2015 Day 13

Saturday 12/12/2015 Day 14 12:00 PM

5:00 PM

Legend Rest

Activity Sleep

12:00 AM White Light

Excluded

Custom

Red Light Sleep/Wake

5:00 AM

12:00 PM

Green Light Off Wrist

Blue Light Marker

FIGURE 8.4  Actigraph showing delayed sleep phase type sleep/wake schedule.

use of actigraphy (32). The most important consideration when selecting an actigraph is whether the design is suitable and has been validated for the patient population(s) who will use it (32). The second most important

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consideration is cost, not only for the device, but the proprietary software, computer interface, batteries, licenses, warranty, maintenance, and technical support. So-called accessories such as an event marker are mandatory, a light meter very helpful, and a watch perhaps unnecessary. Table  8.4 summarizes technical considerations these recommend when purchasing a commercially available actigraph for use in patients or clinical research. How Reliable Is Actigraphy in Identifying Sleep and Wakefulness? Actigraphy estimates sleep 0.88 to 0.95 compared with level 1 PSG in healthy populations. Actigraphy typically identifies sleep onset earlier than PSG especially among poor sleepers who may lie quietly trying to fall asleep (33). Actigraphy is more useful if accompanied by reports of bed, nap, and nonwear times chart on daily logs (not in the waiting room before followup). Brief daytime naps may go unrecognized. User fatigue limits how long an individual (or the caregiver) will continue to use and chart their use of it. Validation of actigraphy in patients with medical, neurological, psychiatric, and/or sleep disorders is limited. Before embarking on a study of such populations, it is best to review what has been done. The reliability of actigraphy is limited by whether the patient (or caregiver) is diligent in using it as directed.

TABLE 8.4  Society for Behavioral Sleep Medicine Technical Considerations When Purchasing an Actigraph for Use in Patients or Clinical Research • Select a device which has been validated for the patient population and which provides good customer support for the device and its computer software • Uses omnidirectional or triaxial accelerometers which are different from accelerometers used to calculate calorie expenditure or pedometry • Collects data as “time above threshold” and/or “digital integration” • Records and stores data in either 30-second or 1-minute epochs for a minimum of 2 weeks using a rechargeable or disposable battery • Has nonvolatile memory that allows information to be stored even if the battery fails • Has an event marker button which patient uses to identify bed, nap, and wake times • Has a light sensor which helps identify bed and rise times, and exposure to ambient light • Other features to consider include: sampling rate of the accelerometer, modes available for calculating activity data, appearance, size, weight, battery life, data storage, computer system requirements, water resistance, event marker button, and recording ambient light data Source: From Ref. (32). Ancoli-Israel S, Martin JL, Blackwell T, et al. The SBSM guide to actigraphy monitoring: clinical and research applications. Behav Sleep Med. 2015;13 (suppl 1):S4–S38.

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Analyzing and Interpreting Actigraphic Data Requires Knowledge of Analytic Methods Although actigraphy at first glance seems simple to read, it is not when used to its full value. Scoring rules for different age groups, patient populations, and daytime naps are largely lacking or vary widely. Recognition of artifact, false or missing data, is crucial. Understanding and selecting which analytic mode and sensitivity settings are most appropriate for a particular patient, patient population, or sleep/wake complaint is needed to obtain meaningful data. Worse yet, reviews summarizing knowledge about actigraphy signal preprocessing, quantification, feature extraction, pattern visualization, and detection of anomalies are limited (34). Many commercially available actigraphs permit the reader to select different analytic modes, longer or shorter scoring rules, and sensitivity levels (low, medium, high) to best analyze the collected data. The two most widely used analytic modes for actigraphy are Proportional Digital Integration (PDI) and Time Above Threshold (TAT). PDI calculates the area under the curve for the accelerometry output, which permits analysis of the amplitude and acceleration of movements (but not the duration or frequency of them). TAT tallies the cumulative amount of time per given time period (epoch) that a particular level of movement is above some threshold (commonly 0.1–0.2 g). TAT ignores amplitude above threshold and acceleration of movements over time. It is wise to review the medical literature and select modes and ­scoring algorithms best suited for a particular patient or study population (34,35). Some are more accurate in children while others in older adults. For example, PDI correlated more with PSG for total sleep time, sleep efficiency, sleep onset latency, and wake after sleep onset compared with TAT in a large cohort of older adult men (36). Another study compared two different actigraphs (Ambulatory Monitoring Inc., Motionlogger, AMI) and Phillips Respironics Mini-Mitter Actiwatch-2, which were simultaneously recorded during level 1 PSG in 95 pediatric patients (37). They found for the AMI device, shorter scoring rules performed best for children and longer scoring rules for adolescents, with shorter scoring rules best in children or adolescents with sleep disordered breathing. For the PRMM device, medium to longer scoring rules performed best across age and sleep disordered breathing groups. Actigraphy can be a useful research tool, typically recorded for 1 to 2 weeks. Actigraphy has been used to correlate the light exposure, impact of chronotype (lark or night owl), daytime napping, nighttime sleep duration, and daytime functioning in adolescents (38). Actigraphy recorded among a large cohort of adults with dementia identified those with apathy had a significant decrease in motor activity from 9 to 12 h and 18 to 21 h (39), whereas

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patients with aberrant motor behaviors had a significant increase in their motor activity from 21 to 24 hours. Actigraphy was used to confirm associations between short sleep duration ( 0.8) with less than 10% of research device mean values for the majority of devices (45). One study compared the reliability and validity of the Fitbit Ultra ­accelerometer at two different settings (normal or sensitive) with level 1 PSG in 63 youth (ages 3–17) (43). The Fitbit Ultra in normal mode significantly overestimated total sleep time (TST) (41 minutes) and sleep efficiency (SE) (8%), and underestimated TST (105 ­minutes) and SE (21%) in sensitive mode. Compared with PSG, the Fitbit using the normal mode demonstrated good sensitivity (0.86) and accuracy (0.84), but poor specificity (0.52) for identifying sleep from wakefulness. The Fitbit set on a sensitive mode had a sensitivity of only 0.70, accuracy 0.71, and specificity of 0.79. NOCTURNAL VIDEO RECORDINGS Patients or parents often bring home video recordings most often showing obstructive breathing or parasomnias to supplement the sleep history. Several small but prospective studies have evaluated the diagnostic utility of nocturnal home video recording in children. One study showed a 30-minute videotape recordings of the child’s head and torso exhibiting OSA signs that had a high sensitivity for predicting OSA (94%) but a lower specifying of 68% for identifying normal (47). Another study found a 15-minute audiotape had a 71% sensitivity to predict PSG-confirmed OSA in children with suspected OSA and an 81% specificity (48). Audiotape recordings in 59 children with suspected OSA found a sensitivity of 88%, specificity of 52%,

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positive predictive value of 62%, and a negative predictive value of 83% for obstructive sounds compared with PSG findings (49). Addition of videotape provided no better accuracy than a verbally administered history, is not as accurate as a level 1 PSG, but worth requesting and reviewing when evaluating nocturnal paroxysmal behaviors. NOCTURNAL SEIZURE DETECTION DEVICES Home nocturnal seizure detection devices that do not record EEG typically try to detect epileptic seizures by either quasi-piezoelectric sensors placed beneath a mattress or bedsheet (50), accelerometers (51–53), surface recorded EMG (54), motion-activated video infrared monitors, seizure alert dogs (55), and devices that detect seizures by the presence of combinations of increases in resting heart rate and decreases in galvanic skin resistance due to sweating (56–59). Studies validating these are few, and most depend on endorsement by caregivers. A recent systematic review of seizure detection devices found most focus on changes in movement and/or physiological signs and were dependent on an algorithm to determine cutoff points (56). No device was able to detect all seizures, and false alarms are frequent and can lead to alarm, fatigue, and turning the alarm off. Wrist accelerometers or surface EMG are being used to detect convulsive seizures. The Brain Sentinel® (Brain Sentinel Inc., San Antonio, TX) records surface EMG unilaterally from the biceps/triceps muscle. A recent study showed it detected 95% of generalized tonic clonic seizures (GTC) in 11 patients within an average 20 seconds after the video-EEG electroclinical onset (54). A recent prospective study using a wrist-worn wireless accelerometer on 73 consecutive patients admitted for inpatient epilepsy monitoring found the device detected 90% of GTC seizures, detecting all GTC seizures in 16 of 20 patients, two thirds of seizures in 3 (60). The rate of false alarms was 0.2 per day. Another study using accelerometers to detect nocturnal hypermotor seizures found the mean performance of the device in seven patients resulted in a sensitivity of 95% and a positive predictive value of 60% (52). The EMFIT™ movement monitor is a quasi-piezoelectric pad placed ben­ eath a mattress or bedsheet. A recent study found it correctly identified 85% of 13 GTC seizures and 54% of 13 of nocturnal seizures compared with simultaneous video-EEG monitoring in 45 children (50). Median time of the device to identify and alarm a seizure is occurring is 17 seconds and usually in less than 30 seconds. Three studies trying to use the EMFIT to recognize sleep disordered breathing or periodic limb movements showed it of limited value (56). A prospective study compared two different models of a commercially available seizure detection bedside alarm (MedPage) in 15 children undergoing inpatient epilepsy monitoring (61). In 15 patients, 69 seizures were

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recorded by video EEG. One model did not detect any nocturnal seizures. The other detected 1 of 15 GTC seizures that occurred during sleep. Another recent prospective study evaluating the ability of the SmartWatch monitor analyzed 191 seizures in 41 patients aged 5 to 41 years and sadly found it detected only 31% of GTC and 34% of seizures that had rhythmic arm movements (51). It is crucial to review the limited literature on the reliability of a device before condoning its use. SMARTPHONE APPS THAT MONITOR SLEEP, SNORING, AND/OR MOVEMENT A dizzying and ever increasing array of free or inexpensive smartphone software applications (apps) are available for Internet download that are designed to record sleep architecture and/or sleep quality (62,63), snoring (64–68), arterial oxyhemoglobin desaturations based on finger pulsatile arterial blood flow (69), periodic limb movements (70,71), and drowsy driving (72). Searching the Apple App Store using the key words “snore” or “sleep apnea” alone results in more than 300 hits. Most sleep apps use standard features of smartphone technology to generate sleep/wake data: global positioning systems (GPS), accelerometer, timer, camera, speaker, and microphone (73). Others have developed attachments or accessories that interact with the smartphone by direct, wireless, or Bluetooth attachment (eg, pulse oximeter, stethoscope). The face validity (seeming logic) and irresistible charm of these is evidenced by the ready acceptance of them. Smartphone savvy patients proudly show us sleep data summaries and interpretation, request we acknowledge these, even though the overwhelming majority of such apps have not been validated (74,75). Ignoring their often earnest efforts may possibly provoke patient dissatisfaction, dismay, or disdain. We are further amazed that some medical insurance companies promise lower insurance premiums if their insured use health app data (73). Some comfort for us are recent studies examining the validity (or lack thereof) of smartphone sleep apps. We cannot review all these, only those for which validation studies have been published. One study compared 15-minute epochs of summary graph generated by simultaneously recorded level 1 PSG and a sleep app (Sleep Time, Azumio Inc., Palo Alto, CA) in 20 volunteers with no previously diagnosed sleep disorders (62). They found no correlation between PSG and app sleep efficiency (percent of time in bed spent sleeping), percentages of light or deep sleep, or sleep latency. The Sleep Time app significantly underestimated light sleep by 28%, overestimated deep sleep by 11%, and overestimated sleep onset latency by 16%. Epoch-by-epoch comparison showed accuracy of PSG versus App was only 46% for discriminating sleep stages but discriminated sleep/wake in 86%. The app had high sensitivity but poor specificity in detecting sleep (89.9% and 50%, respectively).

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An automated wireless system built into a fabric headband (ZEO, Inc., Newton, MA, USA) for assessing nighttime sleep was developed that communicated wirelessly to a bedside base station at 2.4 GHz, interfaced with a smartphone app, and was marketed directly to consumers until the company went out of business in 2013. The headband contained three dry fabric leads which recorded EEG, eye movements, and frontalis EMG from the forehead. Using proprietary algorithms, WS scored sleep as wake, light, deep and REM sleep in 30-second epochs. Several peer-reviewed studies have compared it with in-laboratory PSG (76,77) and one with actigraphy (78). They found the ZEO over-scored REM sleep and had a low ability to detect wakefulness (76,77). One study compared ZEO with level 1 PSG and found it (a) underestimated wake, sleep onset latency, and wake after sleep onset; (b) overestimated total sleep time, sleep efficiency, and duration of REM sleep; and (c) had a sensitivity of 59% for wake, 67% for light sleep, 83% for deep sleep, and 82% for REM sleep (78). The company that developed ZEO recently went bankrupt, and customers complain bitterly on Internet sites of inability to obtain replacement parts for their device. Snoring Apps Given that 60% of men and 40% of women snore and some of them will have health-altering OSA, more than 125 smartphone apps that claim to record, quantify, and even analyze snoring are available for download on iOS or Android. A recent well-designed study evaluated how reliably three different snoring apps were able to detect, record, and analyze snoring sounds (64). They tested the reliability of each to record 600 snoring noises on four different iPhone devices, repeating the same trial six times per app, first in a soundproof environment and then in a real-life environment with various disturbing noises (64). They further compared reliability of each app with a level 3 cardiorespiratory device worn for a night by subjects with OSA. They found all three apps recorded snoring noises reliably in a soundproof environment but had trouble distinguishing snoring from other noises in the bedroom or close by. Performance differed depending on which iPhone device and version was used. Snore counts were never congruent with the level 3 device but showed at least “the same tendency.” The researchers concluded the audio recordings were generally good, but reproducibility of the data was fairly poor and data differed between apps and which mobile device they were recorded on. Snoring apps can only  tell  the patient (and clinician) if the patient snores and roughly how much. Another study found a smartphone microphone strapped to the sternum of 50 adults undergoing level 1 PSG could accurately record snoring

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sounds and estimate the number of respiratory disturbances per hour of sleep in the controlled environment of a sleep laboratory (68). The smartphone application acquired ambient sound from the smartphone microphone and analyzed it using a fast Fourier transform on a real-time basis. The diagnostic sensitivity and specificity of the respiratory disturbance index correlated with the PSG AHI for diagnosing OSA (AHI ≥ 15) were 0.70 and 0.94, respectively. Another study evaluated how well a smartphone app could distinguish snoring from other noises in the bedroom, reporting 95% accuracy, 99% sensitivity, 94% specificity, and 70% positive prediction (67). Placing the finger on the smartphone camera lens permits recording pulsatile arterial flow using photoplethysmography (PPG) (79–81). Smartphone apps have been developed that use PPG to record and analyze cardiac R–R intervals, heart rate variability (HRV), pulse oxygen saturation (SpO2), and breathing rates accurately based on pulsatile flow. Cardiologists have readily embraced the ease, value, and accuracy of these smartphone apps  ­permitting patients to forward EKG strips to them for review (82,83). Smartphone Oximeters Phone Oximeter is a smartphone accessory that integrates a pulse oximeter finger probe with a smartphone. A recent study simultaneously recorded in-laboratory PSG and the Phone Oximeter in 146 children (56 with sleep disordered breathing, 90 without it) (69). Using the software of the device, they were able to perform sophisticated spectral analyses of pulse rate variability (PRV), using it to estimate HRV and parasympathetic/sympathetic balance. Combining SpO2 and HRV analysis, they were able to correctly identify sleep disordered breathing (SDB) with a high negative predictive value of 93%, a sensitivity of 88%, and specificity of 84% (69). CONCLUSION Driven by increasing third-party payer demand and patient preference, HSAT devices are rapidly replacing level 1 PSG as the first line diagnostic test for identifying OSA in adults. Patients with OSA who are diagnosed and treated using HSAT compared with traditional level 1 PSG have similar compliance with CPAP, optimal CPAP titration pressures, time to treatment, and functional outcomes. Other methods for recording sleep and wakefulness in the more natural home setting include actigraphy coupled with sleep diaries; video recordings by caregivers to identify paroxysmal behaviors from sleep; smartphone applications to quantify sleep and wakefulness using proprietary computer

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programs; and devices recording sleep/wake patterns using even less ­physiological parameters including pulse arterial waveforms, rocking beds, pulse oximetry, or bedside behavioral observation. The ubiquitous growing presence and seductive allure of increasingly available but rarely validated health care apps require practitioners and health care organizations to consider whether data from these can or should be integrated into electronic health records, especially without validity of the data and the lack of knowledge by the practitioner that the results provided are valid. Do smartphone applications in health care require a governance and legal framework (84)?

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65. Camacho M, Robertson M, Abdullatif J, et al. Smartphone apps for snoring. J Laryngol Otol. 2015;129(10):974–979. 66. Shin H, Choi W, Kim YG, Cho J. Preliminary study for the personal handheld device based snoring detection in ordinary sleep situation. Conf Proc IEEE Eng Med Biol Soc. 2014;2014:3687–3690. 67. Shin H, Cho J. Unconstrained snoring detection using a smartphone during ordinary sleep. Biomed Eng Online. 2014;13:116. 68. Nakano H, Hirayama K, Sadamitsu Y, et al. Monitoring sound to quantify snoring and sleep apnea severity using a smartphone: proof of concept. J Clin Sleep Med. 2014;10(1):73–78. 69. Garde A, Dehkordi P, Wensley D, Ansermino JM, Dumont GA. Pulse oximetry recorded from the Phone Oximeter for detection of obstructive sleep apnea events with and without oxygen desaturation in children. Conf Proc IEEE Eng Med Biol Soc. 2015;2015:7692–7695. 70. Madhushri P, Ahmed B, Penzel T, Jovanov E. Periodic leg movement (PLM) monitoring using a distributed body sensor network. Conf Proc IEEE Eng Med Biol Soc. 2015;2015:1837–1840. 71. Bhopi R, Nagy D, Erichsen D. Can a novel smartphone application detect periodic limb movements? Stud Health Technol Inform. 2012;182:36–42. 72. Li G, Chung WY. A context-aware EEG headset system for early detection of driver drowsiness. Sensors (Basel, Switzerland). 2015;15(8):20873–20893. 73. Higgins JP. Smartphone applications for patients’ health and fitness. Am J Med. 2016;129(1):11–19. 74. Domingues A. Smartphone based monitoring system for long-term sleep ­assessment. Methods Mol Biol. 2015;1256:391–403. 75. Behar J, Roebuck A, Domingos JS, Gederi E, Clifford GD. A review of current sleep screening applications for smartphones. Physiol Meas. 2013;34(7):R29–R46. 76. Tonetti L, Cellini N, de Zambotti M, et al. Polysomnographic validation of a wireless dry headband technology for sleep monitoring in healthy young adults. Physiol Behav. 2013;118:185–188. 77. Cellini N, McDevitt EA, Ricker AA, Rowe KM, Mednick SC. Validation of an automated wireless system for sleep monitoring during daytime naps. Behav Sleep Med. 2015;13(2):157–168. 78. Markwald RR, Bessman SC, Reini SA, Drummond SP. Performance of a portable sleep monitoring device in individuals with high versus low sleep efficiency. J Clin Sleep Med. 2016;12(1):95–103. 79. Scully CG, Lee J, Meyer J, et al. Physiological parameter monitoring from optical recordings with a mobile phone. IEEE Trans Biomed Eng. 2012;59(2):303–306. 80. Guede-Fernandez F, Ferrer-Mileo V, Ramos-Castro J, Fernandez-Chimeno M, Garcia-Gonzalez MA. Real time heart rate variability assessment from Android smartphone camera photoplethysmography: postural and device influences. Conf Proc IEEE Eng Med Biol Soc. 2015;2015:7332–7335. 81. Ferrer-Mileo V, Guede-Fernandez F, Fernandez-Chimeno M, Ramos-Castro J, Garcia-Gonzalez MA. Accuracy of heart rate variability estimation by photoplethysmography using an smartphone: Processing optimization and fiducial point selection. Conf Proc IEEE Eng Med Biol Soc. 2015;2015:5700–5703. 82. Kakria P, Tripathi NK, Kitipawang P. A real-time health monitoring system for remote cardiac patients using smartphone and wearable sensors. Int J Telemed Appl. 2015;2015:373474.

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83. Pierleoni P, Pernini L, Belli A, Palma L. An android-based heart monitoring system for the elderly and for patients with heart disease. Int J Telemed Appl. 2014;2014:625156. 84. Charani E, Castro-Sanchez E, Moore LS, Holmes A. Do smartphone applications in healthcare require a governance and legal framework? It depends on the application! BMC Med. 2014;12:29.

CHAPTER 9

Chronic Ambulatory EEG With Implanted Electrodes BENJAMIN N. BLOND, MD and LAWRENCE J. HIRSCH, MD

INTRODUCTION The gold standard for the definitive diagnosis, characterization, and localization of epilepsy remains inpatient monitoring. However, as discussed throughout this text, ambulatory EEG provides several advantages over standard inpatient monitoring, including longer term observation than is practical in an inpatient stay and an ability to monitor patients in their home environment on their normal medication regimen with exposure to normal triggers. These benefits come at the cost of increased artifact from inability to continually adjust electrodes and often from lack of continuous video monitoring. A separate major advantage of inpatient monitoring is the ability to perform intracranial EEG, whether subdural (electrocorticography [ECoG]), intraparenchymal via depth electrodes (including stereo-EEG), or a combination. This intracranial monitoring substantially increases spatial resolution and dramatically improves the signal-to-noise ratio, largely eliminating artifact from the recordings. New fully implanted devices now exist, with more being developed, which allow for chronic intracranial EEG in the ambulatory setting. The current and future potential implications of chronic ambulatory intracranial EEG (CAIEEG) are the focus of this chapter. INTRACRANIAL EEG ECoG refers to the recording of electrical activity from the surface of the cerebral cortex. The main indication for ECoG has been for the surgical treatment of refractory epilepsy when other tests to identify the seizure focus are 155

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discordant or inconclusive, when there is no MRI abnormality (except select medial temporal cases), when the seizure onset zone abuts eloquent cortex (including many lesional cases), and when there is dual pathology (eg, hippocampal sclerosis plus a lesion) (1). Although some cortical mapping and identification of the irritative (“spiking”) zone can be done via brief intraoperative ECoG, implantation of electrodes in order to monitor over 1 to 2 weeks is usually required in order to identify the seizure onset zone and its relationship to eloquent cortex. Complete removal of the seizure onset zone is associated with a greater chance of seizure freedom, even after accounting for lesion resection (2). Because ECoG is recording from the surface of the brain, it is able to detect high-frequency activity that scalp recording cannot detect due to the intervening skull, muscle, and other soft tissues. High-frequency oscillations (HFOs; ripples: 80–250 Hz and fast ripples: 250–600 Hz) may help to localize epileptogenic tissue and may be more localizing than traditional interictal epileptiform discharges (3,4). ECoG is not without risk. Complications of implanted intracranial electrodes, and associated extracranial wires, amplifiers, and other equipment, including raised intracranial pressure (ICP), subdural hematoma, and infection occur in about 9% of patients and are mostly transient with permanent deficits in less than 2% and rare mortality (5). Risks are higher with greater numbers of implanted electrodes, larger subdural grids, and perirolandic location. Most significant for the current discussion, risks are also increased with increased length of monitoring, at least in part due to infection risk (6). CHRONIC AMBULATORY INTRACRANIAL EEG As described in the previous section, standard intracranial EEG provides many advantages in regards to sensitivity and localization of epileptogenic foci. However, there are significant deficiencies. In addition to the morbidity from an invasive procedure, there are limitations to the length of time such recording can take place, due to the increasing risk of complications, especially infections. This short duration, typically 1 to 2 weeks for most patients, comes with inherent limitations, as this represents an artificial period subject to potential confounders of the acute surgical procedure, medication withdrawal, and inpatient stay. Scalp ambulatory EEG, as discussed in the preceding chapters, provides comparative advantages in capturing patients without these acute confounders. CAIEEG could theoretically provide profound insights with longitudinal assessment of an individual patient’s seizures, but the quickly accruing artifact as a result of the lack of continuous electrode maintenance limits such investigations to typically a 1- to 3-day period.

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Fully implantable devices are now available for recording CAIEEG. This has the potential to be truly revolutionary, as it provides the advantages of long-term ambulatory monitoring while eliminating the main limitation of artifact, and concurrently provides the advantages of spatial resolution of intracranial EEG without the confounders present in the acute setting. Such devices may allow seizure prediction and warning, which would improve patient safety and quality of life, as well as allow responsive treatment for seizure prevention, such as with electrical stimulation (currently available via the responsive neurostimulation [RNS] device), cooling, medication administration, or combinations of these. Electrocorticographic signal analysis is also useful for brain–computer interfaces. Responsive Neurostimulation Prior research has demonstrated that “open-loop” stimulation systems (stimulate on a preset schedule regardless of brain activity, etc.), such as vagus nerve stimulation and anterior thalamus deep brain stimulation, can modestly reduce seizure frequency, demonstrating improved seizure control over time, with approximately 50% of subjects achieving at least 50% reduction in seizure frequency after several years (7). A closed-loop system would theoretically be superior, as providing stimulation only when necessary to abort seizures should be able to reduce adverse effects, minimize battery use, and expand the possible locations where stimulation could be safely administered. New intracranial devices have been developed that monitor continuous intracranial EEG for just such a purpose. Seizure detection programs can be tailored to individual patients based on prior EEG data for optimal accuracy and can be adjusted as needed. Once a seizure is detected, the device can apply electrical stimulation in order to disrupt seizure propagation, with the hope of preventing evolution into a clinical event. One of these devices, the RNS® System (NeuroPace, Mountain View, California) was Food and Drug Administration (FDA) approved in late 2013 for the treatment of focal onset epilepsy based on the results of a randomized double blind trial demonstrating decrease in seizure frequency with use of responsive stimulation compared with sham stimulation (8). This device provides responsive cortical stimulation via a cranially implanted programmable neurostimulator connected to one or two recording and stimulating depth or subdural cortical strip leads, each of which contains four electrodes (Figure 9.1). These strips or depth electrodes are surgically placed in the brain in the predetermined seizure focus. The neurostimulator continually senses intracranial EEG activity and is programmed by the physician to detect abnormal activity and then provide stimulation

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RNS® Neurostimulator

Cortical Strip lead Depth Lead

FIGURE 9.1  RNS® System. This example displays both a cortical subdural strip and a longitudinal hippocampal depth electrode lead. Source: From https://www.neuropace.com/manuals/RNS_System_User_ Manual.pdf, with permission.

(Figure 9.2 provides an example of this). The physician adjusts detection and stimulation parameters for each patient to optimize detection and control of seizures. The device is capable of storing about 6 minutes of four channels of intracranial EEG data for later review (or 12 minutes of two channels), plus diagnostic and therapeutic data for the past couple hundred of detections and stimulations (without the full EEG tracings); the exact storage can be predetermined by the clinician. Detection is based on amplitude, frequency, and other pattern features on two different channels and can be combined. Patients can also swipe a magnet over the location of the implanted device to leave a marker and to store a sample at that time, and a preceding time interval, also programmable by the clinician. The stored intracranial EEG recordings are downloaded from the device to a dedicated laptop (where there is nearly unlimited storage capacity), and the data are uploaded to a secure website where the treating MD can review it (and try out new detection parameters if desired). At our center, we encourage patients to download their intracranial EEG recordings daily to maximize the data available for interpretation and to keep clinical diaries to correlate their reporting with the device recordings. The most recent follow-up study of the efficacy of the RNS system examined patient data from an average of 5 years with the device. The initial reductions in seizure frequencies of around 50% were sustained and even improved to about 60%, and the responder rates were also about 60% at these time points (9). Quality of life, as measured by the QOLIE-89, was significantly improved (9). For a review of this subject, see Fisher and Velasco (7).

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FIGURE 9.2  Sample output from the RNS device. Channels 1 and 2 demonstrate periodic spiking from the hippocampus that is aborted with stimulation. Ds: seizure detection; Tr: treatment (ie, stimulation).

Reliability As with any new technology, prior to consideration of potential advantages, it is important to attempt to systematically validate that the diagnostics are reliable and accurate. This was formally assessed in a recent study by Quigg et al (10). Five reviewers formed five pairs that reviewed 7,221 ECoG recordings from 128 subjects. Interrater reliability for identification of seizures was variable based on patients. Half of the patients had agreement rates of 94% or greater. It was the lowest quartile with agreement rates of less than 75%, which weighted down the overall average to a moderate agreement rate of 79%. Reasons for disagreement in these subjects included (a) low amplitude rhythmic activity with limited spatial or temporal evolution until the end of the discharge; (b) discharges with clear evolving activity that were less than 10 seconds because ictal onsets, and thus, the overall duration, were ambiguous; and (c) quasi-periodic discharges during which

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runs became more organized with frequencies near 2 Hz. These c­ hallenging diagnostic situations are not unique to CAIEEG. Much of this work has been completed in analysis of critical care EEG. This process of standardizing terminology (even if chosen arbitrarily) has substantially increased interrater reliability compared with prior terminologies (11). It will be important to continue to develop rigorous terminology to improve consensus and allow research on highly epileptiform intracranial EEG patterns of varying durations. ADVANTAGES OF CAIEEG Elimination of Acute Confounding Variables A potential advantage of CAIEEG, akin to other forms of ambulatory EEG, is the ability to remove the confounders of the acute hospital environment. Seizures captured in the acute setting may be partially provoked and therefore not typical. An often cited benefit of ambulatory EEG is the ability to detect seizures when patients are on their home medications. In order to capture seizures during the short duration of stay necessitated by inpatient monitoring admissions, rapid withdrawal of antiepileptic medications is common practice. This can lead to adverse effects including a higher incidence of secondarily generalized seizures (12). There has been some literature to suggest that seizures provoked by rapid withdrawal from antiepileptic medications may not always be typical of baseline seizures and may be falsely localizing (13). Engel and Crandall reported a case of a patient who had left temporal onset of seizures during a rapid medication taper, which were of atypical semiology. The patient was ultimately found to have an infiltrating right temporal meningioma and was seizure free after right temporal lobectomy, supporting the contention that the atypical events could be ascribed to medication withdrawal and were not reflective of the patient’s underlying epilepsy (13). However, although frequently cited as a potential issue, this appears to be uncommon. Studies examining this phenomenon have indeed recorded atypical seizures, but these have also occurred while on therapeutic levels of antiepileptic medications prior to withdrawal in patients who were shown to have multifocal disease or in patients who were later found to have multifocal disease (14). There is no clear evidence that these atypical events lead to significant morbidity or impair final medical decision making (14–16). There is some concern that electrode implantation may itself serve as a provoking factor for seizures and could be an alternate reason for atypical seizures in the acute monitoring setting (14). Another chief advantage of recording in a subject’s home environment is the ability to capture normal sleep patterns. Epilepsy and sleep are highly interrelated, with both epileptiform activity and clinical seizures being

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strongly related to certain circadian patterns (17). However, the ­inpatient environment can be highly disruptive to sleep. Some early work using chronic ECoG from an RNS device over a short time frame in an epilepsy monitoring unit suggested that CAIEEG devices are capable of detecting circadian variations, demonstrating peaks in intracranial EEG energy and complexity at 0530 and 1500 (18). A recent study greatly expanded on this finding by using CAIEEG to study circadian associations longitudinally by following 65 subjects over an average of 676 days in their home environment with presumably baseline sleep patterns (19). Significant circadian rhythmicity of epileptiform activity was seen in 63 out of 65 subjects, and this was seen in all lobes, hemispheres, and for both neocortical and hippocampal foci. The main circadian peaks were at 2300 and 0500, and there were lesser peaks in the early afternoon. These results further support the importance of the relationship between sleep and epilepsy and may imply an important role for CAIEEG for diagnosing or localizing epilepsy during these normal circadian rhythms. Further, understanding the circadian peaks of epileptiform activity may be important for future seizure prediction technology. This may also be used to guide the optimal timing of antiepileptic medication administration or scheduled neurostimulation in order to provide maximal protection during highest risk periods for seizures. Length of Monitoring The main advantage of CAIEEG is the capability for long-term monitoring. As mentioned earlier, inpatient intracranial EEG is typically limited to approximately 2 weeks due to the risks of complications and other practical considerations. This results in a population of patients for whom inpatient monitoring is not sufficient to adequately localize a seizure focus. CAIEEG provides an opportunity to obtain substantially more data over months or years, with the hope this may provide more accurate and definitive localization in some of these patients. Long-term monitoring has demonstrated the potential fallibility of shorter periods of observation. An analysis of a case implanted with RNS looked at 54 seizures occurring over 2 years in a patient with bitemporal onsets (20). Although the lateralization was not significantly different for the total counts over the 2-year period, the localization and lateralization of seizures did significantly change over time (Figure 9.3). Note that the patient had predominantly left-sided seizures for the first 5 months of monitoring and then had predominantly right-sided seizures including a 6-month period with only right-sided seizures. This suggests that a short time period of observation could be misleading within an individual patient, due to the dynamic nature of seizures. The specific question of how CAIEEG can be applied in a surgical evaluation for refractory epilepsy has been analyzed in some detail in a

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Daily tracking the SOZ localization over two years 10

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FIGURE 9.3  Tallying ECoG seizures by laterality each month over a 2-year time period. Note that the patient had predominantly left-sided seizures for the first 5 months of monitoring and then had predominantly right-sided seizures including a 6-month period with only right-sided seizures. Source: Reproduced and legend modified from Ref. (20). Smart O, Rolston JD, Epstein CM, Gross RE. Hippocampal seizure-onset laterality can change over long timescales: a same-patient observation over 500 days. Epilepsy Behav Case Rep. 2013;1:56–61.

retrospective analysis of 82 subjects with medically refractory bitemporal lobe epilepsy with focal onset seizures participating in the randomized double blind trial of RNS and who were implanted with bilateral temporal lobe electrodes (21). Electrographic seizures were defined as episodes of lowvoltage fast activity or rhythmic sharp activity, distinct from background, evolving and lasting longer than 25 seconds (shorter episodes were usually not stored). Out of this group, 71 subjects were presumed to have bilateral onset of seizures. The remaining 11 subjects were presumed to have unilateral seizures but had bitemporal electrodes placed due to additional data suggestive of contralateral pathology, such as bilateral hippocampal atrophy or mesial temporal sclerosis, an intracarotid amobarbital (Wada) test indicating that the contralateral temporal lobe did not adequately support memory (five subjects), a prior contralateral temporal lobectomy (two subjects), or discordant EEG and PET lateralization (one subject). In the 71 subjects who were presumed to have bilateral seizures, 62 (87%) demonstrated bilateral seizures on CAIEEG. The first contralateral seizure

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was recorded during the first week of CAIEEG in 38.7% (24–62) and during the second week in 17.7% (11–62). However, first contralateral seizure was not detected until the third week in 6.5% (4–62), during the fourth week in 9.7% (6–62), and after the fourth week in 27.4% (17–62), which demonstrates that approximately 40% of the patients would not have had such information available during a standard 2-week inpatient monitoring stay. Even more surprisingly, 9 of the 71 “bitemporal” subjects displayed only unilateral seizures over an average of 5 years of CAIEEG. In the 11 subjects with only unilateral seizure onsets documented previously (but implanted bilaterally), CAIEEG showed that 7 (64%) of the 11 subjects had independent bilateral electrographic mesial temporal lobe (MTL) seizures; average time to record the first contralateral electrographic seizure was 72.4 days (median 35 days; range 7–330 days). Four of the 11 subjects had only unilateral seizures recorded by CAIEEG, with an average duration of 3.9 years (median 4.1 years; range 0.4–7.0 years) of recording. Figure 9.4 displays a graph of all 69 patients who were ultimately found to have bilateral seizure onsets and the time required for detection. Six of the subjects represented the particularly challenging case of patients with prior temporal lobectomy and recurrent refractory seizures. 1st week: 36.2% (25/69)

Patients

2nd week: 17.4% (12/69)

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(124,197,231, 330,368,376) 0

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FIGURE 9.4  Time (in days) to record independent bilateral temporal onset seizures for each patient. See text for further discussion. Source: Reproduced and legend modified from Ref. (21). King-Stephens D, Mirro E, Weber PB, et al. Lateralization of mesial ­temporal lobe epilepsy with chronic ambulatory electrocorticography. Epilepsia. 2015;56(6):959–967.

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This group proved to be highly variable, with two subjects confirmed to have bilateral seizures as suspected, two subjects who were thought to have unilateral but ultimately demonstrated bilateral seizures, and two subjects who were thought to have bilateral seizures but only demonstrated unilateral (contralateral to the resection) during the monitoring period. There was an average of 100 and a median of 34.5 days to detection of the first contralateral seizure. This was significantly longer than in the subjects who had not had prior temporal lobectomies (average of 38, median 11) and suggests this may be a population where CAIEEG is particularly important. The authors reanalyzed their sample to examine whether additional clinical information could reliably predict bitemporal seizures. There was no significant difference in demographic features. As might be expected, MRI was a significant predictor of unilateral or bilateral seizures, correlating with unilateral or bilateral mesial temporal sclerosis or atrophy, although it did not predict the time to detection. Thus, CAIEEG was invaluable in providing clinical information that could not be reliably gleaned in another manner. Although intriguing that CAIEEG is able to establish diagnoses that would not typically be possible during a standard 2-week admission to an epilepsy monitoring unit, the essential question is whether this EEG data can significantly change clinical management. In this study, 47 subjects did not have prior intracranial monitoring. In that grouping, the conclusion about lateralization changed in 13 of these subjects after CAIEEG (27.7%). Six subjects originally categorized as bilateral had only unilateral electrographic seizures, and seven originally categorized as unilateral had bilateral electrographic seizures with the time to detection of the first contralateral seizure within 1 week in 36.8% (14–38), during the second week in 15.8% (6–38), during the third week in 5.3% (2–38), during the fourth week in 7.9% (3–38), and after the fourth week in 34.2% (13–38). This suggests only about half of the subjects in this group would have had similar diagnosis with traditional monitoring, although it is possible that seizures would have occurred more quickly with medication withdrawal during inpatient admission. Thirty five of the subjects had already undergone prior inpatient monitoring, with all but one thought to have bilateral seizures. In this sample, three (8.6%) of the subjects who were thought to have bilateral seizures only had unilateral seizures during CAIEEG. This type of information can identify new surgical candidates, and in the entire sample of 82 patients, three eventually underwent resective surgery based on the CAIEEG recordings. Two of these patients were found to have unilateral seizures on CAIEEG. They both achieved seizure freedom with surgery, although one of the patients did require two resections. A third subject had bilateral seizures during his first

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month of recording; however, his seizures were predominantly unilateral on CAIEEG and so he was eligible for surgery in a palliative context and achieved a seizure reduction of 63%. In our personal experience, 3 out of 10 patients implanted with RNS were eventually offered resective surgeries; two of these were bitemporal cases. Both improved significantly after resection (but were not seizure free) and continued getting stimulation to the other side. One only has a few seizures per year now. Dilorenzo et al reported four additional cases of RNS patients who were not initially considered for surgery and were ultimately able to have successful resective procedures, in part due to the additional data provided by CAIEEG (22). Two of the patients had proven refractory to prior medications, resections, multiple subpial transections, and vagus nerve stimulators. They both became seizure free with additional resective procedures, which were in part guided by CAIEEG data, demonstrating that these were the active seizure foci. However, it should also be noted that both patients ultimately chose to have these successful procedures knowing that it would result in focal neurological deficits from the resection, and it is unclear how much CAIEEG data may have played a role in their confidence in this decision-making process and whether it was a decision that could have been reached without this added information. The other two patients, who are reported on, demonstrate an intriguing possibility for future seizure control. Both of these cases were patients with bilateral seizure foci, but CAIEEG was able to demonstrate that one of the foci was well controlled with RNS. This made the two subjects eligible for epilepsy surgery, as all of their current seizures were from a unilateral focus. Both subjects became seizure free after resection and with maintaining RNS on the contralateral side. An additional case was previously reported by Enatsu et al (23). Their patient had bitemporal seizures, likely from bilateral mesial temporal sclerosis. He had RNS implanted with 50% seizure reduction. Over 22 months of recording, 90% of the patient’s seizures had right onset. He had right temporal resection and became seizure free with continued RNS on the left and antiepileptic medications. In summary, this research demonstrates that CAIEEG is capable of ­providing diagnostic information that can significantly change clinical management, especially in patients with possible or definite bitemporal epilepsy. The time elapsed to recording the first contralateral seizure supports the notion that it is impractical to keep individuals inpatient to record such data, and yet the information can be achieved in the ambulatory setting in a time frame that allows for meaningful clinical decisions. In particular, the individuals with epilepsy who were newly recognized as surgical candidates and successfully underwent surgery can be expected to experience dramatic improvements in their quality of life.

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Current Limitations There are limitations to CAIEEG. Due to the current technological limitations in providing a durable intracranial device, capable of long-term recording without causing significant complications, spatial sampling is limited to a few electrodes rather than the extensive electrode arrays possible in the inpatient setting. The chronically implanted electrodes must be placed near the seizure onset zone in order to detect an electrographic seizure, which requires that standard inpatient scalp and/or intracranial monitoring be performed to plan lead electrode locations. Therefore, CAIEEG is subject to the same initial complication rates as inpatient intracranial EEG. The memory available for recording is also a limitation. The current devices monitor continuously but are not capable of storing such vast data. Only a few select events can be stored at a time, and research is dependent on subjects to download the data from the device frequently, often daily, otherwise data will be lost as they are overwritten by new events. Finally, current CAIEEG does not possess the same audio-visual equipment as inpatient monitoring, and it is not always possible to determine whether a clinical seizure has occurred. This is problematic as it is important for both research and clinical management to know whether the patterns detected on EEG have a clinical correlate or not, and patients are often unaware of whether or not they have seizures, with some data suggesting patients are unaware of up to 50% of their seizures (24,25). Some groups have used paired audio recording devices that are automatically triggered to record when an electrographic seizure is detected to help confirm a convulsive event (24). Despite these measures, the current generation of technology is clearly no substitute for conventional EEG monitoring in assessing seizure semiology and localization. PRIMARY RESEARCH While primarily being developed for more direct clinical applications, CAIEEG provides an excellent platform for research on cortical activity, which can now be conducted longitudinally in humans. As mentioned earlier, research on the relation of circadian rhythms to epileptiform activity may enhance our knowledge of the interactions between sleep and epilepsy and may be an essential part of future seizure prediction technology (19). Other research is focused on basic questions regarding the functioning of the human brain. There has been a substantial body of literature using intracranial EEG to investigate auditory systems in humans; see Nourski and Howard for a review (26). This includes mapping responses in different areas of auditory cortex in response to varying stimuli, investigation of frequency tuning at a single neuron level, and in some cases, even recording responses

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to direct electrical stimulation. These studies are shaping our knowledge of the complex organization of human auditory systems, including the ability to variously encode both frequency and temporal specific signals. CAIEEG devices have also been used in research in animal models for more intensive monitoring and experimentation in the field of epilepsy. Research utilizing CAIEEG in canines with epilepsy has explored automated seizure detection/prediction algorithms and the significance of different EEG patterns. A recent study focused on the potential significance of interictal bursts and their relation to ictal phenomena (27). The researchers analyzed over 11,000 hours of EEG data from four dogs, including approximately 200 ictal events. Both bursts and seizures were detected using line length, which is a feature that incorporates both amplitude and frequency, and then confirmed by review of epileptologists. The events were then modeled in a complex statistical paradigm, which assigned “states,” such as onset, resting, or offset, to each moment in time, allowing for comparison between the different events. The authors found that bursts were not correlated with seizure occurrence, except for mild correlation in one dog. However, the bursts were found to share pronounced similarity to seizure onsets and therefore may be informative in reflecting similar underlying networks. This is particularly important, as bursts are at least an order of magnitude more prevalent than seizures, and occur more regularly, and thus may provide more opportunity for clinical diagnosis and management than possible when analyzing seizures alone. Indeed, it may be possible to reduce or even eliminate the requirement to record ictal activity in select patients, with brief intraoperative recording being sufficient to record these epileptiform bursts. Future work seeks to investigate whether bursts have similar localizing value to seizures, whether bursts represent aborted seizures, and if so, how this may inform why some events propagate to clinical phenomena, why the middle of seizures is very dissimilar to bursts, whether this reflects a decrease in network synchronization, and why seizures during status epilepticus become dissimilar to both isolated seizures and bursts. FUTURE APPLICATIONS Seizure Prediction Currently, most EEG monitoring focuses on seizure detection. An emerging area of research seeks to predict seizures before they happen. There has been an ongoing debate in the literature as to whether seizures occur at random and thus whether seizure prediction is possible or not. Some authors have suggested that seizures occur in a Poisson distribution; that is, that they are  independent of each other, unless acted on by an external provoking factor, such as sleep deprivation, missed medications, and alcohol

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consumption. However, others have argued that the occurrence of a seizure changes the probability of the occurrence of another seizure, which if able to be better understood, may assist in the development of seizure prediction. Accurate seizure prediction would have tremendous applications, as it would provide an opportunity for preventative treatment, either with RNS or drug therapy, or at the least, provide a warning to an individual so that he could engage in risk reduction. Indeed, if such a seizure prediction system were sufficiently sensitive, it may be possible for individuals with refractory epilepsy to safely engage in activities such as driving, the loss of which is typically a major detriment to quality of life. CAIEEG is an ideal setting for seizure prediction research, as it provides the durability for the longitudinal study necessary to develop and test such prediction algorithms. Cook et al (24) conducted the first study in humans addressing seizure prediction with an implanted intracranial EEG device. Fifteen subjects had a NeuroVista device implanted, which consisted of two implanted subdural strips with eight platinum iridium contacts each, attached to a tunneled telemetry unit. The placement of the leads was determined by prior clinical investigations including seizure semiology, EEG, and MRI. An algorithm was developed based on data from at least 1 month of EEG and at least five seizures, which were either clinical seizures that were observed via monitoring or electrographically equivalent to the subject’s proven clinical seizures. The algorithm was developed by a cluster computing environment to be statistically robust, taking care to avoid errors from multiple comparisons. Many quantitative EEG features were analyzed, and the computer system chose the best combination for a given patient. Risk was then stratified into simple classifications that could be displayed on a handheld device carried by the subjects. The device displayed a red (high risk of seizure), green (low risk of seizure), or yellow light (indeterminate or intermediate state) to be used by the patient as a seizure forecast. Eleven out of 14 patients had an algorithm that was successful enough at suggesting high probability of seizure that they were able to move on to a prospective phase of the study. Eight of the patients had demonstrated continued success of high-probability prediction over the 4-month study period, and two of the subjects had 100% sensitivity for seizures. Five patients were also assessed for a low likelihood of seizure indicator, with four achieving 100% negative predictive value and the remaining subject at 98%. Overall though, the success of seizure detection was quite variable. The average time from receiving a high likelihood warning to clinical seizure was 113 minutes, with a standard deviation of 151 minutes. Patients spent between 3% and 41% of the record in a high likelihood state. These data suggest that current seizure prediction algorithms were much more accurate for some patients than others. Patients with the least amount of time in the high probability zone were best able to use the device to modify their behavior, such as avoiding swimming, minimizing public activities, and so on, and

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derived significant benefit from the program. However, an algorithm that is ­predicting high risk of seizure a full 41% of the time is clearly not as successful and may ultimately be excessively impairing to activity. It must also be noted that this type of device is not without morbidity, with four serious adverse events occurring among three patients over a 1-year period, including an infection and an episode of device migration leading to neck discomfort, both of which required explantation of the device. However, given the promise of accurate seizure prediction in some of the patients, these risks may be outweighed by future benefits. If zero-probability states are sufficiently reliable for a long enough time, this may allow a given patient to drive or do other activities that he or she would otherwise avoid. These devices may also allow for intervention with medications to prevent seizures and return to a lower risk brain state. In an additional analysis of the same subjects, Cook et al (28) sought to further delineate the effect of the time interval between seizures on the likelihood of seizure occurrence. Identifying the existence of a power law and longmemory process would demonstrate that the occurrence of a seizure strongly affects the probability of future seizures over a long time period, and the details of such relationships could be used for more accurate seizure prediction. The authors analyzed data over 2 years from eight of their subjects whose data were stable enough for such robust statistical analysis. When combining all the events together, they were able to neatly define a power law suggesting that the frequency of seizures varies as a power of the interseizure time interval. They then sought to calculate measures of ­long-memory processes for each of the eight subjects. Two of the subjects did not have stationary enough data and could not be computed. Five out of the remaining six subjects were found to have Hurst exponents between 0.66 and 0.77, which suggest a persistent time series. A persistent time series is one wherein extremes are correlated with each other. In this case, it suggests that the longer the period of seizure freedom, the less likely the subject is to have a seizure, and that indeed, seizures beget seizures during the time period of the long-memory process. Perhaps most interestingly, the range of time scales that were significant in this analysis varied from 30 minutes to up to 40 days. This type of long-term relationship affecting the probability of seizure occurrence could have dramatic implications for the ability to create seizure prediction tools. The subjects who had long-memory functions were the same individuals who had successful seizure prediction in the prior study. In another analysis of these subjects, the researchers examined seizure duration and interseizure intervals (29). Overall, the subjects remained highly variable. The number of seizures varied from 10 to 1,564 and that was after excluding a subject who had approximately 4,000 subclinical seizures. There were some overall trends, including temporal seizures being of longer duration than extratemporal. Generally, longer events were more likely to be clinical and recognized; the mean duration of clinically recognized events

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was 99 seconds against 82 seconds for electroencephalographically identical but unrecognized events and 18 seconds for subclinical seizures. However, events of 7 to 8 seconds duration were often clinically recognized, and sometimes extremely brief events of 3 seconds were recognized. Conversely, subjects lacked awareness of some events of more than 100 seconds duration and in one case of an event of more than 27 minutes. Analysis of both seizure duration and interseizure intervals demonstrated some subjects with unimodal populations of seizures, and others with multimodal in respect to both duration and interseizure interval. The subjects with unimodal populations of seizures had substantially better seizure prediction metrics. An additional analysis was performed in 2 of the 11 subjects, for whom there was enough data to attempt to explore a relation between seizure duration and subsequent interseizure interval. They found that short duration seizures were associated with shorter interseizure interval in both subjects, and, in particular, clinical and recognized seizures were followed by the longest seizure-free interval, and subclinical seizures were most likely to be followed by a short interval. The authors suggest that given the heterogeneity of their sample in all of these studies, it may be important to have future research examining seizures within individuals rather than across populations. The subjects with multimodal seizure durations and interseizure intervals were significantly more difficult to predict than the unimodal subjects. This is possibly due to having multiple different types of seizures, with distinct patterns of onset. Thus, designing seizure prediction algorithms for such individuals may require independent prediction of each type of seizure. This intensive research would be essential for seizure prediction efforts and may yield insights into different mechanisms of seizure generation and offset, which accounts for the different durations and frequencies observed. This research is in agreement with prior work examining 60 individuals with pharmacoresistant localization-related epilepsy, which looked at seizure intervals over several days of intracranial monitoring in an inpatient setting and demonstrated evidence of clustering in this population, with increased probability of additional seizure in the 30 minutes before and after a seizure, and demonstrated the dependence of the average conditional additional waiting time is directly proportional to the time already elapsed since the last event (30).

BRAIN–MACHINE INTERFACES AND NEUROPROSTHETICS Prosthetics meant to aid injured or missing limbs have grown more sophisticated over time. Traditionally, prosthetics have been controlled by muscle, either by using intact musculature in the back attached to pulleys that can then move the prosthetic mechanically or by using residual ­myoelectric

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potentials in the damaged limb to move the prosthesis. However, as the technology behind prostheses advances, newer models are capable of more degrees of freedom of movement. This increases the difficulty of designing sophisticated control mechanisms and has stimulated an interest in brain– machine interfaces. Early work focused on using scalp EEGs and attempted to tie prosthetic control to the mu rhythm, which is associated with planned motor activity. However, this is problematic due to the lack of spatial resolution in scalp recordings limiting fine motor control and the high maintenance of the scalp electrodes needed, as increasing artifact prevents use of the device. ECoG provides the advantages of higher spatial resolution and much higher signal-to-noise ratio (31). Preliminary studies are demonstrating tremendous potential in the spatial and temporal abilities of this technology. Investigators have been able to reliably decode independent ECoG patterns for movements including for moving individual fingers, varying timing of movements such as grasp, and moving in up to three dimensions. Some work has gone even further demonstrating the possibility for humans using the device to reliably control a computer cursor and grasp with a prosthetic limb, entirely through cortical activity detected via ECoG. There are many challenges ahead for this line of research. Currently, ECoG is typically limited to patients undergoing evaluation for epilepsy surgery or RNS, which is often not the best population to test brain–machine interfaces. The current CAIEEG devices are designed to have few contacts and much less computing power, which may be sufficient for their role in the management of epilepsy but might not be adequate for neural interfaces, which could control a prosthetic, computer, or other machine. Work is also ongoing to develop means for sensory feedback, such as proprioception or touch, which would be needed for higher level control of prosthetic limbs. The earlier research is extremely promising as a way to provide individuals with motor impairment, either from loss of limb or from spinal cord injury, with an ability to regain function, provided they have intact cerebral cortex, which can interact with an ECoG-based brain–machine interface. However, an intriguing new study suggests the applicability could be even broader. This preliminary study (32) demonstrated that chronic stroke patients with residual paralysis still had enough cortical activity that it was possible to decode hand movements with 61% accuracy. Coupling such brain activity of motor intention with a device physically moving the affected muscle and thus providing somatosensory feedback, presuming somatosensory systems were not part of the infarction, may facilitate rehabilitation or at least serve to restore functionality. One of these patients had a subcortical stroke, so it is perhaps not surprising that motor cortex can be used as part of a brain–machine interface in his case, but the remaining three subjects had cortical damage as well. Despite this degree of injury to the

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cortex, sufficient electrical activity existed for the authors to decode seven distinct hand movements in the subjects whose hands were paralyzed. This provides the potential for much more complex control of prosthetic devices than has previously been possible, with the opportunity for dramatic functional gains in these patients. Further work is expanding the possibilities of brain–machine interfaces, exploring means of using the interface to assist higher cortical function, as a means of communication, as a means to control a patient’s environment, and as a research tool on consciousness and neural coding. For reviews of these topics, see Schalk and Leuthardt (33) and Moxon and Foffani (34). ILLUSTRATIVE CASE We present a case of “Jane,” a right-handed young lady whose clinical course illustrates many of these concepts. Jane had first onset of seizures at age 17, when she presented with a minor car accident (she drove straight when the rode curved, suggestive of a seizure as the cause of the accident) with no head injury, and then had a cluster of seizures requiring ICU care. She had complex partial seizures with a prodrome of headache, decreased appetite, nausea, malaise, difficulty sleeping, and a sensation of her head “whizzing” and occasionally spoke gibberish during her seizures. She developed refractory epilepsy after this. When referred to Yale 2 years later, she was found to be positive for antibodies to voltage-gated potassium channels but had no evidence of active inflammation on cerebrospinal fluid (CSF) or MRI, and there was no improvement with a course of steroids. She was admitted to the epilepsy monitoring unit and had 19 seizures over 2 weeks, with independent right and left temporal ictal onsets, and interestingly, initially displayed more on the right but changed to the left during the course of the admission (Figure 9.5 demonstrates samples of each). The right temporal onset seizures had a “prodrome” lasting hours, including epigastric discomfort, which turned out to be due to a cluster of seizures in her sleep the prior night (postictal rather than preictal). Jane was not aware of the left temporal onset complex partial seizures. MRI suggested bilateral mesial temporal sclerosis, and PET demonstrated bilateral mesial temporal hypometabolism. Due to her bilateral foci, she had RNS implanted with bitemporal longitudinal (occipital implant site) depth electrodes. Over subsequent years, her RNS recordings have demonstrated many patterns that correspond to group trends in the earlier studies. She has had great variability in seizure frequency over time but with an overall trend to decreasing seizure frequency over time with RNS (Figure 9.6). The laterality of onset has varied at different periods, once again demonstrating that a short period of monitoring can be substantially misleading (Figure 9.7). Although overall seizures were fairly equally distributed between left and right, Jane had abundant periodic epileptiform

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discharges on the right at ~1 Hz (for the majority of the time) and not on the left. She was therefore reconsidered for possible resection. During Wada testing, injection of the right hemisphere did not affect her language in French or English, and her memory remained perfect, while injection of the left hemisphere resulted in aphasia and poor memory. She therefore was offered (A)

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a palliative right temporal resection. After surgery and with continued RNS on the left, she has had no further right-sided seizures or spikes. She initially continued to have clusters of seizures from the left; she was unaware of these until she had full days with marked amnesia. We programmed the device to provide a warning when she was c­ lustering (we turned up the

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stimulation with longer episodes so that she would feel an eye twitch when she was having long episodes); this enabled her to take ­additional medications and abort clusters, preventing the days with amnesia. With this aggressive treatment, despite her r­ efractory ­epilepsy she was able to get a BS in her final year of college and graduate with a dual degree. Seizures continued to improve with no specific changes to her s­ timulator. She lived independently in France for several years after college, with only one seizure per month on the RNS device and only a few clinical seizures per year. She needed the cluster protocol only once in the past 2.5 years. CONCLUSION CAIEEG is an exciting new technology with vast potential. It provides the advantages of longitudinal monitoring in an environment free of acute confounding factors with the high sensitivity and spatial resolution and minimal artifact of intracranial EEG recordings. Current devices are beginning to make a clinical impact, particularly in the surgical workup of people with epilepsy and the burgeoning field of RNS. Longitudinal research studies will deepen our knowledge of the electrical activity of the brain, and these new insights may drive future areas of research. The scientific community continues to work to overcome the current obstacles of limited electrode arrays, device storage capabilities, and practical surgical considerations. The future for CAIEEG technology may prove to be truly revolutionary in medicine. The development of algorithms to successfully predict seizures and take corrective measures prior to any clinical events could dramatically change the management of epilepsy. Successful brain– machine interfaces could revolutionize the field of neurorehabilitation and prosthetics. This field is slowly moving from Hollywood movies to reality and has nearly unlimited potential. REFERENCES 1. Ritaccio A, Brunner P, Gunduz A, et al. Proceedings of the Fifth International Workshop on advances in electrocorticography. Epilepsy Behav. 2014;41:183–192. 2. Asano E, Juhasz C, Shah A, Sood S, Chugani HT. Role of subdural electrocorticography in prediction of long-term seizure outcome in epilepsy surgery. Brain. 2009;132(Pt 4):1038–1047. 3. Jacobs J, Zijlmans M, Zelmann R, et al. High-frequency electroencephalographic oscillations correlate with outcome of epilepsy surgery. Ann Neurol. 2010;67(2):209–220. 4. Wu JY, Sankar R, Lerner JT, Matsumoto JH, Vinters HV, Mathern GW. Removing interictal fast ripples on electrocorticography linked with seizure freedom in children. Neurology. 2010;75(19):1686–1694.

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5. Wong CH, Birkett J, Byth K, et al. Risk factors for complications during ­intracranial electrode recording in presurgical evaluation of drug resistant partial epilepsy. Acta Neurochir (Wien). 2009;151(1):37–50. 6. Hamer HM, Morris HH, Mascha EJ, et al. Complications of invasive video-EEG monitoring with subdural grid electrodes. Neurology. 2002;58(1):97–103. 7. Fisher RS, Velasco AL. Electrical brain stimulation for epilepsy. Nat Rev Neurol. 2014;10(5):261–270. 8. Morrell MJ. Responsive cortical stimulation for the treatment of medically intractable partial epilepsy. Neurology. 2011;77(13):1295–1304. 9. Bergey GK, Morrell MJ, Mizrahi EM, et al. Long-term treatment with responsive brain stimulation in adults with refractory partial seizures. Neurology. 2015;84(8):810–817. 10. Quigg M, Sun F, Fountain NB, et al. Interrater reliability in interpretation of electrocorticographic seizure detections of the responsive neurostimulator. Epilepsia. 2015;56(6):968–971. 11. Gaspard N. ACNS critical care EEG terminology: value, limitations, and perspectives. J Clin Neurophysiol. 2015;32(6):452–455. 12. Yen DJ, Chen C, Shih YH, et al. Antiepileptic drug withdrawal in patients with temporal lobe epilepsy undergoing presurgical video-EEG monitoring. Epilepsia. 2001;42(2):251–255. 13. Engel J Jr., Crandall PH. Falsely localizing ictal onsets with depth EEG telemetry during anticonvulsant withdrawal. Epilepsia. 1983;24(3):344–355. 14. Marks DA, Katz A, Scheyer R, Spencer SS. Clinical and electrographic effects of acute anticonvulsant withdrawal in epileptic patients. Neurology. 1991;41(4):508–512. 15. Zhou D, Wang Y, Hopp P, et al. Influence on ictal seizure semiology of rapid withdrawal of carbamazepine and valproate in monotherapy. Epilepsia. 2002;43(4):386–393. 16. Rizvi SA, Hernandez-Ronquillo L, Wu A, Tellez Zenteno JF. Is rapid withdrawal of anti-epileptic drug therapy during video EEG monitoring safe and efficacious? Epilepsy Res. 2014;108(4):755–764. 17. Jain SV, Kothare SV. Sleep and epilepsy. Semin Pediatr Neurol. 2015;22(2):86–92. 18. Duckrow RB, Tcheng TK. Daily variation in an intracranial EEG feature in humans detected by a responsive neurostimulator system. Epilepsia. 2007;48(8):1614–1620. 19. Anderson CT, Tcheng TK, Sun FT, Morrell MJ. Day-night patterns of epileptiform activity in 65 patients with long-term ambulatory electrocorticography. J Clin Neurophysiol. 2015;32(5):406–412. 20. Smart O, Rolston JD, Epstein CM, Gross RE. Hippocampal seizure-onset laterality can change over long timescales: a same-patient observation over 500 days. Epilepsy Behav Case Rep. 2013;1:56–61. 21. King-Stephens D, Mirro E, Weber PB, et al. Lateralization of mesial temporal lobe epilepsy with chronic ambulatory electrocorticography. Epilepsia. 2015;56(6):959–967. 22. DiLorenzo DJ, Mangubat EZ, Rossi MA, Byrne RW. Chronic unlimited recording electrocorticography-guided resective epilepsy surgery: technology-enabled enhanced fidelity in seizure focus localization with improved surgical efficacy. J Neurosurg. 2014;120(6):1402–1414.

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23. Enatsu R, Alexopoulos A, Bingaman W, Nair D. Complementary effect of ­surgical resection and responsive brain stimulation in the treatment of bitemporal lobe epilepsy: a case report. Epilepsy Behav. 2012;24(4):513–516. 24. Cook MJ, O’Brien TJ, Berkovic SF, et al. Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study. Lancet Neurol. 2013;12(6):563–571. 25. Detyniecki K, Blumenfeld H. Consciousness of seizures and consciousness during seizures: are they related? Epilepsy Behav. 2014;30:6–9. 26. Nourski KV, Howard MA 3rd. Invasive recordings in the human auditory cortex. Handb Clin Neurol. 2015;129:225–244. 27. Davis KA, Ung H, Wulsin D, et al. Mining continuous intracranial EEG in focal canine epilepsy: relating interictal bursts to seizure onsets. Epilepsia. 2016;57(1):89–98. 28. Cook MJ, Varsavsky A, Himes D, et al. The dynamics of the epileptic brain reveal long-memory processes. Front Neurol. 2014;5:217. 29. Cook MJ, Karoly PJ, Freestone DR, et al. Human focal seizures are characterized by populations of fixed duration and interval. Epilepsia. March 2016;57(3):359–368. 30. Osorio I, Frei MG, Sornette D, Milton J. Pharmaco-resistant seizures: selftriggering capacity, scale-free properties and predictability? Eur J Neurosci. 2009;30(8):1554–1558. 31. Thakor NV, Fifer MS, Hotson G, et al. Neuroprosthetic limb control with electrocorticography: approaches and challenges. Conf Proc IEEE Eng Med Biol Soc. 2014;2014:5212–5215. 32. Spuler M, Walter A, Ramos-Murguialday A, et al. Decoding of motor intentions from epidural ECoG recordings in severely paralyzed chronic stroke patients. J Neural Eng. 2014;11(6):066008. 33. Schalk G, Leuthardt EC. Brain-computer interfaces using electrocorticographic signals. IEEE Rev Biomed Eng. 2011;4:140–154. 34. Moxon KA, Foffani G. Brain-machine interfaces beyond neuroprosthetics. Neuron. 2015;86(1):55–67.

CHAPTER 10

FUTURE DIRECTIONS IN AMBULATORY EEG STEVEN C. SCHACHTER, MD

INTRODUCTION There are numerous current uses for ambulatory EEG (aEEG) monitoring as detailed in other chapters, including confirmation of a clinical suspicion of epilepsy, documentation of seizure type(s) and frequency by recording in an environment where a patient has the events of interest, and screening for possible nonepileptic seizures, including movement disorders and psychogenic nonepileptic seizures (1,2). With advances in aEEG monitoring equipment, technical issues ­producing an uninterpretable recording have been reduced. Nonetheless, fixing technical problems that arise in the field and identifying the nature and source(s) of artifacts and their differentiation from electrographic seizures are still problematic. In addition, assessment of very infrequent events is not possible with current technology, tapering antiseizure drugs (ASDs) to evoke seizures presents safety risks, and patients or family members may not maintain the seizure diary as completely and accurately as they do in the epilepsy monitoring unit (EMU). Another disadvantage is that certain seizure types may not be reliably detected or distinguished with use of only EEG, but even when video recording is used in conjunction with EEG, patients may be off camera or under bed sheets during events, thereby limiting the diagnostic value of the video component (3). Unmet Needs in Monitoring Patients With Epilepsy Given these and other limitations, aEEG technology is primarily used for short-term monitoring to facilitate diagnosis. Yet, seizures are unpredictable and have associated risks of fractures, intracranial hematomas, burns, accidents, aspiration, drowning, and sudden unexpected death (SUDEP); 179

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high  rates of drug-resistant seizures continue despite availability of new ­ASDs and cognitive and other issues negatively impact on treatment adherence. Therefore, the quality of life (QOL) of patients with epilepsy as well as the ability of physicians to care for patients and for patients to be selfempowered could substantially be enhanced by new systems that incorporate EEG and/or other modalities that could reliably and consistently: • Provide patients or caregivers with a warning of an impending ­debilitating seizure with sufficient time to protect the patient’s safety • Couple seizure detection to a therapeutic intervention (closed-loop) • Enhance safety and lessen risk of injuries during and after a seizure • Summon help or emergency evaluation when needed but not otherwise • Identify and monitor biomarker(s) for SUDEP • Measure the impact of treatments on seizure control and comorbidities • Improve adherence to medication and lifestyle issues (eg, sleep) • Assess quality and adequacy of sleep and comorbidities, such as anxiety and depression • Correlate specific symptoms with EEG, ASD levels, and other physiological signals in close to real time • Supplement patient diaries with objective records of seizure type, ­frequency, and severity for use in the clinic and during clinical trials of antiseizure therapies Given these opportunities, aEEG monitoring is evolving from EEG- and video-based devices for short-term diagnostic use to long-term comprehensive epilepsy management systems incorporating a range of technologies to improve the QOL of persons with epilepsy. Many such enabling technologies, whether based on EEG or other modalities, are emerging either de novo for use in epilepsy monitoring or by adapting technologies that were ­initially developed for other applications. A representative sample of the technologies, especially those that have been presented in public forums (eg, the Epilepsy Foundation Pipeline Update Conference (4)), is presented in the following section. Technical discussions related to development of seizure detection/prediction algorithms and other engineering design aspects are beyond the scope of this chapter. EMERGING TECHNOLOGIES AND POTENTIAL APPLICATIONS Epilepsy monitoring technologies may be categorized by whether they are EEG- or non-EEG-based, and by whether their intended use is primarily for ambulatory seizure detection/prediction (see (5–7) for reviews) and therefore to facilitate therapy (8) or prevent injuries/death or to self-empower persons to live with their epilepsy. The rapid pace of technological development, for example, compared to drug development, suggests that the

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availability, capabilities, and features of systems described in the following will likely change over time. However, the unmet needs they seek to address will remain until practical, affordable, and reliable solutions are developed, tested, validated, and implemented. Ambulatory Seizure Detection/Prediction Systems EEG-Based Systems: Extracranial Many scalp-EEG electrode systems have been designed for gaming or for brain–computer interfaces and serve a variety of uses from entertainment to research to cognitive remediation (9–11). However, they are generally not practical for chronic epilepsy monitoring because they are too conspicuous and prone to slippage, noise, and other artifacts, especially due to poor electrode–scalp conductivity, which has prompted efforts to identify adhesives that work well for a watertight electrode–scalp interface (4,12). Therefore, electrodes that are inconspicuous, easy to apply, and less subject to slippage on the scalp are preferable. For example, Epitel, Inc. is developing a wireless, waterproof, disposable EEG patch for scalp placement to document occurrence and duration of seizures using flexible circuit boards and two gold electrodes that are positioned based on a patient’s prior EEG findings (4). The device is covered by flexible urethane and once applied to the scalp starts to log and transmit EEG data immediately and for a period of 3 days, though with further development the recording time will be extended to 7 days. Multiple devices could be placed on the scalp if desired. Other groups are developing wireless electrodes that would be inserted under the scalp using small incisions and therefore be invisible to others, including one system that is designed to record and store EEG using a behind-the-ear platform (Figure 10.1) (13). EEG-Based Systems: Intracranial Intracranial EEG-based systems for chronic seizure monitoring, while more invasive than extracranial systems, are further ahead in development. The Responsive Neurostimulation System™ (Neuropace, Inc.) delivers closedloop stimulation in response to detected epileptiform activity and received Food and Drug Administration (FDA) approval for use in adults with ­partial-onset seizures not controlled with greater than or equal to 2 ASDs. The device consists of a cranially implanted, programmable, battery powered, ­microprocessor-controlled neurostimulator that is connected to depth and/or subdural strip leads. The system appears to be well tolerated and self-reported median seizure reductions at 1 and 2 years were 44% and 53%, respectively (14). The Seizure Advisory System (originally developed by NeuroVista) utilizes intracranial electrodes to transmit ongoing EEG to a subclavicular, implanted

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FIGURE 10.1  Schematic of subscalp electrode system.

telemetry unit. Data are then sent wirelessly to an external, handheld personal ­advisory device that uses a patient-specific seizure prediction algorithm to display indicator lights for seizure likelihood—blue (low), white (moderate), or red (high). A published study of 15 ambulatory patients with 2 to 12 disabling partial-onset seizures per month showed that intracranial EEG monitoring for seizure prediction is feasible (15). The enrolled patients entered a data ­collection phase, during which an algorithm for identification of periods of high, moderate, and low seizure likelihood was established for each patient. Eleven out of 15 patients had likelihood performance estimate sensitivities ranging from 65% to 100% and entered an advisory phase. Clinical ­effectiveness measures remained ­stable between baseline and 4 months after implantation. Non-EEG-Based Systems: Accelerometers Non-EEG-based seizure detection systems take advantage of changes in physiological signals other than EEG that stereotypically change with seizures, such as limb movements in generalized tonic–clonic seizures (GTCSs). Accelerometers detect changes in velocity and direction, and some are designed to determine movement in the x, y, and z planes. The SmartWatchTM (Smart Monitor) consists of a Global Positioning System (GPS) module and a proprietary accelerometer/gyroscopic sensor to detect the excessive and repeated motions of GTCSs, in addition to keeping time (4). Algorithms continuously monitor and analyze wrist motion to detect repetitive shaking movements from a GTCS and then automatically send a text message and/ or phone call to caregivers or other designated alert recipients, providing the

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location of the patient based on GPS. A button on the watch can be pressed by the patient in case of an emergency, if they feel that they are about to have a seizure, when a non-GTCS (such as a complex partial seizure) has occurred, when medication was taken, or in the event of a false detection. The SmartWatch also provides medication reminders, analyzes sleep duration and quality, records audio during seizure episodes, and provides analytics and reporting/seizure tracking for physicians, including seizure duration, severity (amplitude and frequency of the shaking ­movements), frequency, time of occurrence, and associated audio. In one published study of the SmartWatch, patients aged 6 to 68 years were monitored in an EMU for 17 to 171 hours (total of 4,878 hours) (16). Thirty-nine GTCSs were recorded in 20 patients, of which the device detected 35 (89.7%). In 16 patients, all seizures were detected, while in three other patients, more than two thirds of seizures were detected. Mean detection latency measured from the start of the focal seizure preceding the secondarily GTCS was 55 seconds. The rate of false alarms was 0.2/day. A number of other studies are in progress in children and adults and to integrate output from the device with an online seizure diary. Submission to the FDA for 510(k) clearance is planned. Other wrist-worn devices, including the Epi-Care Free device (Danish Care  Technology ApS) (6) and the Apple Watch (http://www.­ hopkins medicine.org/epiwatch/#.VxOyY_krJD8), are undergoing testing as ­seizure ­detection platforms. Non-EEG-Based Systems: Electrodermal Activity Sensor EmbraceTM (Empatica, Inc.) is a wrist-worn device that measures motion, ­temperature, pulse, and electrodermal activity (EDA), a marker of sympathetic tone (4). A 90-patient study showed that EDA was elevated more than two standard deviations above preictal levels in 100% of GTCSs and in 86% of focal seizures with impaired consciousness (complex partial seizures) (17), findings that were supported by another small study (18). Combining EDA with motion sensing increased the accuracy in detecting GTCSs compared with motion sensing alone (19). The magnitude of the ictal EDA change in association with complex partial seizures and GTCSs correlates significantly and positively with the duration of postictal generalized EEG suppression (17), an EEG biomarker that has been associated with SUDEP (20). Non-EEG-Based Systems: ECG Monitoring Seizure detection systems using ECG sensors are based on the frequent occurrence of ictal tachycardia (21) and may also be designed to detect life-threatening ictal or interictal arrhythmias or ECG morphologies

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that may put patients at risk for sudden cardiac death (22). For example, the AspireSR® generator for VNS Therapy (LivaNova, PLC), which has received CE Mark and FDA approval, triggers vagus nerve stimulation (VNS) based on algorithmic detection of tachycardia. A technologically related, externally worn, ECG monitor called Proguardian REST™ (LivaNova, PLC) is under development to monitor, detect, and log nighttime seizure activity as manifested by ictal changes in heart rate and bodily movement with algorithms that also generate audible and visual ­notifications for caregivers (4). Sensitivity settings for both heart rate changes and movement detection can be adjusted by the end user. The sensor is a flexible, single-use patch that is applied to the chest, after which the sensor automatically begins to monitor heart rate and establish a communication link with a hub within 30 feet. If the caregiver is farther away than 30 feet, an optional Android smartphone app will send notifications throughout a wi-fi-enabled home as an alert. Another system called esap (RTI International) detects seizures based on ECG, movement, and respiration (4). An initial study of 28 children using postprocessing analysis demonstrated high detection accuracy for generalized seizure types and 50% detection for focal seizures. The current prototype consists of a sensor (the Zephyr BiopatchTM) worn on the chest that communicates via Bluetooth to a processing module. An adaptive, patientspecific algorithm that “learns” from performance accuracy will eventually be integrated into the sensor to send an alarm locally or remotely to activate emergency medical systems.

Non-EEG-Based Systems: Skin Sensors The  Brain Sentinel® Seizure Monitoring  and Alerting System (Brain Sentinel, Inc.) detects convulsive seizures using an algorithm that monitors a surface electromyography EMG recording of the biceps muscle to detect patterns of muscular contractions consistent with seizures such as GTCSs (4). The EMG sensor communicates via wi-fi with a base station, which in turn alerts nearby caregivers when a seizure is detected. In addition, a microphone records s­ eizure-related sounds. Alerts can also be sent via phone, text, or e-mail to caregivers or to summon emergency services. All EMG recordings, including ictal EMG, are saved and stored in near real time in the cloud to assist the patient’s clinician in the determination of ­seizure semiology (eg, tonic, clonic, or tonic–clonic) and hence potentially in the selection of therapy. In February 2017, Brain Sentinel, Inc. received US Food and Drug Administration (FDA) de novo clearance to begin marketing the SPEAC® System, the Brain Sentinel® Seizure Monitoring and Alerting System. The adjunctive seizure monitoring system is

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indicated for adults at home or in health care facilities during periods of rest. Similarly, MC10, Inc. is developing “Biostamp,” a wearable skin patch with sensors that detect motion and muscle activation and which could also feasibly monitor temperature, respiration, and heart rate (4). Data are ­transmitted to a smartphone with an option for onboard memory. Non-EEG-Based Systems: Miscellaneous Several other technologies are being explored for their utility in detecting seizures, including near-infrared spectroscopy (23,24); a system combining video, accelerometry, and radar-induced activity recording (25); and wearable body sensor networks (26); while a mattress movement monitor (27) and SAMi, a video-based system that analyzes movements during sleep, are commercially available (4). As examples of the common occurrence of persons personally impacted by epilepsy to develop their own seizure monitoring technology, a computer science engineer and his 13-year-old son designed a smartphone app called Seizario that leverages the built-in smartphone accelerometer to detect seizures and communicate the seizure detection to others (4), as did another father of a child with epilepsy (www.epdetect.com). Treatment, Quality of Life, and Safety Monitors Aside from seizure detection, technologies to monitor drug treatment and related side effects and to protect safety in the event of a seizure are advancing forward. For example, point-of-care devices in the home or doctor’s office for measuring concentrations of ASDs from capillary blood (finger stick) and saliva are undergoing testing (4). Three of the most commonly used online epilepsy diaries—My Epilepsy Diary, Seizuretracker.com, and Patients Like Me—help patients provide QOL information to their health care practitioners and to better self-manage their epilepsy, as do a number of other electronic seizure record applications, which can be used at any portal able to access the worldwide web (6). Seizure-related injuries may be mitigated by technology even if seizures cannot be fully controlled. Shower Power is a safety monitoring system for persons taking a shower that maintains an individual’s privacy (4). The inventor’s daughter was diagnosed with epilepsy after her first seizure, which occurred in the shower. The system discreetly monitors the person while in the shower for upright posture. When an upright showering posture is not maintained, the device automatically shuts off the water and ­triggers a local alarm, sending an alert via a smartphone.

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OPPORTUNITIES AND CHALLENGES FOR FURTHER DEVELOPMENT Designing epilepsy monitoring systems generally requires ­multidisciplinary and multi-institutional collaborations that bridge medicine, technology, engineering, and business. However, the process of translating new ideas to systems that improve patient care is complex, long, and risky. Academic-based clinicians and people living with epilepsy are fertile ­ sources of innovative ideas and are closest to the unmet needs. Yet, medical research and engineering development have traditionally been done in silos defined by individual laboratories with minimal emphasis or value placed on multidisciplinary, multi-institutional collaborations, and patient input. Further, academic-based clinicians and engineers or persons impacted by epilepsy are typically not experts in the process of commercialization, especially in the current highly competitive and constantly evolving business and regulatory environments. While the impetus to development may be an existing technology for which a health care–related problem is sought, a process called “technology push,” experience has shown this approach is less successful for improving patient care than starting with a well-defined unmet medical need and then identifying the appropriate technology for the solution (“clinical pull”) (28). A final significant challenge is that early stage, academic-based multidisciplinary translational projects such as those discussed in this chapter have little chance of receiving funding for scientific validation from conventional sources. Many epilepsy monitoring systems are being developed as consumer products rather than FDA-approved products. This should be kept in mind by both health care providers and consumers, especially when considering an ambulatory monitoring system for seizures. Epilepsy poses a potentially life-threatening situation, such as the risk of SUDEP and since the level of premarketing testing may not be sufficient to fully validate a specific system’s performance characteristics, battery life, reliability, and appropriateness for specific clinical situations and for specific patients, physicians should counsel patients appropriately with regard to their safety.

CONCLUSION Technologies are emerging that will broaden the capabilities and ­applications of seizure monitoring toward comprehensive, patient-specific ambulatory epilepsy management systems. While detection of seizure types other than GTCSs is generally unreliable at present (5), technologies will rapidly develop to detect other seizure types. Successful systems will likely be those that solve unmet needs and that are feasible, comfortable, socially and cosmetically acceptable, accurate, reliable, and patient-friendly. They

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must also be sufficiently tested to allow health care providers to confidently ­recommend them to their patients. Current trends suggest that systems will eventually require integration of multiple modalities, perhaps using different proprietary technologies, in order for clinicians to optimize a specific suite of sensors for individual patients. In the future, ambulatory monitoring systems may also help in the ­diagnosis and treatment of both humans and domesticated animals with epilepsy (29–31), to identify patients at substantial risk for developing epilepsy (eg, patients with traumatic brain injury, stroke, and Alzheimer’s ­disease), to monitor autonomic measures of emotional stress (32), and to detect drowsiness and improve alertness in persons whose alertness is essential to the safety of others (such as industrial workers, commercial drivers, and pilots) (33,34). While recent and future advances have the potential to transform ­epilepsy monitoring and the lives of persons with epilepsy outside the hospital, cautious and thoughtful evaluation is needed to ensure they provide maximal value. To this end, one group recommended that ambulatory seizure detection systems be evaluated with particular emphasis on QOL, comfort, privacy of patients, and psychological impact on self-image and false alarms on the user experience (5). These and other challenges can be solved by the engineering, medical, and patient communities working together from the earliest possible stages of system design and development. The involvement of patients is particularly important in the design of devices for chronic use. One study of patients’ views on the relevance, performance requirements, and implementation of seizure prediction devices, for example, showed that few patients were willing to wear EEG electrodes for signal acquisition on a long-term basis (35). Ultimately, demonstrations of positive clinical impact with decreased direct medical costs will be needed for systems to gain widespread adoption and third-party payer coverage.

REFERENCES 1. Lawley A, Evans S, Manfredonia F, et al. The role of outpatient ambulatory ­electroencephalography in the diagnosis and management of adults with epilepsy or nonepileptic attack disorder: a systematic literature review. Epilepsy Behav. 2015;53:26–30. 2. Michel V, Mazzola L, Lemesle M, et al. Long-term EEG in adults: sleep-deprived EEG (SDE), ambulatory EEG (Amb-EEG) and long-term video EEG recording (LTVER). Clin Neurophysiol. 2015;45(1):47–64. 3. Goodwin E, Kandler RH, Alix JJ. The value of home video with ambulatory EEG: a prospective service review. Seizure. 2014;23(6):480–482. 4. French J, Schachter SC, Sirven J, et al. The Epilepsy Foundation’s 4th biennial epilepsy pipeline update conference. Epilepsy Behav. 2015;46:34–50.

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5. Van Andel J, Thijs RD, de Weerd A, et al. Non-EEG based ambulatory seizure detection designed for home use: what is available and how will it influence epilepsy care? Epilepsy Behav. 2016;57(pt A):82–89. 6. Ramgopal S, Thome-Souza S, Jackson M, et al. Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy. Epilepsy Behav. 2014;37:291–307. 7. Van de Vel A, Cuppens K, Bonroy B, et al. Non-EEG seizure-detection systems and potential SUDEP prevention: state of the art. Seizure. 2013;22(5):345–355. 8. Duun-Henriksen J, Madsen RE, Remvig LS, et al. Automatic detection of childhood absence epilepsy seizures: toward a monitoring device. Pediatr Neurol. 2012;46(5):287–292. 9. Mihajlovic V, Grundlehner B, Vullers R, et al. Wearable, wireless EEG solutions in daily life applications: what are we missing? IEEE J Biomed Health Inform. 2015;19(1):6–21. 10. Balanou E, van Gils M, Vanhala T. State-of-the-art of wearable EEG for personalized health applications. Stud Health Technol Inform. 2013;189:119–124. 11. Arns M, de Ridder S, Strehl U, et al. Efficacy of neurofeedback treatment in ADHD: the effects on inattention, impulsivity and hyperactivity: a meta-­ analysis. Clin EEG Neurosci. 2009;40(3):180–189. 12. Lee SM, Kim JH, Byeon HJ, et al. A capacitive, biocompatible and adhesive electrode for long-term and cap-free monitoring of EEG signals. J Neural Eng. 2013;10(3):036006. 13. McLaughlin BL, Mariano LJ, Prakash SR, et al. An electroencephalographic recording platform for real-time seizure detection. Conf Proc IEEE Eng Med Biol Soc. 2012;2012:875–878. 14. Heck CN, King-Stephens D, Massey AD, et al. Two-year seizure reduction in adults with medically intractable partial onset epilepsy treated with responsive neurostimulation: final results of the RNS System Pivotal trial. Epilepsia. 2014;55(3):432–441. 15. Cook MJ, O’Brien TJ, Berkovic SF, et al. Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study. Lancet Neurol. 2013;12(6):563–571. 16. Beniczky S, Polster T, Ljaer TW, et al. Detection of generalized tonic-clonic seizures by a wireless wrist accelerometer: a prospective, multicenter study. Epilepsia. 2013;54:e58–e61. 17. Poh MZ, Loddenkemper T, Reinsberger C, et al. Autonomic changes with seizures correlate with postictal EEG suppression. Neurology. 2012;78(23):1868–1876. 18. Heldberg BE, Kautz T, Leutheuser H, et al. Using wearable sensors for ­semiology-independent seizure detection—towards ambulatory monitoring of ­epilepsy. Conf Proc IEEE Eng Med Biol Soc. 2015;2015:5593–5596. 19. Poh MZ, Loddenkemper T, Reinsberger C, et al. Convulsive seizure detection using a wrist-worn electrodermal activity and accelerometry biosensor. Epilepsia. 2012;53(5):e93–e97. 20. Ryvlin P, Nashef L, Lhatoo SD, et al. Incidence and mechanisms of cardiorespiratory arrests in epilepsy monitoring units (MORTEMUS): a retrospective study. Lancet Neurol. 2013; 12(10):966–977. 21. Sevcencu C, Struijk JJ. Autonomic alterations and cardiac changes in epilepsy. Epilepsia. 2010;51(5):725–737.

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22. Schomer AC, Nearing BD, Schachter SC, et al. Vagus nerve stimulation reduces cardiac electrical instability assessed by quantitative T-wave alternans ­analysis in patients with drug-resistant focal epilepsy. Epilepsia. 2014;55(12):1996–2002. 23. Peng K, Pouliot P, Lesage F, et al. Multichannel continuous electroencephalography-functional near-infrared spectroscopy recording  of ­ focal seizures and interictal epileptiform discharges in human epilepsy: a review. Neurophotonics. 2016;3(3):031402. doi:10.1117/1.NPh.3.3.031402. 24. Safaie J, Grebe R, Abrishami Moghaddam H, et al. Toward a fully integrated wireless wearable EEG-NIRS bimodal acquisition system. J Neural Eng. 2013;10(5):056001. 25. Van de Vel A, Milosevic M, Bonroy B, et al. Long-term accelerometry-triggered video monitoring and detection of tonic-clonic and clonic seizures in a home environment: pilot study. Epilepsy Behav Case Rep. 2016;5:66–71. 26. Dalton A, Patel S, Chowdhury AR, et al. Development of a body sensor network to detect motor patterns of epileptic seizures. IEEE Trans Biomed Eng. 2012;59(11):3204–3211. 27. Narechania AP, Garic´ II, Sen-Gupta I, et al. Assessment of a quasi-piezoelectric mattress monitor as a detection system for generalized convulsions. Epilepsy Behav. 2013; 28(2):172–176. 28. Schachter SC, Collins J, Dempsey MK, et al. Deep innovation in the medical domain a la Boston’s CIMIT: institutional case study. Venture Findings. 2016;3:21–30. 29. Uriarte A, Maestro Saiz I. Canine versus human epilepsy: are we up to date? J Small Anim Pract. 2016;57(3):115–121. 30. Wijnberg ID, van der Ree M, van Someren P. The applicability of ambulatory electroencephalography (aEEG) in healthy horses and horses with abnormal behavior or clinical signs of epilepsy. Vet Q. 2013;33(3):121–131. 31. Davis KA, Sturges BK, Vite CH, et al. A novel implanted device to wirelessly record and analyze continuous intracranial canine EEG. Epilepsy Res. 2011;96(1-2):116–122. 32. Roh T, Bong K, Hong S, et al. Wearable mental-health monitoring platform with independent component analysis and nonlinear chaotic analysis. Conf Proc IEEE Eng Med Biol Soc. 2012;2012:4541–4544. 33. Arnin J, Anopas D, Horapong M, et al. Wireless-based portable EEG-EOG monitoring for real time drowsiness detection. Conf Proc IEEE Eng Med Biol Soc. 2013;2013:4977–4980. 34. Liu NH, Chiang CY, Hsu HM. Improving driver alertness through music selection using a mobile EEG to detect brainwaves. Sensors (Basel). 2013;13(7):8199–8221. 35. Schulze-Bonhage A, Sales F, Wagner K, et al. Views of patients with epilepsy on seizure prediction devices. Epilepsy Behav. 2010;18(4):388–396.

CHAPTER 11

REIMBURSEMENT ISSUES IN AMBULATORY EEG MARC R. NUWER, MD, PhD

Health care public policy now uses coding to communicate clearly the nature of services and diagnoses. In these days of digital processing, big databases, and automated processes these formal coding systems aid in many ways. Insurance carriers use coding to check whether to pay for services, and then to pay promptly without human review. Research teams evaluate outcomes using the large databases with coded patient care information. Government and hospital policies are tuned to screen for proper services using coding. Altogether, systematic coding of services and diagnoses are here to stay. All these uses depend on physicians and institutional coders entering ­diagnoses and procedural coding correctly in the first place. DIAGNOSTIC CODING Diagnostic coding in the United States uses the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM). The World Health Organization (WHO) adopted the ICD-10 in the early 1990s. More than 100 countries use it in some form. The U.S. Centers for Disease Control and Prevention’s National Center for Health Statistics converted to it in its Clinical Modification (CM) in 2014 (1). Epilepsy, defined by the International League Against Epilepsy (ILAE) as recurrent unprovoked seizures more than 24 hours apart or a first-time unprovoked seizure with a high likelihood of recurrence, is subdivided into several categories in ICD-10-CM. Tables 11.1 and 11.2 show many ICD-10-CM codes for epilepsy and seizures. This WHO system is not always consistent with or easily harmonized with the most recent definitions and classifications promulgated by the ILAE (2,3). Symptomatic refers to presence of an etiologic basis of a focal seizure onset. Otherwise, ­ICD-10-CM considers the epilepsy to be idiopathic. This is so even though modern genetics and neuroscience reveal specific 191

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TABLE 11.1  International Classification of Diseases, Tenth Revision, Clinical Modification Epilepsy and Seizure-Related Codes Diseases of the Nervous system G40 Epilepsy and recurrent seizures EXCLUDES: conversion disorder with seizures (F44.5), convulsions NOS (R56.9), hippocampal sclerosis (G93.81), mesial temporal sclerosis (G93.81), post traumatic seizures (R56.1), seizure (convulsive) NOS (R56.9), seizure of newborn (P90), temporal sclerosis (G93.81), Todd’s paralysis (G83.8)  USE: x = 01 for “not intractable, with status epilepticus”      x = 09 for “not intractable, without status epilepticus”      x = 11 for “intractable, with status epilepticus”      x = 19 for “intractable, without status epilepticus”  USE: z = 1 for “not intractable, with status epilepticus”      z = 2 for “not intractable, without status epilepticus”      z = 3 for “intractable, with status epilepticus”      z = 4 for “intractable, without status epilepticus”  G40.0x Localization-related (focal) (partial) idiopathic epilepsy and epileptic syndromes with seizures of localized onset      Examples: Benign childhood epilepsy with centrotemporal EEG spikes, childhood epilepsy with occipital EEG paroxysms      Excludes: adult onset localization-related epilepsy (G40.1-, G40.2-)  G40.1x Localization-related (focal) (partial) symptomatic epilepsy and epileptic syndromes with simple partial seizures      Examples: Attacks without alteration of consciousness, epilepsia partialis continua, simple partial seizures developing into secondarily generalized seizures  G40.2x Localization-related (focal) (partial) symptomatic epilepsy and epileptic syndromes with complex partial seizures      Examples: Attacks with alteration of consciousness, often with automatisms; complex partial seizures developing into secondarily generalized seizures  G40.3x Generalized idiopathic epilepsy and epileptic syndromes      Code also MERRF syndrome, if applicable (E88.42)  G40.Ax Absence epilepsy syndrome      Examples: Childhood absence epilepsy, juvenile absence epilepsy, absence epileptic syndrome  G40.Bx Juvenile myoclonic epilepsy (continued )

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TABLE 11.1  International Classification of Diseases, Tenth Revision, Clinical Modification Epilepsy and Seizure-Related Codes (continued ) Diseases of the Nervous system  G40.4x Other generalized epilepsy and epileptic syndromes      Examples: Epilepsy with grand mal, myoclonic, atonic, clonic, or tonic seizures or myoclonic encephalopathy  G40.5x Epileptic seizures related to external causes      Examples: Seizures related to alcohol, drugs, hormonal changes, sleep deprivation, or stress      Code also, if applicable, associated epilepsy and recurrent seizures (G40.-)      Use additional code for adverse effect, if applicable, to identify drug (T36-T50 with fifth or sixth character 5)   G40.8 Other epilepsy and recurrent seizures      Examples: Epilepsies and epileptic syndromes undetermined as to whether they are focal or generalized, Landau–Kleffner syndrome      G40.80z Other epilepsy      G40.81z Lennox–Gastaut syndrome      G40.82z Epileptic spasms      G40.89 Other seizures      Excludes: post traumatic seizures (R56.1), recurrent seizures NOS (G40.909), seizure NOS (R56.9)  G40.9x Epilepsy, unspecified      Examples: Unspecified Epilepsy, Epileptic convulsions, Epileptic fits, Epileptic seizures, Recurrent seizures, Seizure disorder NOS This table summarizes the ICD-10-CM codes (1) that apply to many Epilepsy and Seizures diagnoses. To use the table, the sixth and seventh digital characters are added to the end of the first five characters given in the table. The ­characters to use for the sixth and seventh characters are specified by the variables x and z, as noted at the top to the table. For example, Localization-related (focal, partial) symptomatic epilepsy with partial complex s­ eizures, ­intractable, ­without status epilepticus, would be coded as G40.219 by adding 19 (intractable, without status ­epilepticus) onto the end of the Epilepsy code family G40.2.

known findings associated with certain such disorders. Absence epilepsy and juvenile myoclonic epilepsy are given their own unique diagnostic code family in this system, as are a few other specific disorders. The seventh digit for many codes refers to whether the patient suffers from status epilepticus related to this diagnosis. The sixth digit refers to whether the epilepsy or seizures are refractory, that is, intractable. The following terms are to be considered equivalent to intractable: pharmacologically resistant, treatment resistant, medically refractory, and poorly controlled. In practice, the term intractable refers to a patient who continues

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TABLE 11.2  International Classification of Diseases, Tenth Revision, Clinical Modification, Additional Seizure, and Related Codes Mental, Behavioral, and Neurodevelopmental Disorders F44 Dissociative and conversion disorders   Includes: conversion hysteria, conversion reaction, hysteria, hysterical psychosis   Excludes: malingering [conscious simulation] (Z76.5)   F44.5 Conversion disorder with seizures or convulsions   Dissociative convulsions

Symptoms, Signs, and Abnormal Clinical and Laboratory Findings, Not Elsewhere Classified R56 Convulsions, not elsewhere classified  Excludes: dissociative convulsions and seizures (F44.5), epileptic convulsions and seizures (G40.-), newborn convulsions and seizures (P90)  R56.0 Febrile convulsions    R56.00 Simple febrile convulsions, Febrile convulsions, Febrile seizure    R56.01 Complex febrile convulsions     Atypical febrile seizure, Complex febrile seizure, Complicated febrile seizure    Excludes: status epilepticus (G40.901-)  R56.1 Posttraumatic seizures    Excludes: post traumatic epilepsy (G40.-)    R56.9 Unspecified convulsions Convulsion disorder, Fit NOS, Recurrent convulsions, Seizure(s) (convulsive) NOS

Certain Conditions Originating in the Perinatal Period P90 Convulsions of newborn    Excludes: benign myoclonic epilepsy in infancy (G40.3-), benign neonatal convulsions (familial) (G40.3-) This table describes additional ICD-10-CM codes (1) that apply to some convulsions and related seizure diagnoses apart from those that appear in the G40 Epilepsy ICD code family.

to suffer seizures or has suffered a seizure in the past year. The latter tactic avoids repeatedly changing a patient’s diagnosis when he or she is seen frequently and sometimes did or did not have seizures since the most recent previous visit. Physicians are encouraged to code to the highest degree of accuracy. This helps research teams who use this data for studies about population health

11.  REIMBURSEMENT ISSUES IN AMBULATORY EEG  •  195

and outcomes. It also most correctly represents the patient’s condition for the health care coverage carrier. The carrier then uses that information to consider payment policy about the visit, imaging, electroencephalographies (EEGs), and seizure medications. Tables 11.1 and 11.2 describe the typical ICD-10-CM diagnostic codes used for Epilepsy and Seizures. The final one or two characters are not given explicitly in the table for many codes. That helps to organize the code into a short form. A seventh character of 1 is used for patients with status epilepticus, while a seventh character of 9 is used for patients without status epilepticus. A sixth character of 0 is used for not intractable, while a sixth character of 1 is used for intractable. Some codes use an alternate substitution pattern as noted in the table. Table 11.2 describes alternate codes commonly used for seizures in patients diagnosed with a conversion disorder, for febrile or posttraumatic convulsions, for convulsions, of unspecified cause, and for perinatal convulsions. An extended, explicit form of these diagnostic coding tables is on the National Center for Health Statistics, Centers for Disease Control and Prevention website (1). Table 11.3 describes some diagnostic codes for Sleep Disorders. This list is a subset of the available codes. For a more comprehensive list, see the full ICD-10-CM code set published by the National Center for Health Statistics at the Centers for Disease Control and Prevention (1). That includes more codes in these specific families as well as codes for parasomnias, narcolepsy, and other sleep-related disorders. PROCEDURAL CODING Physicians, carriers, hospitals, and regulators rely on Current Procedural Terminology (CPT®) to identify and report accurately their services. A CPT code specifies what service was performed. These include patient visits, neurodiagnostic procedures, and many other services. American Medical Association (AMA) owns, maintains, and copyrights the CPT code set. The 1966 CPT first edition standardized surgical procedural terminology. The 1970s second and third editions expanded CPT to medical and testing procedures. The fourth edition introduced periodic updating of the coding system. Annual updates now appear to the fourth edition (4). Health Care Financing Administration (HCFA), now the Center for Medicare and Medicaid Services (CMS), formally adopted CPT in 1983 as the official system for reporting physician Medicare Part B services. In 1987 HCFA expanded CPT’s required use to reporting outpatient surgical procedures. In the Health Insurance Portability and Accountability Act of 1996 (HIPAA), Congress required the Department of Health & Human Services (HHS) to develop coding standards for electronic data storage and transmission.

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TABLE 11.3  International Classification of Diseases, Tenth Revision, Clinical Modification, Selected Sleep Disorder Diagnoses G47.0 insomnia   G47.00 unspecified   G47.01 due to medical condition   G47.09 other insomnia G47.2 circadian rhythm sleep disorders   G47.20 unspecified type   G47.21 delayed sleep phase type   G47.22 advanced sleep phase type   G47.24 non-24   G47.25 jet lag type   G47.26 shift work type G47.3 sleep apnea   G47.30 unspecified   G47.31 primary central sleep apnea   G47.33 obstructive sleep apnea These are selected ICD-10-CM diagnostic codes (1) for sleep disorders. These are common ones used for ambulatory monitoring. Many additional Sleep Disorders ICD-10-CM diagnostic codes are included in the more ­comprehensive, full ICD-10-CM list, such as codes for narcolepsy and parasomnias.

HHS selected CPT for reporting physician and other allied health services, as well as selecting ICD for reporting diagnoses. CPT’s use has grown to near universal use now for reporting physician health care services. Levels of CPT Codes There are 3 categories of CPT. Category I CPT codes are the typical CPT codes. These procedures are widely used and standard of practice across the United States. Their effectiveness is well supported in the medical literature. FDA approved of their devices if that is relevant. The AMA–CMS Relative Value Scale Update Committee (RUC) evaluates and recommends relative value units (RVUs) for these codes. A series of Ambulatory EEG codes are discussed in the following.

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Category II CPT codes are tracking codes designed to measure ­ erformance improvement. These codes facilitate quality improvement by p standardizing reporting about performance of services. They begin with the character F. A series of epilepsy performance improvement codes are given in Table 11.4. CPT codes that identify quality and outcome of services have a role in a health care economy that strongly encourages physician quality reporting. In the United States, the Medicare Access and CHIP Reauthorization Act (MACRA) refines the Physician Quality Reporting System in a new Merit-Based Incentive Payment System. This quality reporting system went into effect in January 2017. The quality reporting allows use of certain CPT Category II codes. CMS accepts only a subset of Category II codes. The particular codes acceptable for use continues to be refined. Category III CPT codes are temporary codes for new or emerging technology or procedures. They serve for data collection and to code new technology. Category III CPT codes are not assigned a value through the RUC process. Carriers pay for services submitted with some of these codes, whereas others are considered investigational and are not paid. Table 11.5 shows examples of two Category III codes that may be used for patients with epilepsy. Healthcare Common Procedure Coding System (HCPCS) Level II contains codes describing supplies, services, and procedures. While many of these codes describe supplies, some of these codes describe physician services and medical tests. These supplement CPT codes in special circumstances. TABLE 11.4  Category II CPT Codes for Patients With Epilepsy 1119F

Initial evaluation for condition

1121F

Subsequent evaluation for condition

1200F

Seizure type(s) and current seizure frequency(ies) documented

1205F

Etiology of epilepsy or epilepsy syndrome(s) reviewed and documented

3324F

MRI or CT scan ordered, reviewed, or requested

3650F

Electroencephalogram (EEG) ordered, reviewed, or requested

4330F

Counseling about epilepsy specific safety issues provided to patient (or caregiver(s))

4340F

Counseling for women of childbearing potential with epilepsy

5200F

Consideration of referral for a neurological evaluation of appropriateness for surgical therapy for intractable epilepsy within the past 3 years

6070F

Patient queried and counseled about antiepileptic drug (AED) side effects

These are quality of care measurement codes used for patients with epilepsy. These CPT Category II codes (4) are used with patient visits to code for the quality of services.

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TABLE 11.5  Example of Category III CPT Code for Epilepsy 0381T

External heart rate and 3-axis accelerometer data recording up to 14 days to assess changes in heart rate and to monitor motion analysis for the purposes of diagnosing nocturnal epilepsy seizure events; includes report, scanning analysis with report, review and interpretation by a physician or other qualified health care professional

0382T

Review and interpretation only

These two codes are examples of Category III CPT codes (4). In each case the code number ends in the letter T. Four similar codes in this family describe longer recordings.

Definition of Terms Several useful terms are defined in the CPT Neurodiagnostics section: Actigraphy: the use of a portable, noninvasive device that continuously records gross motor movement over an extended period of time. The periods of activity and rest are indirect parameters for estimates of the periods of wakefulness and sleep of an individual. Attended: a technologist or qualified health care professional is physically present (ie, sufficient proximity such that the qualified health care professional can physically respond to emergencies, to other appropriate patient needs, or to technical problems at the bedside) t­ hroughout the recording session. Peripheral arterial tonometry (PAT): a plethysmography technique that continuously measures pulsatile volume changes in a digit. This reflects the relative change of blood volume as an indirect measure of sympathetic nervous system activity, which is used in respiratory analysis. Respiratory analysis: generation of derived parameters that describe components of respiration obtained by using direct or indirect parameters, for example, by airflow or peripheral arterial tone. Unattended: a technologist or qualified health care professional is not physically present with the patient during the recording session. Special EEG Tests Ambulatory 24-hour EEG is coded with CPT code 95953. Two other codes are described here to compare and contrast them with code 95953. Those two other codes are the EMU and ICU video-EEG monitoring code 95951, and the no-video ICU EEG monitoring code 95956. Codes 95951, 95953, and 95956 are used per 24 hours of recording. Codes 95951 and 95956 are used for recordings in which interpretations can be made throughout the recording time, with interventions to alter or end the recording or to alter the patient care during the recordings as needed.

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The descriptors for these three codes are: 95951 Monitoring for localization of cerebral seizure focus by cable or radio, 16 or more channel telemetry, combined EEG and video recording and interpretation (eg, for presurgical localization), each 24 hours. 95953 Monitoring for localization of cerebral seizure focus by computerized portable 16 or more channel EEG, EEG recording and interpretation, each 24 hours, unattended. 95956 Monitoring for localization of cerebral seizure focus by cable or radio, 16 or more channel telemetry, electroencephalographic (EEG) recording and interpretation, each 24 hours, attended by a technologist or nurse. Code 95953 is used either with or without video. It is used in unattended situations, that is, when a technologist is not physically present with the patient during the recording session (5). Code 95953 often is recorded first and interpreted after the recording is finished. In contrast, codes 95951 and 95956 are interpreted as the recordings are being made, with decision making during the recording time about continuing the recording or about changes in medication. Codes 95951, 95953, and 95956 include digital analysis as a bundled part of the basic procedures. Spike and seizure detection is a kind of digital analysis that is included as a part of those procedures. Code 95957 is used when a separate digital analysis is performed that requires separate extended technologist time and extended physician time, sometimes by a technologist and physician different than those involved in the base 95951, 95953, or 95956 procedure. The typical use of code 95957 is for digital spike dipole source localization using advanced software and averaging processes. Code 95957 requires a separate report describing the procedure, results, and interpretation of the separate procedure (6). Code 95957 is not appropriate for spike and seizure detection, or for simple trend displays, in addition to codes 95951, 95953, or 95956. For recording 12 hours or less, use modifier 52. For overnight recordings of approximately 24 hours, code for one unit of 95953. For a recording of 30 to 36 hours code the first day as 95953 and the second day as 95953 with modifier 52. For a recording of 36 to 54 hours, code one unit of 95953 for each of two separate days. Note that the code is not two units of service on the same date of service but rather one unit of service on each of two successive dates. Ambulatory Sleep Tests Four CPT Category I codes describe ambulatory sleep testing. In addition, CMS created three HCPCS codes, G0398, G0399, and G0400, to describe similar ambulatory sleep testing. Currently the CPT codes have mostly replaced the use of those CMS G-codes. Some carriers still use the HCPCS G-codes, whereas others accept either a CPT or an HCPCS G-code. An additional

200  •  Ambulatory EEG Monitoring

CPT code exists for actigraphy. See aforementioned definitions for the terms peripheral arterial tone and respiratory analysis, which are used in these descriptors. The code descriptors are: 95800 Unattended sleep study, simultaneous recording of heart rate, oxygen saturation, respiratory analysis (eg, by airflow or peripheral arterial tone), and sleep time. 95801 Unattended sleep study, simultaneous recording of minimum of heart rate, oxygen saturation, and respiratory analysis (eg, by airflow or peripheral arterial tone). 95806 Unattended sleep study, simultaneous recording of heart rate, oxygen saturation, respiratory airflow, and respiratory effort (eg, thoracoabdominal movement). G0398 Home sleep study test (HST) with type II portable monitor, unattended; minimum of 7 channels: EEG, electro-oculography (EOG), electromyography (EMG), electrocardiogram (ECG)/heart rate, airflow, respiratory effort, and oxygen saturation. G0399 HST with type III portable monitor, unattended; minimum of 4 channels: 2 respiratory movement/airflow, 1 ECG/heart rate, and 1 ­oxygen saturation. G0400 HST with type IV portable monitor, unattended; minimum of 3 channels. 95803 Actigraphy testing, recording, analysis, interpretation, and report (minimum of 72 hours to 14 consecutive days of recording). The sleep studies 95800, 95803, 95806, G0398, G0399, and G0400 are recorded overnight. If less than 6 hours are recorded, report with modifier 52. The actigraphy code is recorded for many days. Only one unit of service is coded even if the record continues up to 2 weeks. If less than 72 hours is recorded, report with modifier 52. Beyond these basic ambulatory tests, polysomnography is conducted overnight commonly in a Sleep Lab facility. In recent years, carriers have encouraged use of ambulatory sleep testing in lieu of in-lab polysomnography. The codes used most often at traditional Sleep Lab facilities are CPT 95810, 95811, 95782, and 95783, which are attended tests. Those codes require more extensive preparation and recording. The extensive rules for use of those attended tests are beyond the discussion of this chapter. DATE AND SITE OF SERVICE The official Site of Service is where the patient was when the service was conducted. For a diagnostic test, that is when the recording took place even if the physician later interpreted the recording from an office or a hospital lab. Some carriers consider that the site of service is home for ambulatory testing in which

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the patient is sent home with a recorder. Other carriers consider that the site of service may be office or outpatient hospital, wherever the hookup and takedown is accomplished. Physicians and labs should check if your local carriers have preferences for the use of home for site of service for ambulatory recording. Since the site of service is the place where the patient was at the time of the recording, the physician interpreting the test should hold a license in the state where the patient was tested. This might be a problem for physicians whose patients may live at a distance across a state line and who take home an ambulatory system for recording. Likewise the official date of service for a professional component is the date on which the recording took place, not the date on which the physician interpreted the test or dictated the report. A 24-hour EEG service, a weeklong actigraphy, or an overnight sleep study may be coded on the initial day or on the completion day. Many providers use the completion date. CMS bundles some of these codes’ technical components with a facility visit if the patient is seen in clinic or another facility site on the same date of service used for coding. That bundling rule applies more specifically to the actigraphy service and the unattended sleep study code 95801. The technical component coding does not need to match the professional component in its coding detail. The two might have different dates of service or other coding differences when a facility lab submits the technical component while a physician’s professional group submits the professional component. ACKNOWLEDGMENT CPT® is a registered trademark of the American Medical Association.

REFERENCES 1. Centers for Disease Control and Prevention, National Center for Health Statistics. International Classification of Diseases, Tenth Revision, Clinical Modification ­(ICD-10-CM). Available at: http://www.cdc.gov/nchs/icd/icd10cm.htm 2. Fisher RS, Acevedo C, Arzimanoglou A, et al. ILAE official report: a practical clinical definition of epilepsy. Epilepsia. 2014;55:475–482. 3. Jette N, Beghi E, Hesdorffer D, et al. ICD coding for epilepsy: past, present, and future—a report by the International League Against Epilepsy Task Force on ICD codes in epilepsy. Epilepsia. 2015;6:348–355. 4. American Medical Association (AMA). Current Procedural Terminology, Version 4 (CPT-4), 2016. Chicago, IL: American Medical Association; 2016. 5. American Medical Association. Frequently Asked Questions: Medicine: Neurology and Neuromuscular Procedures. American Medical Association, CPT Assistant 2014, December 2014:17. 6. American Medical Association. Coding Brief: EEG Epileptic Spike Reporting. American Medical Association, CPT Assistant 2010, November 2010.

CHAPTER 12

AMBULATORY EEG CASES JOSEPH DRAZKOWSKI, MD and EJERZAIN ANILES-RENOVA, BS, R. EEG T., CLTM

Advances in technology have expanded the capabilities to record electroencephalography (EEG) in the last two decades. The improved use of technology to digitalize EEG increases the versatility of recording. This has led to more reliability in interpretation of EEG recordings by prolonging traditional routine scalp EEG. This allows more clinical neurophysiological techniques including ambulatory EEG (aEEG) to be available to the clinician who desires to supplement his or her traditional skills of a comprehensive history and physical exam in the diagnosis of epilepsy and seizure-like events (aka spells). Ambulatory EEG is typically utilized if the routine scalp EEG obtained awake and asleep has not been diagnostic or if uncertainty persists (1,2). The aEEG is superior to the routine scalp sleep-deprived EEG in recording typical seizures (15%) but had similar results when recording interictal epileptiform discharges (2). The obvious advantage to support the use of aEEG is the extended recording time that has become possible through the advent of large memory storage capacity and extended battery life with reliable recording devices that approach the sophistication of inpatient videoEEG monitoring systems. As with any diagnostic tool, the yield of useful information regarding individual cases depends on the questions that are asked for an individual patient (3). In addition to EEG, other physiological parameters can now be acquired easily and reliably. These additional measurements often include ECG, eye movements, respirations, oxygenation, and digital video. Another major advantage of aEEG is the relatively lower cost as compared with inpatient video-EEG monitoring in the epilepsy monitoring unit (EMU). The three main indications for ordering an aEEG are similar to the use of other forms of EEG and include (a) differential diagnosis of paroxysmal events (including identifying and quantifying epileptiform discharge to support a clinical diagnosis), (b) spell classification, (c) seizure quantification, and (d) rarely characterizing seizures for epilepsy therapy. 203

204  •  Ambulatory EEG Monitoring

Older studies using four- and eight-channel aEEG machines reported utility in recording seizures and seizure quantification in the adult and pediatric population (4,5). In adults this concept of utility of aEEG in epilepsy and seizure quantification was also confirmed (2). The use of aEEG had substantial impact in the management of enrolled subjects with a yield of 67% and approximately 80% respectively in two studies involving aEEG (6,7). These studies also confirm that many of the subjects were unaware of seizures and did not reliably report a substantial number of recorded seizures (2). The fact that the seizures may occur without awareness should be remembered by ordering clinicians in that the patient may under report the actual number of seizures. The clinician should not be discouraged from ordering aEEG based on reported seizure counts alone. The history and physical exams on neurology patients have been suggested to be the cornerstone of the evaluation of neurology patients. This truism is probably applicable to many neurological diseases. However, an evaluator of seizure and spells patients should know that accurate diagnosis has significant treatment implications. For example, the divergent treatment of an epilepsy patient with antiseizure drugs (ASDs) versus a nonepileptic seizure patient treated by psychiatric interventions versus a convulsive syncope patient with cardiovascular implications underscores the considerable diversity of treatments and serious clinical implications. How good are we at making an accurate diagnosis in hard to classify patients with transient neurological spells? The answer is that with our current level of accuracy there is room for improvement. Studies that try to answer this question suggest that we make the wrong diagnosis 20% to 25% of the time (8) and that self-reported seizures are typically under reported (9, 10). A significant limitation associated with the aEEG is that we cannot provide ancillary testing during event monitoring, immediate technical support, or reduce medications safely in the outpatient setting. There is no ability without supportive staff to execute a rescue plan in the event of an emergency. This could be especially problematic in the outpatient setting. The National Association of Epilepsy Centers requires a rescue plan in the event of an emergency such as status epilepticus or other event-related complications (www.naec-epilepsy.org). These series of cases were spe­ cifically chosen and included to emphasize important issues when considering the clinical use of aEEG. CASE 1 CB is a 50-year-old male with a 9-month history of spells reported with a sudden onset anxiety and hyperventilation. The spells began without clear etiology and occurred at a frequency of one to three times per day.

12.  AMBULATORY EEG CASES  •  205

The spells lasted 30 to 60 seconds. The patient never lost awareness and was able to communicate with family members. The spells increased in frequency to nine times a day. Neuroimaging and a routine scalp EEG were unrevealing. Psychiatry was consulted with a preliminary diagnosis of panic disorder, and appropriate anxiolytic medications were started. Unfortunately, the spells continued, and a 48-hour computer-assisted ambulatory EEG (CAA-EEG) was ordered. Figure 12.1A demonstrates the EEG prior to a typical event captured representing one of six that were recorded. The EEG during the event illustrates evolving left hemispheric rhythmic ictal theta activity that originated from the left temporal head region (Figure 12.1B). The spells were delineated during the aEEG recording utilizing the push-button event that was activated by the patient and confirmed to represent his typical attack. The spell lasted approximately 20 seconds and terminated with an abrupt offset and a rapid return of the baseline electrocerebral activity (Figure 12.1C). (A)

FIGURE 12.1  (A) The event marker initiated by patient just prior to EEG changes. Parameters of recording: time base 30 mm/sec, filters 1 to 70 Hz, and sensitivity 7 μV/mm. (continued )

206  •  Ambulatory EEG Monitoring

(B)

(C)

FIGURE 12.1 (continued)  (B) Subtle ictal theta developing in the left hemisphere that emanated from the temporal derivations. Parameters of recording: time base 30 mm/sec, filters 1 to 70 Hz, and sensitivity 7 μV/mm. (C) The end of the ictal period and a return to baseline EEG after about 21 seconds of clinical and EEG changes. Parameters of recording: time base 30 mm/sec, filters 1 to 70 Hz, and sensitivity 7 μV/mm.

12.  AMBULATORY EEG CASES  •  207

Key Points for the Use of aEEG in Case 1 • The spells were frequent enough to facilitate the rapid assessment with outpatient CAA-EEG to capture a typical event. • Although the EEG changes were subtle, the aEEG was technically adequate to interpret the ictal EEG during the patients’ typical events to make the diagnosis of focal epilepsy. • The patient was ultimately diagnosed as having voltage-gated potassium channel auto antibodies, which were successfully treated with immune modulation (steroids then mycophenylate). CASE 2 TR is a 6-year-old boy with a normal birth and development until 1 month prior to evaluation when he experienced two convulsive seizures in a 1  week period of time. An MRI and neurological examination were normal. The preschool teacher noted he did not seem to be recalling things that he had mastered 2 months prior. Because of the seizures, a routine 30-­minute EEG was completed showing a normal waking pattern and a pattern that was suspicious for bi-frontal spike and wave during one brief period of sleep (Figure 12.2A). The child was placed on an antiseizure medication, and a follow-up examination was established for reevaluation after several weeks. Unfortunately the antiseizure medication was ineffective, and he continued to have seizures. A second opinion was then sought, and a 24-hour CAA-EEG was obtained. The following EEG sample shows ­essentially a ­continuous bilateral fronto-central spike-and-wave discharge (Figure 12.2B). Key Points for Case 2 • The CAA-EEG was diagnostic for electrical status epilepticus during sleep (ESES). The EEG provided information that confirmed the diagnosis of Landau–Kleffner syndrome associated with the characteristic EEG pattern present in slow wave sleep with this condition. • The aEEG guided the redirection of the therapeutic approach to the proper therapy with steroids and benzodiazepines. The early diagnosis-targeted therapy is important in patients with ESES to provide a better long-term outcome. • The long-term aEEG offered overnight EEG recording as an alternative in the natural environment as opposed to scheduling an admission to the hospital where natural sleep may have been disturbed and interrupted to limit diagnostic recovery of the abnormality during sleep.

208  •  Ambulatory EEG Monitoring

(A)

(B)

FIGURE 12.2  (A) A waking pattern and possible bi-frontal spike and wave during a brief period of sleep. Parameters of recording: time base 30 mm/sec, filters 1 to 70 Hz, and sensitivity 7 μV/mm. (B) EEG reveals continuous bi-fronto-central spike-and-wave discharges during sleep. Parameters of recording: time base 30 mm/sec, filters 1 to 70 Hz, and sensitivity 7 μV/mm.

CASE 3 AJ is a 34-year-old female with spells of uncertain etiology. She had been seen by a neurologist for spells that occurred two to three times per week though reported no clear diagnosis. The spells were described as having symptoms of déjà vu followed by sudden loss of awareness. In addition, she also experienced symptoms that included spells during which she

12.  AMBULATORY EEG CASES  •  209

would suddenly drop to the ground that appeared to be different than the other type of spell. Her sister reported witnessing one of the later events during which the patient appeared pale and suddenly slumped to the floor. They would typically last about 60 to 90 seconds, and she would occasionally be slightly confused after the event. A routine scalp awake and asleep EEG, ECG, general laboratory assessment, and brain MRI were all normal. The patient was reluctant to be admitted to the EMU to seek a definitive diagnosis for classification of her spells. Subsequently a 48-hour CAA-EEG was performed, and an event was captured during the recording (Figure 12.3A–D). During the CAA-EEG, focal seizures were captured that arose from the left temporal head regions (Figure 3A and B). These accounted for the patient’s dyscognitive seizures historically when they were self-limited. An event was captured during the CAA-EEG (Figure 12.3C and D) with a different semiology that included pallor and fall to the ground identified by (A)

FIGURE 12.3  (A) EEG demonstrating a focal seizure arising from the left temporal head regions within the left hemisphere during a “déjà vu.” EKG demonstrates sinus rhythm. Parameters of recording: time base 30 mm/sec, filters 1 to 70 Hz, and sensitivity 7 μV/mm. (continued )

(B)

(C)

FIGURE 12.3 (Continued) (B) EEG during the spread of the seizure in (A) evolving to the hemispheres bilaterally during a focal seizure with dyscognitive feature. Parameters of recording: time base 30 mm/sec, filters 1 to 70 Hz, and sensitivity 7 μV/mm. (C) EEG during prolongation of the seizure in (A and B) with heart rate decline in latter part of the tracing leading into a period of asystole. Parameters of recording: time base 30 mm/sec, filters 1 to 70 Hz, and sensitivity 7 μV/mm.

12.  AMBULATORY EEG CASES  •  211

(D)

FIGURE 12.3 (Continued) (D) ECG continuing to demonstrate cardiac asystole with increasing higher amplitude delta slowing characteristic of the changes seen with syncope. Parameters of recording: time base 30 mm/sec, filters 1 to 70 Hz, and sensitivity 7 μV/mm.

the patient diary and video review. Figure 12.3A–D depicts the onset of a focal seizure starting in the left temporal head regions and evolving to ictal asystole and terminal high amplitude diffuse delta slowing characterizing her episode of syncope due to asystole. Key Points for Case 3 • Despite self-reporting a modest seizure frequency, with the use of CAAEEG, we were able to document an event to make a definitive diagnosis of focal epilepsy. • In addition to the semiology suggesting focal seizures, the aEEG was able to document more than one type of event to assist in classification of events involving both epileptic seizures and physiological nonepileptic events. • We were able to record an event of ictal asystole to explain the atypical seizure symptoms when she would turn pale and drop to the ground that

212  •  Ambulatory EEG Monitoring

could result in injury from witnessed events that were consistent with ­syncopal episodes. • Typical of aEEG, the ECG is included as part of the routine study. Unfortunately, the same is not true of long-term ECG recordings (Holter/ event monitors). In this case, if an event monitor captured the ECG changes, an erroneous diagnosis would have been made of a cardiac arrhythmia causing the symptoms instead of the reverse. Insertion of a cardiac pacemaker in addition to ASDs were considered after the definitive dual diagnosis obtained with CAA-EEG monitoring. CASE 4

Device 101977, 11/19/2005 200 180 160 140 120 100 80 60 40 20 0

0 1 2 0

1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Hour

Seizures, Long Episodes

Detections, RX, patterns

JJ is a 39-year-old female with well-documented independent bitemporal seizures. Bilateral seizure onset was present on noninvasive monitoring that was subsequently confirmed on Phase II monitoring with intracranial electrode utilizing standard depth electrocorticography (ECoG). The patient had been refractory to more than 12 different ASDs and was not a favorable candidate for resective surgery. She was subsequently implanted with the Responsive Neuro Stimulator (Neuropace, Inc., Mountain View, California). The device was regularly interrogated and ECoG analyzed during chronic ambulatory intracranial EEG. During the initial programming, the patient had a stable baseline number of daily detections of “long episodes” and “saturations.” During routine uploads of data one day, it was noted that the number of episodes of epileptiform activity had increased greatly around midnight. Indeed the patient had missed two doses of her antiseizure medication and had been suffering from several auras that night. Figure 12.4 illustrates the trend in the frequency of epileptiform activity detected that day followed by a sudden increase in detections.

FIGURE 12.4 The stable hourly detections followed by a sudden increase around midnight when the frequency increased from approximately 60 to 80 per hour to 180 per hour.

12.  AMBULATORY EEG CASES  •  213

Key Points for Case 4 • Technology in recording CAA-EEG is constantly changing and i­ mproving. While the RNS technology is invasive and records intracranial EEG, it nonetheless is chronic intracranial aEEG confined to highly selected cases. One can imagine the development of CAA-EEG technology advances that include minimally invasive or wearable aEEG for extended periods of time to help quantify seizures. • CAA-EEG may aid in the quantification of seizures in refractory epilepsy patients and may also help patients better manage their epilepsy by analyzing seizure trends and quantification of abnormal scalp or intracranial aEEG. • The use of chronic intracranial aEEG may aid in the long-term management of seizures in the ambulatory setting. • Chronic intracranial CAA-EEG has a demonstrated safety profile of chronically implanted intracranial electrodes recording directly from the cortex with either subdural strip electrodes and/or from depths (typically mesial temporal structures). CASE 5 MM is a 50-year-old male with daily spells that he described as episodes that would start with a “funny feeling.” He would then proceed to stiffening of his limbs and falling to the floor subsequently “flopping like a fish.” The number of attacks had been increasing in frequency and occurred several times per week during the last month. Two routine scalp EEGs were normal. An aEEG captured a “typical event,” but no video was available at the time. The event began and ended quickly, and during the attack the aEEG appeared that is depicted Figure 12.5. Key Points for Case 5 • This EEG appears to show rhythmic movement artifact; the EEG is severely obscured by this activity. No abnormal interictal activity is present to support a diagnosis of epilepsy. The lack of concomitant video during aEEG recording may hinder the interpretation of events like this. • A home video obtained using a video recorder, tablet smartphone, or other personal electronic device could potentially add significant clarity to individual cases in which aEEG is obscured by artifact or absent on routine scalp EEG recording (eg, frontal lobe epilepsy). • This case highlights an inherent problem with the aEEG. The lack of observation for the semiology of an event may lead to mistakes in drawing an electroclinical conclusion that helps definitively identify the etiology of patient events. Most proprietary recording systems now include video recording capabilities to combine aEEG to avoid these situations.

214  •  Ambulatory EEG Monitoring

FIGURE 12.5  Motion artifact obscuring interpretation of the background EEG. Note the “contamination” of the ECG and alternating phase reversals that are consistent with nonphysiological movement artifact. Parameters of recording: time base 30 mm/sec, filters 1 to 70 Hz, and sensitivity 7 μV/mm.

CASE 6 AH is a 22-year-old female with a 5-year history of staring spells and occasional convulsions. Her initial evaluation included a normal brain MRI and an EEG that reportedly demonstrated sleep activated “brief fragments of spike-and-wave activity” that were at times more prominent over the right hemisphere. The patient was originally placed on valproic acid several years ago with excellent results, becoming seizure free. The patient is now planning to become pregnant and wanted to stop her antiseizure medication. During reevaluation, a follow-up brain MRI was reported to have hippocampal asymmetry with a relative left-sided atrophy but without signal change. A 72-hour aEEG was performed to provide further classification and quantification in addition to clarifying the clinical situation that included consideration for a trial of ASD taper to discontinuation. Figure  12.6A shows the initial recording that was obtained. The normal study that was initially encountered then became abnormal as the study continued as the number and duration of generalized spike-and-slow-waves and generalized polyspike-and-slow-waves increased dramatically and is shown in Figure 12.6B. When the patient returned to have the aEEG removed, the

12.  AMBULATORY EEG CASES  •  215

(A)

(B)

FIGURE 12.6  (A) Normal initial routine scalp EEG recording. Parameters of recording: time base 30 mm/sec, filters 1 to 70 Hz, and sensitivity 7 μV/mm. (B) Ambulatory EEG demonstrating generalized spike-and-waves and generalized polyspike-and-wave discharges. Parameters of recording: time base 30 mm/sec, filters 1 to 70 Hz, and sensitivity 7 μV/mm.

216  •  Ambulatory EEG Monitoring

technologist noted the patient was a bit confused and was manifesting ­frequent myoclonus of the upper extremities. A quick review of the EEG before the patient was sent home is shown in Figure 12.6B. On further inquiry, it turns out the patient self-discontinued her valproic acid due to concerns of teratogenicity. A diagnosis of genetic generalized epilepsy was made and the EEG features consistent with juvenile myoclonic epilepsy with the need for long-term treatment and compliance emphasized. Admission to the hospital following immediate interpretation of the aEEG was required to stabilize her condition and reintroduce ASDs. Key Points for Case 6 • The results of prolonged EEG monitoring confirmed myoclonic seizures associated with generalized epilepsy that were not present on routine scalp EEG. • The abnormal generalized spike-and-waves and polyspike-and-waves on this patient’s EEG were suppressed by valproic acid defying identification on routine scalp EEG. Only during noncompliance was an increase in the generalized epileptiform discharges apparent on EEG. • Patient self-discontinuation of her antiseizure medication valproic acid against medical advice emphasizes one limitation of aEEG recording and the potential danger imposed by tapering or stopping seizure medications as an outpatient. • In this case, the asymmetry of the hippocampus without signal change remained stable for 2 years and at first confused the therapeutic approach. Proper classification is essential to correctly select an appropriate treatment for a patient with juvenile myoclonic epilepsy given the possibility to aggravate myoclonic seizures and produce recurrent myoclonic status epilepticus. CONCLUSION The aEEG is a tool that can be used to help clarify an individual patient’s clinical situation with transient paroxysmal neurological events. A clear understanding of the aEEG’s technical and safety limitations is essential for using this recording technique effectively. Where there is a question of a definitive diagnosis, spell classification (including nocturnal events), quantifying seizures, or addressing seizure characterization, the aEEG is often a helpful supplement to the clinical diagnosis by providing prolonged EEG recording in the natural environment. The cases presented here are provided to demonstrate some of the examples where the utility of aEEG played a significant role in clinical practice.

12.  AMBULATORY EEG CASES  •  217

REFERENCES 1. Tatum W, Winters L, Gieron M, et al. Outpatient seizure indentification: results of 502 patients using computer assisted ambulatory EEG. J Clin Neurophysiol. 2001;18:14–19. 2. Liporace J, Tatum W, Morris GL, French J. Clinical utility of sleep deprived versus computer assisted ambulatory 16 channel EEG in epilepsy patients: a multicenter trial. Epilepsy Res. 1998;32:357–362. 3. Schomer D. Ambulatory EEG telemetry. How good is it? J Clin Neurophysiol 2006;23:94–305. 4. Aminoff M, Goodin DS, Bergo BO, Compton MN. Amulatory EEG recordings in epileptic and non-epileptic children. Neurology. 1988;38:558–562. 5. Kerr S, Shucard M, Kohman MH, Cohen ME. Sequential use of standard and ambulatory EEG in neonatal seizures. Pediatr Neurol. 1990;6:159–162. 6. Morris G, Galezowska R, Leroy R, North R. The results of computer-assisted ambulatory 16 channel EEG. EEG Clin Neurophysiol. 1994;91:229–231. 7. Morris G. The clinical utility of ambulatory 16 channel EEG. J Med Eng Tech 1997;21:47–52. 8. Lanz M, Oehl B, Brandt A, Schulze-Bonhage A. Seizure induced cardiac asystole in epilepsy patients undergoing long term video-EEG monitoring. Seizure. 2011;20:167–172. 9. Noe K, Grade M, Stonnington CM, Driver-Dunckley E, Locke DE. Confirming psychogenic non-epilepsy seizures with video EEG. Epilepsy Behav. 2012;23(3):220–223. 10. Blum DE, Eskola J, Bortz JJ, Fisher RS. Patient awareness of seizures. Neurology. 1996;47(1):260–264.

EPILOGUE

Over the last 40 years, ambulatory electroencephalography (aEEG) has evolved as a practical–clinical neurophysiology tool. It has proven to be helpful in the diagnosis of epilepsy and nonepileptic events, yet remains an extension of the history and physical examination. It is most complete when audio-video recording is used simultaneously. Many of the initial technological drawbacks since the inception of the first 4-channel cassette recorders have been overcome by digital advances in computer technology and software development. The strengths of aEEG include the greater yield compared with routine scalp electroencephalography (EEG) in diagnosis and classification of seizure disorders due to prolonged interictal ambulatory monitoring and potential to obtain ictal recordings. The unique ability to perform overnight EEG recording in the patient’s natural environment exists with the conveniences of home monitoring and the high-fidelity but lower cost compared with inpatient video-EEG monitoring. The use has been extended to provide clinical support for a differential (or definitive) diagnosis, classification, quantification, and in rare cases electroclinical characterization of patients with drug-resistant focal seizures selected as epilepsy surgery candidates. Limitations include a significant concern with artifact that arises in the outpatient setting due to the unsupervised (and often immediately uncorrectable) environment outside the hospital by a qualified technologist. In addition, due to the safety constraints incurred from the lack of supervision by trained personnel, limitations of antiseizure drug withdrawal and lack of immediate access to emergency medical care are drawbacks of aEEG compared with inpatient video-EEG monitoring. Artifacts commonly occur during aEEG and often include nonphysiological artifacts from electrode malfunction and physiological artifacts generated by patient eye movement, muscle use, and more complex movements that are typically present during normal activities of daily living. The significance of artifact in aEEG is self-evident to practitioners who use this EEG technology for patient care with the potential to mistake artifact for epileptiform activity. The decision to obtain an aEEG must first be considered as an alternative to repeating a sleep-deprived EEG, prolonged EEG recording in the EEG laboratory, and inpatient video-EEG. 219

220 • Epilogue

Important issues for successful implementation of aEEG include the initial setup to ensure an optimal scalp-electrode interface is secured and conducting EEG with ECG and other monitors to adequately personalize the study for an individual patient. Reviewing with the patient important aspects that will render a useful aEEG monitoring session includes patient participation such as the use of a diary for self-reporting events, adhering to common safety precautions, combined push-button activation, and contacting technical support if system failure is encountered including video recording. In the future, the practice of aEEG will evolve further and expand to involve novel sensors, wireless interfaces, and as yet unrealized benefits from greater knowledge obtained through long-term recording, on circadian rhythms, and environmental influences obtained from chronic ambulatory scalp-based as well as intracranial EEG. As mobile health surges in the United States and technology advances our ability to perform interactive studies remotely, aEEG is advancing to provide costeffective, essential information in the field of epilepsy, sleep medicine, nocturnal movement disorders, and memory complaints bridging the “gray area” between seizures and paroxysmal neurological events with neurophysiological signals generated by the brain. William O. Tatum, IV, DO

Index

AASM. See American Association of Sleep Medicine academic-based clinicians, 186 accelerometers, 182–183 actigraphy, 140–144, 198, 200 adults, 75 differential diagnosis, 77–90 management, 90–95 aliasing, 22 AMA. See American Medical Association ambulatory sleep monitoring home sleep apnea testing. See home sleep apnea testing nocturnal video recordings, 144–145 polysomnogram, 131, 132, 200 seizure detection devices, 145–146 smartphone applications, 146–148 ambulatory sleep tests, 199–200 American Association of Sleep Medicine (AASM), 132 American Medical Association (AMA), 195 amplifier/connection unit, 36–38 analog electrical waveforms, 21–24 antiseizure drugs (ASDs), 179 APAP. See auto-adjusting CPAP device apnea, 137–138 Apple Store, 146 artifact, 41 and ambulatory EEG, 46 nonphysiologic sources, 54, 56–59 overcoming, 66–68 physiologic sources, 59–61 reduction, 69 significance, 61–66 technical considerations, 51–53 ASDs. See antiseizure drugs AspireSR®, 184 attended technologist, 198 auto-adjusting CPAP device (APAP), 133

autoclipping, 29 automated scoring, 139 automated wireless system, 147 automatic computerized analysis, 29 bilateral seizure, 212 bilateral temporal onset seizures, 162–164 bioelectrical fluctuations, 15 Biostamp, 185 blocked analog, 5 brain–machine interfaces, 170–172 Brain Sentinel®, 145, 184 CAA-EEG. See computer-assisted ambulatory EEG CAIEEG. See chronic ambulatory intracranial EEG cardiac arrhythmias, 86 cardiorespiratory devices, 135–136 cases of ambulatory EEG, 203–216 category I CPT codes, 196 category II CPT codes, 197 category III CPT codes, 197 for epilepsy, 198 cEEG. See continuous EEG chewing artifact, 50, 51 child perception, 111 children advantages of aEEG recordings, 108–109 disadvantages of aEEG recordings, 109–111 recommendations for family, aEEG recordings, 102–103 success rates of aEEG in, 108–111 chin electromyography, 132 chronic ambulatory intracranial EEG (CAIEEG), 156, 157 in animal models, 167 auditory systems, 166 brain–machine interface, 170–172 221

222 • Index

chronic ambulatory intracranial EEG (cont.) case study, 172 cortical activity, 166 home environment, recording in, 160 limitations to, 166 long-term monitoring, 161–165 reliability of, 159 responsive neurostimulation, 157 seizure prediction, 167–170 chronic ambulatory monitoring, 127 circadian rhythm, 44 clinical neurophysiology laboratory, 13 clipping, 29 CMR. See common mode rejection collodion technique, 34, 54 common mode rejection (CMR), 19, 36, 38 computer-assisted ambulatory EEG (CAA-EEG), 13, 207, 209, 213 continuous EEG (cEEG), 48 recording, 2–8 yield of, 117 continuous positive airway pressure (CPAP), 132, 133 contralateral seizure, 162 cost of aEEG, 109 CPAP. See continuous positive airway pressure Current Procedural Terminology (CPT) codes, 195 95951, 198, 199 95953, 198, 199 95956, 198, 199 95957, 199 category I, 196 category II, 197 category III, 197 neurodiagnostics terms, 198 for sleep studies, 200 dementia, 127 detection algorithms, 43 diagnostic coding, reimbursement issues and, 191–195 differential amplifiers, 18–20 digitization, 21–24 discontinuous aEEG recording, 8–9 disposable EEG patch (Epitel, Inc.), 181 driven right leg, 19 ECG monitoring, 183 EcoG. See electrocorticography Ecology of Epilepsy, 95 EDA sensor. See electrodermal activity sensor

EDs. See epileptiform discharges eight-channel aEEG montage, 4, 5, 7 18-channel EEG recorder, 9, 10 EKG. See electrocardiogram electrical double layer, 15 electrical status epilepticus during sleep (ESES), 207 electrocardiogram (EKG), 35–36, 101 electrocorticography (EcoG), 155, 156 electrodermal activity (EDA) sensor, 183 electrodes, 15–17, 54, 56 artifact, 15 placement of EEG, 100, 101 wires, 36 electroencephalographers, 33 electrographic seizures, 162 electromyography (EMG), 35, 101 electrostatic artifact, 52 Embrace™, 183 EMFIT™ movement monitor, 145 EMG. See electromyography EMU. See epilepsy monitoring unit epilepsy, 79–82 category II CPT codes for, 197 centers, 93 definitive diagnosis of, 124 ecology of, 95 example of category III CPT code for, 198 ICD-10-CM diagnostic codes for, 192–194 patients’ quality of life, 179–180 syndromes, 124 wearable monitoring system, 187 epilepsy monitoring unit (EMU), versus EEG monitoring, 76 epileptiform discharges (EDs), 41, 43, 61 frequency, 107 presence of, 123 esap (RTI International), 184 ESES. See electrical status epilepticus during sleep extracranial EEG-based systems, 181 eye blink artifact, 49 eye movement artifact, 59, 61 Fitbit Ultra™, 144 focal onset seizures, 83–84 focal seizure, 209 four-channel aEEG montage, 4, 5 galvanometer, 18 generalized spike and waves (GSWs), 45, 63

Index • 223

generalized tonic–clonic seizures (GTCSs), 182, 183 gold-standard method, 116 GSWs. See generalized spike and waves GTCSs. See generalized tonic–clonic seizures HCFA. See Health Care Financing Administration Healthcare Common Procedure Coding System (HCPCS) codes, 199 Level II, 197 Health Care Financing Administration (HCFA), 195 Health Insurance Portability and Accountability Act of 1996 (HIPAA), 195 heart rate, 135 HIPAA. See Health Insurance Portability and Accountability Act of 1996 home sleep apnea testing (HSAT) American Association of Sleep Medicine clinical guidelines for, 134 cardiorespiratory devices for, 135–136 definition of, 132 false-negative rates for, 139 limitations, advantages, and disadvantages of, 138–140 obstructive sleep apnea diagnosed by, 133, 134 scoring apneas and hypopneas using, 137 using nocturnal home pulse oximetry, 136–137 hyperventilation, 102 hypopnea, 137–138 ICD-10-CM. See International Classification of Diseases, Tenth Revision, Clinical Modification ICSD-3. See International Classification of Sleep Disorders version 3 ictal tachycardia, 86 ideal pediatric candidate for aEEG, 105 IEDs. See interictal epileptiform discharges ILAE. See International League Against Epilepsy impedance testing, 54, 56 inpatient video-EEG monitoring, 99 interictal epileptiform discharges (IEDs), 119–120 International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM), 191 additional seizure and related codes, 194 epilepsy and seizure-related codes, 192–193 selected sleep disorder diagnoses, 196 International Classification of Sleep Disorders version 3 (ICSD-3), 140

International League Against Epilepsy (ILAE), 191 intracranial EEG, 155–156, 181. See also chronic ambulatory intracranial EEG juvenile myoclonic epilepsy (JME), 64, 67, 80 limb electrode, 35 long-term monitoring (LTM), 1, 4, 100 machine reference, 20 Medicare Access and CHIP Reauthorization Act (MACRA), 197 Merit-Based Incentive Payment System, 197 Moore's law, 22–23 multicenter randomized controlled trial, 140 myogenic artifact, 48, 58, 59, 61 NEAs. See nonepileptic attacks neuroprosthetics, 170–172 NeuroVista™ device, 168 NFLE. See nocturnal frontal lobe epilepsy nocturnal frontal lobe epilepsy (NFLE), 121 nocturnal home pulse oximetry, 136–137 nocturnal seizures, 121 detection devices, 145–146 nocturnal video recordings, 144–145 noise, 18, 19 non-EEG-based seizure detection systems, 185 accelerometers, 182–183 ECG sensors, 183 electrodermal activity sensor, 183 skin sensors, 184–185 nonepilepsy symptoms, 127 nonepileptic attacks (NEAs), 118 nonepileptic events, 105 nonphysiologic sources, of artifact, 54, 56–59 normal initial routine scalp EEG recording, 215 notch filter, 28 novel electrodes, 56 Nyquist–Shannon sampling theorem, 21 obstructive sleep apnea (OSA), 131–134, 140 online epilepsy diaries, 185 OSA. See obstructive sleep apnea Oxford Medilog 9000, 7 Oxford Medilog 4–24 recorder, 3 oximetry, 101 PAP therapy. See positive airway pressure therapy parental perception, 111

224 • Index

paroxysmal events, 120–123 characterization of, 105–106 evaluation of, 116 history of, 105 PAT. See peripheral arterial tonometry pathological spikes, 47 PDI. See Proportional Digital Integration pediatric population, indications of aEEG in, 105–108 pediatrics clinical use of aEEG in, 99 indications for aEEG in, 100 technical details, 100–102 periodic artifact, 58 peripheral arterial tonometry (PAT), 198 Phone Oximeter, 148 photoplethysmography (PPG), 148 physiologic sources, of artifact, 59–61 PNEAs. See psychogenic nonepileptic attacks polygraphic EEG, 33 body movements, 35 current systems, 34 electrocardiogram, 35–36 electromyogram, 35 eye movements, 35 initial systems, 33 patient instructions, 34 scalp electrodes, 34–35 system components, 36–39 polysomnogram (PSG), 131, 200 gold standard, 132 in-laboratory, 131 positive airway pressure (PAP) therapy, 132 PPG. See photoplethysmography presurgical evaluations, 93–94 procedural coding, reimbursement issues and, 195–200 ambulatory sleep tests, 199–200 CPT neurodiagnostics terms, 198 levels of CPT codes, 196–197 special EEG tests, 198–199 Proguardian REST™, 184 Proportional Digital Integration (PDI), 143 prosthetics, 170 PSG. See polysomnogram psychogenic nonepileptic attacks (PNEAs), 61, 64, 123 psychogenic origin, nonepileptic events of, 123 quality of life, of epilepsy patients, 179–180

reimbursement issues date and site of service, 200–201 diagnostic coding, 191–195 procedural coding, 195–200 Relative Value Scale Update Committee (RUC), 196 respiratory analysis, 198, 200 respiratory distress, 87 responsive neural stimulator (RNS), 94, 157, 181 rhythmic artifact, 49, 64, 65 RNS. See responsive neural stimulator RUC. See Relative Value Scale Update Committee scalp electrodes, 34 SCOPER system. See Sleep, Cardiac measures, Oximetry, Position, Effort, and Respiration Seizario app, 185 Seizure Advisory System, 181 seizure detection, 29, 145–146 seizure diagnosis, determining, 105 seizure frequency, determining, 107 seizure localization, 107–108 seizure prediction algorithm, 167–170 seizures, 79–82, 126 classification of, 84–86 identification of, 84–86, 117–119 short-term ambulatory EEG (ST-aEEG), 115–127 case report, 124–127 24-hour monitoring, 115–117 interictal EDs, 119–120 management, 123–124 paroxysmal events, 120–123 seizures, 117–119 Shower Power, 185 signal-to-noise ratio (SNR), 19 single-electrode artifact, 47, 52, 55, 65, 68 16 channels epoch recorder, 8, 9 skin sensors, 184–185 Sleep, Cardiac measures, Oximetry, Position, Effort, and Respiration (SCOPER) system, 136 Sleep Time app, 146 sleep-triggered EEG abnormalities, 100, 107 smartphone oximeters, 148 SmartWatch™ (Smart Monitor), 182, 183 snoring apps, 148 SNR. See signal-to-noise ratio Society of Behavioral Sleep Medicine, 140 spells and occasional convulsions, 214–216 spells of uncertain etiology, 208–212 spike detection, 44, 45, 48 split night PSG, 132

Index • 225

ST-aEEG. See short-term ambulatory EEG subscalp electrode system, 181, 182 success rates, aEEG in children, 108–111 surgical evaluations, 93–94 sweat sway artifact, 50 syncope, 127 system reference, 20

unattended technologist, 198 unilateral seizures, 163

TAT. See Time Above Threshold technology push, 186 three-channel aEEG montage, 4, 5 Time Above Threshold (TAT), 143 tolerability of aEEG, 111 tonic–clonic seizures, 81

Watch-PAT, 136 wrist actigraphy, 140

VEM. See video-EEG monitoring vertical eye blink artifact, 51, 59 video-EEG monitoring (VEM), 41, 51, 61, 66 video recording, 24, 29

Zephyr Biopatch™, 184