Handbook of Biochips: Integrated Circuits and Systems for Biology and Medicine 9781441993182, 9781461434474, 9781461435037

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Mohamad Sawan Editor

Handbook of Biochips

Integrated Circuits and Systems for Biology and Medicine

Handbook of Biochips

Mohamad Sawan Editor

Handbook of Biochips Integrated Circuits and Systems for Biology and Medicine

With 810 Figures and 89 Tables

Editor Mohamad Sawan Cutting-edge Net of Biomedical Research And INnovation (CenBRAIN) School of Engineering Westlake University Hangzhou, Zhejiang, China Emeritus Professor, Polystim Neurotech Labs Polytechnique Montreal Montreal, Canada

ISBN 978-1-4419-9318-2 ISBN 978-1-4614-3447-4 (eBook) ISBN 978-1-4614-3503-7 (print and electronic bundle) https://doi.org/10.1007/978-1-4614-3447-4 © Springer Science+Business Media, LLC, part of Springer Nature 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Science+Business Media, LLC part of Springer Nature. The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.

Preface

The integrated circuits (ICs) and systems for biology and medicine are biochips spreading in most sciences and engineering disciplines, fundamental sciences, and applications. These biochips are intended for neuroscience discoveries, biosensing, diagnosis, monitoring, DNA and neurotransmitters detection and manipulation, and for diseases treatment applying electrical/optical stimulation and drug delivery methods. A global view of a typical electronic biochip shows that it is composed of several modules integrated altogether to achieve fruitful functionalities. In fact, many biochips (ICs or dies) can be built around a thin board providing placement flexibility on specific application environment. In addition, biochips introduced for in vitro and ex vivo applications are built around various types of structures and platforms. Some of these devices are based on lab-on-chip (LoC) platforms which can occupy miniaturized board areas or each can be integrated in single IC occupying very large silicon area. High reliability, very low power consumption, reduced volume, and flexibility of these biochips are among the main criteria adopted to implement biosensors and bioactuators, which are promising solutions to numerous pathologies. On the one hand, the advent of micro/nano electronics and microsystems motivated the emergence of novel wearable and implantable biochips intended for introducing various smart medical devices (SMD). A typical implantable SMD is wirelessly powered and can be a biochip intended for neurorecording and/or electrical/optical stimulation. Also, usually it is composed of a power receiver block, an external controller, and implantable communication modules for the exchange of power and encoded data over the electromagnetic transmitted wave. In addition, the SMD includes front- and back-end stages, a digital control unit, as well as on-chip memory. For the external controller, it is intended to provide instructions and stimulation parameters (downlink) to its corresponding implantable part. On the other hand, the monitored and recorded neuromuscular signals and implant’s status are sampled and sent to the external controller (uplink) for further processing. Currently SMDs are used to enhance vital functions such as pacemakers and defibrators for cardiovascular irregularities, intracortical stimulators to recover vision for the blind, other brain stimulators for brain diseases like Parkinson and epilepsy, and cochlear implants to recover hearing function to profoundly deaf v

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Preface

persons. Also, many other devices dedicated for respiration and leg and hand movements are available. In addition, major research activities are being conducted to recover urinary bladder functions (voiding and to prevent incontinence). Several unique attractive features characterize this handbook. It is intended to bring to readers state-of-the-art materials in main types of biochips. However, we focus on the following five major parts: Biosensing Technologies; Multi-chip Smart Neuroprosthesis; Lab-on-Chip (LoC) for Diagnosis, Monitoring, and Drug Delivery; Telemetry and Other Wireless Link–Related Biochips; and Microstimulators. These parts are composed of 61 chapters. Nevertheless, it is important to notice that covering all categories of biochips with details may require several handbooks. The benefit of this handbook is to have a state-of-the-art reference describing main emerging/booming research topics with clear analysis of relation between different categories of biochips and interactions with the body including wireless remote control and array of microelectrodes based on new biomaterials. In this handbook, we focus on the most currently conducted research activities in biochips. Consequently, part 1 titled Biosensing Technologies, which includes 15 chapters, describes the latest results in biosensors such as blood pressure, biopotentials, neurorecording, optical neural interfaces, and biosensors of different vital signs. Part 2 titled Multi-chip Smart Neuroprosthesis, which consists of 9 chapters, concerns artificial olfactory systems, closed-loop devices, brain-computer interfaces, and visual stimulation systems. Part 3, which contains 11 chapters, is about lab-on-chip devices for diagnosis, monitoring, and drug delivery. Some chapters in this part cover DNA detection, capacitive cell sensing, glucose monitoring, nuclear magnetic resonance, optical biosensing, porous silicon-based sensing, and other brain-on-a-chip devices. In part 4 titled Telemetry and Wireless Link–Related Biochips, there are 18 chapters sharing the latest research intended to improve the capacitive, inductive, and optical links to wirelessly transmit data and power to various biochip-based applications such as Doppler radar sensor platform, intracortical brain-machine interfaces, wireless capsule navigation within the body, healthy radios, and security and innovation protection of biochips. In part 5 we focus mainly on microstimulators; eight chapters in this part cover retinal and subretinal visual prostheses, a foot-drop stimulator, endocardial stimulation system, etc. Search and analysis of prior article publications related to topics of these chapters give that parts 1–5 share 37%, 11%, 28%, 17%, and 7%, respectively. However, topics in part 1 are ECG monitoring 18%, blood pressure monitoring 17%, multichannel neurorecording 10%, biopotential amplifiers 9%, and peripheral nerve sensors 8%, and several other topics share the remaining 38% of literatures. Topics in part 2 are retinal visual systems 37%, brain-computer interfaces 20%, closed-loop neural systems 14%, and neuromodulation for epilepsy 9%, and a few other topics share the remaining 20% of related literatures. Published results from part 3 are various probes for biosensing 35%; optical bioanalysis 25%; and lab, body, and brain-on-chip devices 18%; and other topics share the remaining 22%. Topics in part 4 are power and data telemetry 33%, ultralow power transmitters 16%, and optical platforms 15%, and many other areas share the remaining 36%. Finally subjects sharing the content of part 5 are sub-retinal stimulator 30%, multichannel

Preface

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microstimulation ICs 22%, food-drop stimulator 9%, electroencephalogram-based projects 9%, and all other related topics share the remaining 30%. The numerous chapters of various parts are the result of huge efforts spent by a truly international group of experts to offer the best landscape in the field of biochips. I feel very much indebted to every author for the invaluable dedication to keep the level of this handbook at a high standard. I wish to thank them for choosing to publish a contribution to this handbook. Due to their individual contribution, I managed to assemble this handbook of high scientific quality. It has been gratifying to learn more about the advances provided by every author. Also, I would like to thank the numerous volunteers that helped to promote the handbook, and to locate and invite the authors. Thanks are due to Sumin Bian, Jie Yang, and Yitian (Claire) Zhang members of my group in Westlake University, and Abbas Hammoud and Hussein Assaf from Polystim Neurotech of Polytechnique Montreal. Finally, I owe my deepest thanks to Springer personnel, in particular Lydia Mueller, Charles Glaser, Sunali Mull, and Akshara PP, who gave me the opportunity to edit this handbook, and for the constant support provided to achieve this; this handbook of biochips is only possible with their expert help. Hangzhou, China Montreal, Canada January 2022

Mohamad Sawan, Ph.D. Chair Professor Emeritus Professor Editor

Contents

Volume 1 Part I

Wearable and Implantable Biosensing Technologies

......

1

1

Bladder Control Implants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shuenn-Yuh Lee and Chen-Yueh Huang

3

2

Development and Evaluation of a Continuous Blood Pressure Monitoring System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Toshiya Arakawa, Noriaki Sakakibara, and Shinji Kondo

21

3

Electrical Biosensors: Biopotential Amplifiers . . . . . . . . . . . . . . . . Fan Zhang, Tan Yang, Jeremy Holleman, and Brian Otis

41

4

Electrical Biosensors: Peripheral Nerve Sensors Clemens Eder and Andreas Demosthenous

..............

65

5

Impedance Spectroscopy for Biosensing: Circuits and Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marco Carminati, Giorgio Ferrari, Davide Bianchi, and Marco Sampietro

6

Low-Power Design Technique for Multichannel Neural Recording Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wen-Sin Liew and Yong Lian

7

On-Skin Chemical Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bowen Zhu

8

Optical Biosensors: Implantable Multimodal Devices in Freely Moving Rodents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jun Ohta, Kiyotaka Sasagawa, and Makito Haruta

9

Optical Interfacing of Neuronal Activity . . . . . . . . . . . . . . . . . . . . Qiantao Lv, Dandan Chen, Jing Ning, Xingjiang Zhang, and Yi Sun

87

111 129

143 159

ix

x

Contents

10

Optogenetic Implants Hubin Zhao

...................................

179

11

Sensors for Vital Signs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. M. Rossi and S. Annaheim

207

12

Sensors for Vital Signs: ECG Monitoring Systems Sameer Sonkusale

............

221

13

Sensors for Vital Signs: Humidity Sensors . . . . . . . . . . . . . . . . . . . Wagner Coimbra and Arnaldo Leal-Junior

245

14

Sensors for Vital Signs: Micro-Ball Wireless Endoscopic Capsules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yingke Gu, Xiang Xie, Guolin Li, and Zhihua Wang

15

Sensors for Vital Signs: Oxygen Sensors . . . . . . . . . . . . . . . . . . . . K. N. Glaros, M. L. Rogers, M. G. Boutelle, and E. M. Drakakis

Part II

Multi-Chip Smart Neuroprosthesis . . . . . . . . . . . . . . . . . . . .

263 291

315

16

AC Electrokinetics-Enhanced Capacitive Virus Detection . . . . . . . Cheng Cheng and Jayne Wu

317

17

Artificial Olfactory Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amine Bermak, Muhammad Hassan, and Xiaofang Pan

343

18

Closed-Loop Bidirectional Neuroprosthetic Systems . . . . . . . . . . . Kea-Tiong (Samuel) Tang, Hsin Chen, and Yu-Po Lin

363

19

Closed-Loop Neuromodulation System-on-Chip (SoC) for Detection and Treatment of Epilepsy . . . . . . . . . . . . . . . . . . . . . . . Ming-Dou Ker and Cheng-Hsiang Cheng

383

20

Closed-Loop/Bidirectional Neuroprosthetic Systems . . . . . . . . . . . Gabriel Gagnon-Turcotte, Olivier Tsiakaka, Guillaume Bilodeau, and Benoit Gosselin

21

Current-Based Neurostimulation Circuit and System Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rohollah Shirafkan and Omid Shoaei

445

Immunoreaction-Based Sensors to Improve Bacterial Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Huilin Zhang, Nanjia Zhou, and Feng Ju

471

Implantable Brain-Computer Interfaces for Monitoring and Treatment of Neurological Disorders . . . . . . . . . . . . . . . . . . . . . . . Hossein Kassiri and Roman Genov

487

22

23

413

Contents

24

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Visual Stimulation Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Torsten Lehmann, Louis H. Jung, Gregg J. Suaning, and Nigel H. Lovell

Part III Lab-on-Chip (LoC) for Diagnosis, Monitoring, and Drug Delivery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

517

541

25

Capacitive DNA Hybridization Detection . . . . . . . . . . . . . . . . . . . . Michael S.-C. Lu

543

26

CMOS Capacitance Biosensors to Monitor Cell Viability . . . . . . . Bathiya Senevirathna and Pamela Abshire

553

27

Continuous Glucose Monitoring Sensors for Management of Diabetes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sara Ghoreishizadeh and Sanjiv Sharma

571

28

DNA Optical Readout Methods Takashi Tokuda and Jun Ohta

...........................

589

29

Fabrication of Brain-on-a-Chip Devices . . . . . . . . . . . . . . . . . . . . . Jessica K. Lu, Pramila Ghode, and Nitish V. Thakor

601

30

Molecular Analysis: BioFET Detection Sensors . . . . . . . . . . . . . . . Pedro Estrela

631

31

Next-Generation DNA Sequencing: Ion Torrent Sequencers Versus Nanopore Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chaker Tlili, Khouloud Djebbi, Mohamed Amin Elaguech, Mohamed Bahri, Daming Zhou, Biao Shi, and Deqiang Wang

651

32

On-Chip Nuclear Magnetic Resonance . . . . . . . . . . . . . . . . . . . . . . Jens Anders, Frederik Dreyer, and Daniel Krüger

667

33

Optical Detection Techniques for Bioanalysis . . . . . . . . . . . . . . . . . Hamza Landari, Mounir Boukadoum, Younès Messaddeq, and Amine Miled

699

34

Porous Silicon-Based Biosensors . . . . . . . . . . . . . . . . . . . . . . . . . . . Guoguang Rong, Sumin Bian, and Mohamad Sawan

711

35

Sensing and Sampling Probes for Bioapplications . . . . . . . . . . . . . Amine Miled, Hamza Landari, Mounir Boukadoum, and Younés Messaddeq

731

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Contents

Volume 2 Part IV

Telemetry and Other Wireless Link–Related Biochips . . . .

36

Backscatter Communication for Biomedical Devices . . . . . . . . . . . Aida Aberra, Young-Han Kim, Minkyu Je, and Sohmyung Ha

37

Capacitive Links for Power and Data Telemetry to Implantable Biomedical Microsystems . . . . . . . . . . . . . . . . . . . . . . Mohammad A. Sharif and Amir M. Sodagar

743 745

763

38

Capsule-Based Measurements of Gastrointestinal Impedance . . . . Gang Wang, Dobromir Filip, Michael D. Poscente, Christopher N. Andrews, and Martin P. Mintchev

785

39

Chip-Based MEMS for Healthcare Application . . . . . . . . . . . . . . . Jae Sun Lee, Rajamanickam Sivakumar, and Nae Yoon Lee

803

40

Design Considerations of Frequency Modulated Ultralow Power Transmitter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xing Chen and David D. Wentzloff

815

41

Doppler Radar Sensor Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . Herman Jalli Ng

845

42

Intelligent Intracortical Brain-Machine Interfaces . . . . . . . . . . . . . Shoeb Shaikh and Arindam Basu

869

43

Optical Biosensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wenhao Zhao, Lei Huang, Ke Liu, Jiuchuan Guo, and Jinhong Guo

891

44

Radiotelemetry for Epileptiform Activity in Freely Moving Rats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Abdollah Mirbozorgi

907

45

Security and Innovation Protection of Biochips . . . . . . . . . . . . . . . Chen Dong, Ximeng Liu, Yi Xu, and Sihuang Lian

927

46

Wireless Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Byunghun Lee and Hyung-Min Lee

949

47

Wireless Applications: Dual Band Power and Data Telemetry Anil Kumar RamRakhyani and Gianluca Lazzi

...

967

48

Wireless Applications: Inductive Links for Power and Data Telemetry to Medical Implants . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shirin Pezeshkpour and Mohammad Mahdi Ahmadi

985

49

Wireless Capsule Design and Its Locomotion and Navigation Within the Body . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1013 Fahad N. Alsunaydih, Muhammad A. Ali, and Mehmet R. Yuce

Contents

xiii

50

Wireless Circuits and Systems: Energy-Neutral Links . . . . . . . . . . 1037 Yaoyao Jia and Maysam Ghovanloo

51

Wireless Circuits and Systems: FM Telemetry Devices . . . . . . . . . 1063 Chin-Lung Yang

52

Wireless Circuits and Systems: Healthy Radios . . . . . . . . . . . . . . . 1087 Ziyi Chang and Bo Zhao

53

Wireless Power Transfer, Recovery, and Data Telemetry for Biomedical Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1107 Ashraf Bin Islam, Daniel Costinett, and Syed Kamrul Islam

Part V

Microstimulators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1129

54

A Flexible Software-Hardware Framework for Brain EEG Multiple Artifact Identification . . . . . . . . . . . . . . . . . . . . . . . 1131 Mohit Khatwani, Hasib-Al Rashid, Hirenkumar Paneliya, Mark Horton, Houman Homayoun, Nicholas Waytowich, W. David Hairston, and Tinoosh Mohsenin

55

Adiabatic Electrode Stimulator Shawn K. Kelly

56

Biphasic Current Stimulator for Retinal Prosthesis . . . . . . . . . . . . 1185 Jeong Hoan Park, Han Wu, Joanne Si Ying Tan, and Jerald Yoo

57

Electronic Platforms and Signal Processing for Magnetoresistive-Based Biochips . . . . . . . . . . . . . . . . . . . . . . . . . . 1201 José Germano, Tiago Costa, Filipe A. Cardoso, José Amaral, Susana Cardoso, Paulo P. Freitas, and Moisés S. Piedade

58

Foot Drop Stimulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1241 Dejan B. Popović

59

Microstimulator for Endocardial Stimulation Shuenn-Yuh Lee and Mario Yucheng Su

60

Multichannel Microstimulating SoC . . . . . . . . . . . . . . . . . . . . . . . . 1285 Emilia Noorsal, Hongcheng Xu, Kriangkrai Sooksood, and Maurits Ortmanns

61

Subretinal Neurostimulator for Vision . . . . . . . . . . . . . . . . . . . . . . 1317 Naser Pour Aryan and Albrecht Rothermel

. . . . . . . . . . . . . . . . . . . . . . . . . . . 1157

. . . . . . . . . . . . . . . . 1257

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1337

About the Editor

Mohamad Sawan received his Ph.D. degree from the University of Sherbrooke, Canada. He is currently Chair Professor in Westlake University, Hangzhou, China, and Emeritus Professor in Polytechnique Montreal, Canada. He is founder and director of the Cutting-Edge Net of Biomedical Research And INnovation (CenBRAIN) in Westlake University, Hangzhou, China. He founded the Polystim Neurotech Laboratory, and the Eastern Canadian IEEE-Solid State Circuits Society Chapter. He is cofounder of the International Functional Electrical Stimulation Society, the International IEEE-NEWCAS, and the International IEEE-BioCAS Conferences. Also, he is Co-Founder, Associate Editor, member of the steering committee, and Editor-in-Chief of the IEEE Transactions on Biomedical Circuits and Systems (TBioCAS) (2016–2019). He is Associate Editor of the IEEE Transactions on Biomedical Engineering (TBME). He was Deputy Editor-in-Chief of the IEEE Transactions on Circuits and Systems II (TCAS-II) and Editor and Associate Editor of several other international journals such as the Springer Mixed Signal Letters. Dr. Sawan is member of the board, Editor, Guest Editor, and Associate Editor of several other prestigious scientific journals. He is cofounder of several other IEEE International conferences such as IEEE-ICECS and IEEE-ICM. He was General Chair of both the 2016 IEEE International Symposium on Circuits and Systems and the 2020 IEEE International Medicine, Biology and Engineering Conference (EMBC). He was awarded the Canada Research Chair in Smart Medical Devices (2001–2015) and was leading the Microsystems Strategic Alliance of Quebec (ReSMiQ), Canada (1999–2018), receiving membership support xv

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About the Editor

from 11 universities. His scientific interests are the design and implementation of hybrid and mixed-signal (analog, digital, RF, MEMS, and optic) circuits and microsystems: integration, assembly, and validations. These topics are mainly oriented toward the biomedical fundamental and applied sciences. Dr. Sawan has published more than 900 peer-reviewed papers, 2 books, 13 book chapters, and 12 patents, and 15 other patents are pending, and has offered around 300 invited talks pertaining to the field of biomedical engineering. He has received several awards, among them the Zhejiang Westlake Friendship Award, the Qianjiang Friendship Ambassador Award, the Shanghai International Collaboration Award, the Queen Elizabeth II Golden Jubilee Medal, and the Medal of Merit from the President of Lebanon, the J.A. Bombardier and Jacques-Rousseau ACFAS Awards for technology transfer and research contributions, the Barbara Turnbull Award for medical research in Canada, and the Achievement Award from the American University of Science and Technology. Dr. Sawan is Fellow of the IEEE, Fellow of the Canadian Academy of Engineering, Fellow of the Engineering Institutes of Canada, and “Officer” of the National Order of Quebec.

Contributors

Aida Aberra Department of Electrical and Computer Engineering, Tandon School of Engineering, New York University, Brooklyn, NY, USA Pamela Abshire Department of Electrical and Computer Engineering and the Institute for Systems Research, University of Maryland, College Park, MD, USA Mohammad Mahdi Ahmadi Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran Muhammad A. Ali Electrical and Computer Systems Engineering, Monash University, Melbourne, VIC, Australia Fahad N. Alsunaydih Electrical and Computer Systems Engineering, Monash University, Melbourne, VIC, Australia José Amaral INESC-Microsistemas e Nanotecnologias, Lisboa, Portugal Jens Anders Institute of Smart Sensors, University of Stuttgart, Stuttgart, Germany Christopher N. Andrews Division of Gastroenterology, Faculty of Medicine, University of Calgary, Calgary, AB, Canada S. Annaheim Laboratory for Biomimetic Membranes and Textiles, Empa, Swiss Federal Laboratories for Materials Science and Technology, St. Gallen, Switzerland Toshiya Arakawa Department of Mechanical Systems Engineering, Aichi University of Technology, Gamagori-city, Aichi, Japan Mohamed Bahri Chongqing Key Laboratory of Multi-scale Manufacturing Technology, Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, People’s Republic of China University of Chinese Academy of Sciences (UCAS), Beijing, People’s Republic of China Arindam Basu School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

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Contributors

Amine Bermak Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, China Sumin Bian CenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou, China Davide Bianchi Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy Guillaume Bilodeau Department of Electrical and Computer Engineering, Laval University, Quebec City, QC, Canada Mounir Boukadoum Computer Science Department, Université du Québec À Montréal (UQÀM), Montréal, QC, Canada M. G. Boutelle Department of Bioengineering, Imperial College, London, UK Filipe A. Cardoso INESC-Microsistemas e Nanotecnologias, Lisboa, Portugal Susana Cardoso INESC-Microsistemas e Nanotecnologias, Lisboa, Portugal Instituto Superior Técnico (IST), Universidade de Lisboa, Lisboa, NY, Portugal Marco Carminati Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy Ziyi Chang Institute of VLSI Design, College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China Dandan Chen Key Laboratory of Growth Regulation and Translation Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China Hsin Chen Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan Xing Chen Qualcomm, San Diego, CA, USA Cheng Cheng School of Engineering and Computer Science, Morehead State University, Morehead, KY, USA Cheng-Hsiang Cheng Biomedical Electronics Translational Research Center, National Yang Ming Chiao Tung University, Hsinchu City, Taiwan Wagner Coimbra Mechanical Engineering Department, Federal University of Espírito Santo, Vitória, Brazil Tiago Costa Columbia University, New York, NY, USA INESC-Investigação e Desenvolvimento, Lisboa, NY, Portugal

Contributors

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Daniel Costinett Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN, USA Andreas Demosthenous Department of Electronic and Electrical Engineering, University College London, London, UK Khouloud Djebbi Chongqing Key Laboratory of Multi-scale Manufacturing Technology, Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, People’s Republic of China University of Chinese Academy of Sciences (UCAS), Beijing, People’s Republic of China Chen Dong College of Mathematics and Computer Science, Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Ministry of Education, Fuzhou University, Fuzhou, China E. M. Drakakis Department of Bioengineering, Imperial College, London, UK Frederik Dreyer Institute of Smart Sensors, University of Stuttgart, Stuttgart, Germany Clemens Eder Department of Electronic and Electrical Engineering, University College London, London, UK Mohamed Amin Elaguech Chongqing Key Laboratory of Multi-scale Manufacturing Technology, Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, People’s Republic of China University of Chinese Academy of Sciences (UCAS), Beijing, People’s Republic of China Pedro Estrela Department of Electronic and Electrical Engineering, University of Bath, Bath, UK Giorgio Ferrari Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy Dobromir Filip Department of Electrical and Computer Engineering, University of Calgary, Engineering Complex, Calgary, AB, Canada Paulo P. Freitas INESC-Microsistemas e Nanotecnologias, Lisboa, Portugal INL- International Iberian Nanotechnology Laboratory, Braga, Portugal Gabriel Gagnon-Turcotte Department of Electrical and Computer Engineering, Laval University, Quebec City, QC, Canada Roman Genov Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada José Germano INESC-Investigação e Desenvolvimento, Lisboa, Portugal

xx

Contributors

Pramila Ghode The N.1 Institute for Health, National University of Singapore, Singapore, Singapore Sara Ghoreishizadeh Aspire CREATe, University College London, London, UK Maysam Ghovanloo Bionic Sciences Inc., Atlanta, GA, USA K. N. Glaros Department of Bioengineering, Imperial College, London, UK Benoit Gosselin Department of Electrical and Computer Engineering, Laval University, Quebec City, QC, Canada Yingke Gu Institute of Microelectronics, Tsinghua University, Beijing, China Jinhong Guo School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, P. R. China Jiuchuan Guo School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, P. R. China Sohmyung Ha Engineering, New York University Abu Dhabi, Abu Dhabi, UAE W. David Hairston Human Research and Engineering Directorate, US Army Research Lab, Adelphi, MD, USA Makito Haruta Division of Materials Science, Nara Institute of Science and Technology (NAIST), Ikoma, Japan Muhammad Hassan Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, China Jeremy Holleman Electrical and Computer Engineering, University of North Carolina, Charlotte, Charlotte, NC, USA Houman Homayoun University of California, Davis, Davis, CA, USA Mark Horton Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, USA Chen-Yueh Huang Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan Lei Huang School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, P. R. China Ashraf Bin Islam Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN, USA Syed Kamrul Islam Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN, USA Minkyu Je Korea Advanced Institute of Science and Technology, Daejeon, South Korea

Contributors

xxi

Yaoyao Jia Department of Electrical and Computer Engineering, NC State University, Raleigh, NC, USA Feng Ju Division of Environment and Resources, School of Engineering, Westlake University, Hangzhou, China Division of Environment and Resources, Westlake University, Hangzhou, China Louis H. Jung Samsung Electronics, Seoul, South Korea Hossein Kassiri York University, Toronto, ON, Canada Shawn K. Kelly VA Pittsburgh Healthcare System, Pittsburgh, PA, USA Institute for Complex Engineered Systems, Carnegie Mellon University, Pittsburgh, PA, USA Ming-Dou Ker Biomedical Electronics Translational Research Center, National Yang Ming Chiao Tung University, Hsinchu City, Taiwan Mohit Khatwani Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, USA Young-Han Kim Smart Network Research Center, Korea Electronics Technology Institute, Seoul, South Korea Shinji Kondo KANDS Inc., Kariya-city, Aichi, Japan Daniel Krüger Institute of Smart Sensors, University of Stuttgart, Stuttgart, Germany Harvard University, School of Engineering and Applied Sciences, Cambridge, MA, USA Hamza Landari Research Centre for Advanced Materials (CERMA), LABioTRON Bio-engineering Research Laboratory, Québec City, QC, Canada Department of Electrical and Computer Engineering, Université Laval, Québec City, QC, Canada Gianluca Lazzi Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, USA Arnaldo Leal-Junior Mechanical Engineering Department, Federal University of Espírito Santo, Vitória, Brazil Byunghun Lee Department of Electrical Engineering, Incheon National University, Incheon, South Korea Hyung-Min Lee School of Electrical Engineering, Korea University, Seoul, South Korea Jae Sun Lee Gachon BioNano Research Institute, Gachon University, Seongnamsi, Gyeonggi-do, South Korea

xxii

Contributors

Nae Yoon Lee Department of BioNano Technology, Gachon University, Seongnam-si, Gyeonggi-do, South Korea Shuenn-Yuh Lee Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan Torsten Lehmann School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, Australia Guolin Li Institute of Microelectronics, Tsinghua University, Beijing, China Sihuang Lian College of Mathematics and Computer Science, Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou University, Fuzhou, China Yong Lian Department of Electrical Engineering and Computer Science of Lassonde School of Engineering, York University, Toronto, Canada Wen-Sin Liew Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore Yu-Po Lin Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan Ke Liu School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, P. R. China Ximeng Liu College of Mathematics and Computer Science, Key Lab of Information Security of Network Systems (Fujian Provincial), Ministry of Education, Fuzhou University, Fuzhou, China Nigel H. Lovell Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW, Australia Jessica K. Lu The N.1 Institute for Health, National University of Singapore, Singapore, Singapore Department of Biomedical Engineering, Singapore Institute for Neurotechnology, National University of Singapore, Singapore, Singapore Michael S.-C. Lu Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan, Republic of China Institute of Electronics Engineering, National Tsing Hua University, Hsinchu, Taiwan, Republic of China Institute of NanoEngineering and MicroSystems, National Tsing Hua University, Hsinchu, Taiwan, Republic of China Qiantao Lv Key Laboratory of Growth Regulation and Translation Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China

Contributors

xxiii

Younès Messaddeq Center for Optics, Photonics and Lasers (COPL), Laval University, Québec City, QC, Canada Amine Miled Research Centre for Advanced Materials (CERMA), LABioTRON Bio-engineering Research Laboratory, Québec City, QC, Canada Department of Electrical and Computer Engineering, Université Laval, Québec City, QC, Canada Martin P. Mintchev Centre for Bioengineering and Research, University of Calgary, Engineering Complex, Calgary, AB, Canada Department of Electrical and Computer Engineering, University of Calgary, Engineering Complex, Calgary, AB, Canada Department of Surgery, Faculty of Medicine, University of Alberta, Edmonton, AB, Canada S. Abdollah Mirbozorgi Electrical and Computer Engineering Department, University of Alabama at Birmingham, Birmingham, AL, USA Tinoosh Mohsenin Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, USA Herman Jalli Ng Faculty of Electrical Engineering and Information Technology, Karlsruhe University of Applied Sciences, Karlsruhe, Germany Jing Ning Key Laboratory of Growth Regulation and Translation Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China Emilia Noorsal Faculty of Electrical Engineering, Universiti Teknology MARA, Pulau Pinang, Malaysia Jun Ohta Division of Materials Science, Graduate School of Materials Science, Nara Institute of Science and Technology (NAIST), Ikoma, Nara, Japan Maurits Ortmanns Institute of Microelectronics, University of Ulm, Ulm, Germany Brian Otis Low Power Chip Design. Wireless Biosensors. Electrical Engineering, University of Washington, Seattle, WA, USA Xiaofang Pan Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, China Hirenkumar Paneliya Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, USA Jeong Hoan Park Samsung Electronics, Hwaseong, Republic of Korea Shirin Pezeshkpour Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

xxiv

Contributors

Moisés S. Piedade INESC-Investigação e Desenvolvimento, Lisboa, Portugal Instituto Superior Técnico (IST), Universidade de Lisboa, Lisboa, NY, Portugal Dejan B. Popović Biomedical Engineering, Faculty of Electrical Engineering, University of Belgrade, Belgrade, Serbia Michael D. Poscente Centre for Bioengineering and Research, University of Calgary, Engineering Complex, Calgary, AB, Canada Naser Pour Aryan Continental Engineering Services GmbH, Ingolstadt, Germany Anil Kumar RamRakhyani Department of Electrical and Computer Engineering, College of Engineering, University of Utah, Salt Lake City, UT, USA Hasib-Al Rashid Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, USA M. L. Rogers Department of Bioengineering, Imperial College, London, UK Guoguang Rong CenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou, China R. M. Rossi Laboratory for Biomimetic Membranes and Textiles, Empa, Swiss Federal Laboratories for Materials Science and Technology, St. Gallen, Switzerland Albrecht Rothermel Institute of Microelectronics, University of Ulm, Ulm, Germany Noriaki Sakakibara KANDS Inc., Kariya-city, Aichi, Japan Marco Sampietro Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy Kiyotaka Sasagawa Division of Materials Science, Nara Institute of Science and Technology (NAIST), Ikoma, Japan Mohamad Sawan Cutting-edge Net of Biomedical Research And INnovation (CenBRAIN), School of Engineering, Westlake University, Hangzhou, Zhejiang, China Emeritus Professor, Polystim Neurotech Labs, Polytechnique Montreal, Montreal, Canada Bathiya Senevirathna Department of Electrical and Computer Engineering and the Institute for Systems Research, University of Maryland, College Park, MD, USA Shoeb Shaikh School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore Mohammad A. Sharif Faculty of Electrical Engineering (EE), K.N. Toosi University of Technology, Research Laboratory for Integrated Circuits and Systems (ICAS), Tehran, Iran

Contributors

xxv

Sanjiv Sharma Faculty of Science and Engineering, Swansea University, Swansea, UK Biao Shi Chongqing Key Laboratory of Multi-scale Manufacturing Technology, Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, People’s Republic of China Rohollah Shirafkan Bio-Integrated Systems Lab, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran Omid Shoaei Bio-Integrated Systems Lab, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran Rajamanickam Sivakumar Department of Industrial Environmental Engineering, Gachon University, Seongnam-si, Gyeonggi-do, South Korea Amir M. Sodagar Faculty of Electrical Engineering (EE), K.N. Toosi University of Technology, Research Laboratory for Integrated Circuits and Systems (ICAS), Tehran, Iran Sameer Sonkusale School of Engineering, Tufts University, Medford, MA, USA Kriangkrai Sooksood Department of Electronic Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand Mario Yucheng Su Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan Gregg J. Suaning University of New South Wales, Sydney, NSW, Australia Yi Sun Key Laboratory of Growth Regulation and Translation Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China Joanne Si Ying Tan National University of Singapore, Singapore, Singapore Kea-Tiong (Samuel) Tang Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan Nitish V. Thakor The N.1 Institute for Health, National University of Singapore, Singapore, Singapore Department of Biomedical Engineering, Singapore Institute for Neurotechnology, National University of Singapore, Singapore, Singapore Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA Chaker Tlili Chongqing Key Laboratory of Multi-scale Manufacturing Technology, Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, People’s Republic of China

xxvi

Contributors

Takashi Tokuda Graduate School of Materials Science, Nara Institute of Science and Technology, Ikoma, Nara, Japan Olivier Tsiakaka Department of Electrical and Computer Engineering, Laval University, Quebec City, QC, Canada Deqiang Wang Chongqing Key Laboratory of Multi-scale Manufacturing Technology, Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, People’s Republic of China University of Chinese Academy of Sciences (UCAS), Beijing, People’s Republic of China Gang Wang Centre for Bioengineering and Research, University of Calgary, Engineering Complex, Calgary, AB, Canada Zhihua Wang Institute of Microelectronics, Tsinghua University, Beijing, China Nicholas Waytowich Human Research and Engineering Directorate, US Army Research Lab, Adelphi, MD, USA David D. Wentzloff EECS department, University of Michigan, Ann Arbor, MI, USA Han Wu National University of Singapore, Singapore, Singapore Jayne Wu Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN, USA Xiang Xie Institute of Microelectronics, Tsinghua University, Beijing, China Hongcheng Xu Advanced Low Power Solutions, Texas Instruments Deutschland GmbH, Freising, Germany Yi Xu College of Mathematics and Computer Science, Key Lab of Information Security of Network Systems (Fujian Provincial), Ministry of Education, Fuzhou University, Fuzhou, China Tan Yang Analog Devices, Raleigh, NC, USA Chin-Lung Yang Wireless Innovative System and EM-Applied (WISE) Lab, Instrumentation System and Chip Group, Department of Electrical Engineering, National Cheng Kung University, Tainan City, Taiwan Jerald Yoo National University of Singapore, Singapore, Singapore The N.1 Institute for Health, Singapore, Singapore Mehmet R. Yuce Electrical and Computer Systems Engineering, Monash University, Melbourne, VIC, Australia Fan Zhang Marvell Semiconductor, Santa Clara, CA, USA Huilin Zhang Division of Environment and Resources, School of Engineering, Westlake University, Hangzhou, China

Contributors

xxvii

Xingjiang Zhang Key Laboratory of Growth Regulation and Translation Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China Bo Zhao Institute of VLSI Design, College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China Hubin Zhao Department of Medical Physics and Biomedical Engineering, University College London, London, UK Wenhao Zhao School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, P. R. China Daming Zhou Chongqing Key Laboratory of Multi-scale Manufacturing Technology, Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, People’s Republic of China Nanjia Zhou Division of Nanotechnology and Energy, School of Engineering, Westlake University, Hangzhou, China Bowen Zhu School of Engineering, Westlake University, Hangzhou, China

Part I Wearable and Implantable Biosensing Technologies

1

Bladder Control Implants Shuenn-Yuh Lee and Chen-Yueh Huang

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Neural Anatomy and Normal Bladder Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stimulation Mode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stimulus Pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Blocking Capacitors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Magnitude of Current for Peripheral Nerve Stimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Electrode and Contact Impedance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stimulation Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Digital-to-Analog Converter Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stimulus Generation Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FPGA Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4 4 7 7 8 10 10 11 11 14 15 16 20 20

Abstract

This chapter presents a system of bladder neuromodulation and a method for the control of the variable burst biphasic pulse of a bladder stimulator. The stimulator is used to pass current through the tissue and to generate useful action potentials. The binary-weighted digital-to-analog converter combined with a current mirror has been employed as a microstimulator because of its higher linearity without requiring the decoding of digital inputs. Two algorithms including burst pulse generation algorithm and slow reversal with interphase delay pulse generation algorithm are present. Given that the use of a biphasic pulse could prevent ion-charge accumulation in tissues, two pairs of switches controlled by different clock phases are implemented to provide the biphasic S.-Y. Lee (*) · C.-Y. Huang Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan e-mail: [email protected]; [email protected] © Springer Science+Business Media, LLC, part of Springer Nature 2022 M. Sawan (ed.), Handbook of Biochips, https://doi.org/10.1007/978-1-4614-3447-4_34

3

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S.-Y. Lee and C.-Y. Huang

electrical stimulation pulses. The presented method has been verified on FPGA implementation to demonstrate the proposed algorithms which is helpful for the future implementation in the integrated circuits. In this study, the pulse frequency can be programmed between 1.49 and 47.66 Hz, the burst frequency can be controlled from 190.8 to 763 Hz, and the pulse width can be adjusted between 21 and 325 μs. These stimulation parameters are adapted by the clock divider and by the number of controlled bits in the digital circuits. In the future, the microstimulator with controlled algorithm can be integrated with power interface and sensing channel as an implantable device for animal study.

Introduction Microstimulators are neurosurgical devices that can be used in medical treatment, rehabilitation, and neuron control. Figure 1 shows that many different organs can be treated by developed microstimulators, such as deep brain stimulation, pacemaker, nerve and gastric stimulation, and bladder controller.

Neural Anatomy and Normal Bladder Function Figure 2 displays the proposed closed-loop bladder microstimulator. Lee et al. (2011) posited that microstimulators can be minimized as system on a chip

Fig. 1 Application of implanted stimulator

Vg

M0

C1 L1

C2

Lchoke

d

Internal Circuitry

8-b serial conversion data

LSK Mod ADC 2nd-stage Amplifier

System Controller PG

Stimulator

Preamplifier

Pudendal Nerve Trunk

High-Voltage Supply

Sensing Channel

D/A Controller

Low-Voltage Supply

OSC

Charge Pump

BP Filter with Buffer

Supplying Detector

Digital Circuitry

Battery

Powering Interface Charger Regulator

AD Converter / Detection

PSK Dem

Charging Detector

Rectifier

Detection flag

L2 Cres

Fig. 2 Application of implanted stimulator

External Power / Data Transmitter

Data Encoder

iL

VDD

Skin

Deep Perineal Nerve Urethra

EUS

Bladder

1 Bladder Control Implants 5

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S.-Y. Lee and C.-Y. Huang

(SoC) to meet the requirements of implantable devices, programmable stimulation parameters, and low power. An external device encodes the stimulus parameters decided by physicians. These parameters are then stored in packets and modulated by phase-shift keying (PSK). The data on the stimulus parameters are subsequently transmitted to the human body to update the stimulation parameters in SoC. A regulator and supplying detector provide a stable voltage for all internal circuits. In support of PSK demodulation, a system controller is designed to decode the information. The sensing channel captures the neural signals to monitor the pressure of the bladder and then provides a potential binary value for bladder neuromodulation. Finally, according to the command from the system controller, a digital-to-analog converter (DAC) and a pulse generator provide the correct burst pulse for stimulation. The clinical, sympathetic, parasympathetic, and somatic nervous systems dominate the entire urinary system. Figure 3 signifies that the afferent nerve transmits the message to the spinal cord from S2 to S4 sections and feedbacks to T10 to L2 sections via the pelvic and hypogastric nerves. Meanwhile, the efferent nerve relays the message back to the bladder via the pelvic and hypogastric nerves from the spinal cord. The somatic nervous system dominates the control of the spinal cord from S2 to S4 sections, in which the message can be transmitted via the pudendal nerve to the external urethral sphincter to close the urethra. Figure 3 indicates the graphic explanation of the normal physical bladder control function. When the bladder is in the storage stage, the detrusor, which is a thick layer of smooth muscle in the bladder wall, receives the sympathetic signals from the ganglia of the spinal cord from T10 to L2 sections and expands to store urine.

T10

Pelvic Nerve (Sympathetic)

Detrusor muscle

T11 T12

Bladder

L1 L2 L3 L4

Hypogastric Nerve (Parasympathetic)

L5 Sphincter

S1 S2 S3 S4

Pudendal Nerve (Somatic)

Urethra

Fig. 3 Nerves supplying the bladder and related structures

1

Bladder Control Implants

7

At the same moment, the external sphincter contracts to close the bladder and to prevent urine leakage. Meanwhile, when the bladder is filled, the parasympathetic nerves on the spinal cord from S2 to S4 sections stimulate the detrusor, which then contracts. The external sphincter then expands to release the urine. Therefore, the bladder function can be restored by controlling the expansion and contraction of the detrusor and external sphincter, respectively.

Stimulation Mode Figure 4 demonstrates the two distinct modes for stimulation, namely, the current and voltage modes (Liu et al. 2007). Current-mode stimulation is widely used in surfaces and serves as an implantable stimulator for function electrical stimulation applications. The current amplitude is directly controlled by a DAC and is independent of tissue load. Therefore, in this stimulation mode, the quantity of charge delivered per stimulus pulse is easily controlled. In the voltage-mode stimulation, the stimulator output is a voltage. Hence, the magnitude of the current delivered to the tissue depends on the interelectrode and tissue impedances. Thus, the exact amount of charge supplied to the electrode and tissue is difficult to control because of the impedance variation (Masdar et al. 2012).

Stimulus Pattern Figure 5 illustrates the definitions of the key parameters in pulsing (Merrill et al. 2005). The frequency of stimulation is the inverse of the period of time between pulses, whereas the inter-pulse interval is the period of time between pulses. These stimuli can also be distinguished into two kinds, namely, monophasic and biphasic. Monophasic refers to a single phasic stimulus without reversal phase,

VDD

Istim A

Current Mode

Blocking Capacitor

Voltage Mode

Electrode

Capacitor charging circuitry Nerve Impedance

Istim C

C1

VSS

Fig. 4 Two simple circuits of different stimulation modes

C2

C3

C4

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S.-Y. Lee and C.-Y. Huang

Fig. 5 Types of stimulus

whereas biphasic pertains to a stimulus with two reversal phases. The biphasic stimulus can further be distinguished into charge-balanced and imbalanced pulses because of the reversal current. Charge-balanced pulse denotes that the anodic and cathodic currents are charged in the same degree, and the imbalanced pulse specifies that the cathodic current charge is higher than the anodic current charge. In some conditions, the biphasic stimulation must be accompanied with an interphase delay. Consequently, a variety of stimulus pattern should be developed to meet the requirement of an actual clinical trial. Figure 6 defines the additional parameters of the burst stimulus for the requirement of actual clinical trials. These added parameters include burst time, burst period, pulse period, pulse amplitude, and pulse width (Bruns et al. 2008).

Blocking Capacitors Blocking capacitor, which is an effective method of avoiding charge accumulation for nerve protection, is adopted to avoid unnecessary DC current. The value of blocking capacitor depends on the requirement of a specific stimulation according to the magnitude of the stimulated current (Istim), pulse width (dt), and voltage variation (dV) shown in (1):

C ¼ I stim

dt dV

,

dt ¼ pulse width:

(1)

Both the configurations depicted in Fig. 7a, b are (ideally) charge balanced to avoid charge accumulation. However, achieving an exact zero net charge without

1

Bladder Control Implants Burst times

9 Burst Period

Amplitude

Amplitude

Pulse Width

Pulse Period

Fig. 6 Parameters of a burst pulse stimulus

a

b

VDD

c

VDD

VDD

ZL

IstimA

A Blocking Capacitor

S1

S1

ZL A

S2

S3

Nerve

C

IstimB

S2

S3

Nerve

S1

ZL A

S2

C

Nerve C

S1

Istim Istim

VSS

Fig. 7 Output stage configurations of the conventional stimulators with two-electrode setup

switch S3 after each stimulation cycle is not possible because of the mismatch between the current source and current sink drivers and the stimulus timing errors or the leakage current from the adjacent electrode pairs (Sivaprakasam et al. 2005; Sit and Sarpeshkar 2007). Therefore, switch S3 can be used to provide an extra passive discharging phase to periodically remove all the residual charges from the electrode (anode and cathode). Another structure, which is presented in Fig. 7c, uses the passive discharge phase as the main anodic phase to avoid charge accumulation. The benefit is easily implemented, but the discharging phase must be long enough to ensure that the charge is completely depleted (Bugbee et al. 2001). High-frequency current switching (HFCS) is a creative technique to reduce blocking capacitor area and to inhibit its integration in silicon chip. Figure 8 presents the HFCS circuit and the relative timing diagram (Liu et al. 2008). The timing diagram illustrates that the electrode is actively charged in phase Tcathode and passively discharged in phase Tanode. When ϕ1(ϕ2) is high (low) at phase Tcathode, the programmable current charges capacitor C1 and discharges capacitor C2, and vice versa when ϕ1(ϕ2) is low (high). According to (1), the required capacitor is also smaller because dt becomes the time of half clock period, which is less than the time of pulse width in the output stage of the conventional stimulators (Fig. 7).

10

S.-Y. Lee and C.-Y. Huang VDDA

a f3

SL

b

stimulation cycle Tcathodic

ZL

Tanodic

f1 Is2

Is1 D2 D4

D1 VDDA

C1

VDDA

C2

S3 f2

f2

D3

S4 f1

S1

S2

f1 f2

f3

c Istim

Tcathodic

Istim

Tanodic B

A

Fig. 8 HFCS blocking capacitor circuit and timing diagram

For charge balance, when ϕ3 is high at phase Tanode, the slow reverse current can discharge the tissue and electrodes. Thus, the areas under A and B must be equal. This condition can be achieved by lengthening phase Tcathode than phase Tanode.

Magnitude of Current for Peripheral Nerve Stimulation The magnitude of current should be carefully designed for the stimulation because large stimulation current will burn the tissue and it is invalid on the tissue for small stimulation current. In their previous work, Rodriguez et al. (2000) presented the negative correlation between the current intensity and stimulus pulse width, as indicated in Fig. 9. Thus, the definition of maximal and threshold currents can be clearly determined from the figure, which also reminds that the strength–duration curves of the “first day” and “45th day” are different after the implantation of platinum cuff electrode.

Electrode and Contact Impedance Donfack et al. (2000) revealed that the impedance of the electrodes in bladder stimulators ranges from 300 to 3,000 Ω. The impedance variation of the tissue is between 500 and 1,200 Ω. If the electrodes or lead wire is broken, the impedance is more than 10 kΩ and results in the disconnection between the electrodes and nerve. By contrary, if the impedance is less than 100 Ω, this circumstance is interpreted as a short circuit between the electrodes and nerve caused by the injection of blood or saline between the two elements.

1

Bladder Control Implants

Stimulus Current

11

Day 0

1227 m A

Day 45

909 m A 788 m A

483 m A

486 m A

410 m A

399 m A

322 m A 310 m A

Maximal Current

255 m A

119 m A 109 m A 0.05ms 0.1ms

0.5ms

Threshold Current Stimulus Pulse 1ms Width

Maximal Current

306 m A 296 m A

133 m A 127 m A 0.05ms 0.1ms

Threshold Current 0.5ms

1ms

Fig. 9 Strength–duration curve associated with the bladder stimulation in animal. The horizontal axis depicts the stimulus pulse width, and the vertical represents the stimulus current

Design Stimulation Algorithm Data Format and Error-Checking Mechanism Figures 10 and 11 illustrate the custom packet format and error-checking mechanism for the bladder stimulation, respectively. Each segment length is purposefully arranged. The header and end bits are used for the packet location. The combinational logic circuit with parity check is utilized to enhance reliability during wireless communication. If the data are correct, the system can read or write the digital codes in the register to update the stimulation parameters and controlled bits. Burst Pulse Generation Algorithm Figure 12 shows the burst pulse generation algorithm. The pulse period counter is operated at 97.66 Hz. The output of the pulse period counter is initiated at 0 and is further calculated to attain the required value, which is defined by the stimulus parameters. For the counter, 6 bits is used. Therefore, the stimulation can be adjusted from 1.29 to 47.685 Hz. Once the output of the pulse period counter achieves 1, which briefly emerges about 10.2 ms, this value triggers the burst period counter. The burst period counter is operated at 1.563 kHz clock and is used to control the burst period and burst times. The product of the burst period and burst times cannot exceed 10.2 ms. Otherwise, the last burst pulse would not appear. The

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S.-Y. Lee and C.-Y. Huang

Checked Header 5 bits

Controlled Function Control 2 bits

Stimulus Parameters

Data Write Control 2 bits

Amplitude 8 bits

Pulse Period 6 bits

Pulse Width 5 bits

Stimulus Parameters Function 1 Burst Times 2 bits and Burst Period 3 bits Function 2 Interphase Delay 5 bits

Checked Threshold voltage 8 bits

Parity 6 bits

END 5 bits

Fig. 10 Packet format

Get data from RF demodulation

“Header” & “End” location check

Pass

Parity Check

Pass

According to controlled bits, update parameter of stimulation

Fig. 11 Error-checking mechanism

Counter For Pulse Period

97.66 Hz Clock

/ 16 Time Counter For Burst Period

Burst Times=2

1.563 kHz Clock

/ 64 Time Counter For Pulse Width

100 kHz Clock

Time

Fig. 12 Burst pulse generation algorithm

Stimulus Generator

1

Bladder Control Implants

13

parameter segment length is relative to the operated clock. In this work, 3 bits for the burst period and 2 bits for the burst times are defined in the stimulus parameter. When the burst period output is at 1, the pulse width counter is triggered at 100 kHz to provide the stimulus pulse width. After stimulation, all of the counters will be reset to 0. The microstimulator can change the stimulated direction and activate the pulse width counter again before stopping. This process forms the biphasic pulse to stimulate the nerve. The high-frequency components are began after the low-frequency components. However, the former is accomplished before the statement of low-frequency components is changed. The counter is often idle in unacted state and woken up by the system controller to avoid the clock synchronization problem and to reduce power consumption.

SRID Pulse Generation Algorithm Figure 13 illustrates the slow reversal with interphase delay (SRID) pulse generation algorithm. This algorithm is similar to the burst pulse generation algorithm but without the counters of burst period and burst times. The SRID pulse algorithm uses another counter operated at 100 kHz clock to produce the interphase delay. The output of pulse width counter triggers the interphase counter once it attains 1. At the 97.66 Hz Clock

Counter For Pulse Period

/ 16 Time 1.563 kHz Clock

Counter For Interphase Delay

/ 64 Time

100kHz Clock

Counter For Cathode Pulse Width

Stimulus Generator

Fig. 13 SRID pulse generation algorithm

Counter For Anthode Pulse Width

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S.-Y. Lee and C.-Y. Huang

same time, the input of the DAC produces multiple current ratios of 1/2, 1/4, or 1/8. This condition implies that the input bits of DAC can be shifted right 1, 2, or 3 to change the stimulus current intensity for the requirement of bladder stimulation. Once the output of interphase counter achieves 1, the pulse width counter is once again triggered, but a slight bit is different from the previous phase. According to the current ratio, this counter will count two, four, or eight times more to produce anodic pulse width. After stimulation, all counters will be reset to 0.

Digital-to-Analog Converter Design Figure 14 displays an 8-bit segmented current-mode DAC with a current mirror. The thermometer code scheme is employed in the DAC design because of its small glitch error. The 8-bit digital code is divided into two arrays, namely, 4-bit coarse codes and 4-bit fine code arrays. Each array includes 16 unit cells, and each unit cell contains a matrix decoder and current cell, as shown in Fig. 15. The cell current of the least significant bits is 1/16th of that of the most significant bits in each array. Accordingly, each unit current is 2 and 32 μA for coarse and fine code arrays, respectively. In Fig. 16, two 2-to-4 binary-to-thermometer decoders (row and column decoders) are used to control the current sources. This output current of DAC can provide a precise current Istim for the microstimulator. Instead of a simple current mirror, a wide-swing cascode current mirror is used to boost the output impedance with low power consumption, as shown in Fig. 17. Two switches are designed for the measurement issue, and the stimulus current only passes through one switch at the stimulation period.

B5

B4

B1

Column Decoder

Column Decoder

Vdd

Vdd

Vdd

Row Decoder

B6

Vdd

Row Decoder

B7

B0

Unit Cell

Icoarse

Ifine

Istim Fig. 14 8-bit segmented DAC structure

Idump

B3

B2

1

Bladder Control Implants

15

Cj VDD(1.8V)

i– R2i+1 R2i

Matrix Decoder

Iout Idump

Switched Current Cell i+ Unit Cell

Cj

R2i+1 R2i Cj i+

i–

000,001,100 0

1

1

0

R2i+1

VDD(1.8V)

Others VDD(1.8V)

VDD(1.8V)

R2i

Bias4

i– Bias3

VSS

VSS

i+ i–

i+

Matrix Decoder Idump

Iout VSS

Current Cell

Fig. 15 Unit cell with current cell and matrix decoder

Bi+3 Bi+2

Bi+3 Bi+2

Row Decoder

00 01 10 11

Bi+1

Bi+1 Bi

Bi

00 01 10 11

R7

R7 1

1

1

1

C3 C3 1

1

1

1

R6 R5

R6 1

1

1

0

C2 C2 1

1

1

0

R5 1

1

1

0

C1 1

1

0

0

R4 R3

R4 1

1

0

0

C1 C0 1

0

0

0

R3 1

1

0

0

R2 R1 R0

R2 1

0

0

0

R1 1

0

0

0

R0 0

0

0

0

C0

Column Decoder

Fig. 16 Row and column decoders

Stimulus Generation Design A stimulator with a set of switches, as depicted in Fig. 18, is proposed to provide the required stimulation current for the pudendal nerve because the use of a biphasic pulse can prevent the ion-charge accumulation in tissues. Two pairs of switches, S2 and S2 , which are controlled by the system controller, are designed to produce biphasic electrical stimulation pulses. Switches S1 and S1 with blocking capacitors are used to avoid the passage of DC current through the electrode–nerve interface during stimulation. These switches are controlled by the opposite phase of 100-kHz clock. When S3 is turned on (S3 is turned off), the current is passed through the nerve load according to the on/off of S1 and S1, and the blocking capacitor is either charged or repeatedly discharged. Conversely, when switch S3 is turned off (S3 is turned on), it releases residue charge from ion-charge accumulation in tissues and blocks the leakage current through the nerve load.

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Fig. 17 Wide-swing cascade current mirror

VDD (3.3V) Stimulus Generator

VDD (1.8V)

Control Control Switch1 Switch2

Istim

W1/L1 W1/L1

4*W1/L1

W2/L2

4*W2/L2

Bias2 W1/L1

VDD (3.3V)

Burst Pulse Stimulation

S3

Charged Imbalanced Stimulation

S1 0

__ S2 Out+

S2 Cs

S2 Cp __ S1

__ S3

__ S2

Out–

S1

Istim

0 S3

Cp __ S1

S2

0 S1

(Out+, Out–) 0

Fig. 18 Stimulus generator circuit and timing diagram

Power consumption is reduced by operating the system controller and 8-bit DAC at 1.8-V supply voltage according to the requirement of the TSMC 0.18-μm cell-based library. Meanwhile, the stimulus generator is operated at 3.3 V for the requirement of the microstimulator. The electrode–nerve impedance is about 3 kΩ; 3.3 V is a safety design if the stimulus intensity is more than 500 μA.

FPGA Implementation Figure 19 depicts the measured results of burst pulse stimulation on the fieldprogrammable gate array (FPGA) implementation. The pulse frequency is from 1.49 to 47.66 Hz (Fig. 19a, d), burst frequency ranges from 190.8 to 763 Hz

Fig. 19 Different burst pulse stimulation functions, distinguished pulse frequencies in a and d, distinguished burst frequencies in b and e, distinguished pulse widths in c and f

1 Bladder Control Implants 17

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(Fig. 19b, e), and pulse width is from 21 to 325 μs (Fig. 19c, f). The implementation results reveal that the function is normally operated to provide different burst pulses for the different requirements on the bladder stimulation. Figure 20 illustrates the measured results of the SRID stimulus stimulation. The pulse frequency is similar to the burst pulse period; it reveals that the anodic current intensity is half (Fig. 20a) of, one-fourth (Fig. 20b) of, or one-eighth (Fig. 20c) of the cathodic current. Moreover, the anodic pulse width is twice (Fig. 20a) of, four times Fig. 20b) of, or eight times (Fig. 20c) of the cathodic pulse width. The measured results also determine that the proposed SRID algorithm can provide the stimulation function for the different requirements of bladder control. Figures 19 and 20 show that the high-frequency ripples are caused by the disturbed charge from blocking capacitors on the FPGA board. These ripples can be reduced by replacing the larger capacitors and can be overcome in future implementations by using integrated circuits.

Fig. 20 SRID stimuli with different current ratios

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Bladder Control Implants

19

The proposed bladder control microstimulator has been implemented in a 0.18-μ m TSMC CMOS process using 1.8-V power supply. Figure 21 shows the chip microphotograph. The measured specifications are also illustrated in Table 1 to demonstrate the characteristic. Fig. 21 Microphotograph of the proposed bladder control microstimulator

Table 1 Specifications summary of the proposed bladder control microstimulator General specifications Technology Supply voltage Operation frequency Power consumption Stimulation function Pulse period – max (ms) Pulse period – min (ms) Pulse width – max (μs) Pulse width – min (μs) Burst period – max (ms) Burst period – min (ms) Interphase delay – max (μs) Interphase delay – min (μs)

TSMC 0.18 μm 1P6M CMOS 1.8 V and 3.3 V 100 kHz 36 μW (static); 1 mW (stimulation) Post-simulation Burst pulse 655.36 660.6 10.24 10.49 327.68 328 20.48 20 5.1 (3.2) 4.59 (2.62) 1.28 1.31 327.68 N/A 20.48 N/A

SRID pulse N/A N/A 327.68 (cathodic) 20.48 (cathodic) N/A N/A 328 20

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Conclusions A system-controlled method for a bladder controller is proposed and implemented in this study. The controlled bits can be programmed by surgeons from an external device to provide the required burst pulse or slow reversal with interphase delay pulse stimulation. A total of 52 bits are defined in the communication protocol, and internal codes are used to enhance reliability and to control the stimulated parameters. The measured result shows that the function is effective. The measurement results of the stimulation function on FPGA are helpful for future implementations in the integrated circuits, and the microstimulator with controlled algorithm is integrated with power interface and sensing channel as an implantable device for animal study.

References Bruns TM, Bhadra B, Gustafson KJ (2008) Variable patterned pudendal nerve stimuli improves reflex bladder activation. IEEE Trans Neural Syst Rehabil Eng 16(2):140–148 Bugbee MB, Donaldson NN, Lickel A, Rijkhoff NJM, Taylor J (2001) An implant for chronic selective stimulation of nerves. Med Eng Phys 23:29–36 Donfack CM, Sawan M, Savaria Y (2000) Implantable measurement technique dedicated to the monitoring of electrode-nerve contact in bladder stimulators. Med Biol Eng Comput 38 (4):465–468 Lee SY, Su MYC et al (2011) A programmable implantable microstimulator SoC with wireless telemetry: application in closed-loop endocardial stimulation for cardiac pacemaker. IEEE Trans Biomed Circ Syst 5(6):511–522 Liu A, Demosthenous A, Rahal M, Donaldson N (2007) Recent advances in the design of implantable stimulator output stages. In: 18th European Conference on Circuit Theory and Design (ECCTD), pp 204–207 Liu X, Demosthenous A, Donaldson N (2008) An integrated implantable stimulator that is fail-safe without off-chip blocking-capacitors. IEEE Trans Biomed Circ Syst 2(3):231–244 Masdar A, Ibrahim BSKK, Abdul Jamil MM (2012) Development of low-cost current controlled stimulator for paraplegics. Int J Integr Eng 4(3):40–47 Merrill DR, Bikson M, Jefferys JGR (2005) Electrical stimulation of excitable tissue: design of efficacious and safe protocols. J Neurosci Methods 141(2):171–198 Rodriguez FJ, Ceballos D, Schuttler M, Valero A, Valderrama E, Stieglitz T, Navarro X (2000) Polyimide cuff electrodes for peripheral nerve stimulation. J Neurosci Methods 98(2):105–118 Sit JJ, Sarpeshkar R (2007) A low-power blocking-capacitor-free charge-balanced electrodestimulator chip with less than 6 nA DC error for 1-mA full-scale stimulation. IEEE Trans Biomed Circ Syst 1(3):172–183 Sivaprakasam M, Liu W, Humayun MS, Weiland JD (2005) A variable range bi-phasic current stimulus driver circuitry for an implantable retinal prosthetic device. IEEE J Solid State Circ 40(3):763–771

2

Development and Evaluation of a Continuous Blood Pressure Monitoring System Toshiya Arakawa, Noriaki Sakakibara, and Shinji Kondo

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Steering-Type Blood Pressure Monitoring System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . System Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Experiment and Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Infrared Blood Pressure Monitoring System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . System Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Experiment and Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

22 24 24 29 31 33 35 38 38

Abstract

There is a growing awareness among the populace regarding the role of a healthy lifestyle as a key component to overall health. One integral measure of good health is blood pressure. Hypertension leads to heart disease and other serious health issues for an individual, but it also affects society as a whole by a general decrease in productivity and economic loss and more specifically is a major cause of traffic accidents. Blood pressure monitors have been developed and sold widely and are in wide use throughout society, but these are primarily what are termed noninvasive monitors. The monitors currently available have some disadvantages including discomfort for the patient caused by painful cuff inflation, which may actually influence blood pressure readings, and the unfeasibility of continuous or semicontinuous blood pressure monitoring due to the necessity for repetitive cuff inflation and deflation. Cuffless blood pressure measuring systems T. Arakawa (*) Department of Mechanical Systems Engineering, Aichi University of Technology, Gamagori-city, Aichi, Japan e-mail: [email protected] N. Sakakibara · S. Kondo KANDS Inc., Kariya-city, Aichi, Japan © Springer Science+Business Media, LLC, part of Springer Nature 2022 M. Sawan (ed.), Handbook of Biochips, https://doi.org/10.1007/978-1-4614-3447-4_50

21

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have been developed which help to solve these problems, and in this paper, two of these systems and their effectiveness and practicality are introduced. Steeringtype blood pressure monitoring systems and infrared blood pressure monitoring systems and their measurement principles are described, and their performance evaluation is also explained. The primary disadvantage of steering-type blood pressure monitoring systems is found to be accuracy; infrared blood pressure monitoring systems currently suffer from some time lag in reporting of results.

Introduction There is a growing awareness of the importance of lifestyle in achieving and maintaining good health (Meguro 2001). As such awareness increases, the basic lifestyles of all people are changing to incorporate better-rounded lifestyles. Not only is this important at a personal level, but it is also related to national strategies; in response, new business development related to healthcare has appeared around the world. One health area that has received great interest is that of high blood pressure; it can cause major diseases and ailments such as strokes and heart and kidney diseases (Moser 1992; Stroke Association 2017). Considering the rapid progression of the aging population and a Westernized diet (Monge et al. 2018; Rai et al. 2017), it is becoming increasingly important to prevent the occurrence of hypertension in Japan and in the rest of the world. Hypertension, a major contributor to cardiovascular disease (CVD) including heart disease and stroke, is one of the leading contributors to the global burden of disease and is a growing public health problem worldwide (Constant et al. 2016). In the United States, about 75.2 million adults (one in every three) had hypertension during 2013–2014 (Merai et al. 2016). In 2014, hypertension was listed as a primary or contributing cause of 427,631 American deaths, and heart disease and stroke were the first and fifth leading causes of death, respectively (CDC, National Center for Health Statistics 2016). A study based on analysis data from the 2011 to 2014 National Health and Nutrition Examination Survey (n ¼ 9623) shows that, according to the criteria from the 2017 American College of Cardiology/American Heart Association (ACC/AHA) and the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC7) guidelines, the crude prevalence of hypertension among American adults was 45.6% (95% confidence interval (CI), 43.6–47.6%) and 31.9% (95% CI, 30.1–33.7%), respectively. In addition, antihypertensive medication was recommended for 36.2% (95% CI, 34.2– 38.2%) and 34.3% (95% CI, 32.5–36.2%) of American adults, respectively (Muntner et al. 2018). Another report shows that one in three adults in the United States has high blood pressure and half of them do not have it under control (Godman 2018). Another study of the relation between hypertension and economic loss showed that the total cost of treating hypertension in the United States in 2030 will be

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US$50.3 billion–US$47.2 billion in direct medical costs and US$13.1 billion in indirect costs owing to lost productivity related to morbidity and mortality (William 2003). Based on this study, the total direct cost of hypertension is followed by coronary heart disease (CHD, US$61.2 billion), and the total indirect costs of lost productivity is followed by CHD (US$129.9 billion) and stroke (US$51.2 billion). Thus, it is found that hypertension significantly leads to decreased productivity and economic loss. Besides decreasing productivity and economic loss, hypertension is also a major cause of traffic accidents (Arakawa 2018). One study shows that hypertension is the most common chronic pathology found in traffic offenders (Saei et al. 2018). Another study reported that hypertension is highly prevalent among bus drivers, a group responsible for the safety of many others on the road (Sadri 2015). It is therefore important for professional drivers, like bus drivers, to manage their own health condition daily and to be aware that they should not drive without rest or when they are unwell. In addition, it is desirable to develop a system that manages the driver’s health and monitors sudden spikes in blood pressure, providing them the opportunity to safely pull off the road before a health crisis occurs, thereby decreasing the number of traffic accidents and ensuring the safety of the driver and their passengers (Arakawa 2018). A previous report (Godman 2018) also stated that the AHA and other organizations have called for greater use of home blood pressure monitoring; however, their use is not widespread. One reason is that insurance coverage for such programs still lags, and another is that full-fledged efforts such as those established in the state of Minnesota could cost US$1350 per person. However, this report also states that everyone can buy a good home blood pressure monitor currently available from a pharmacy or online merchant from between US$50 and US$100; this would make checking blood pressure twice a day easy to accomplish for everyone. These inexpensive noninvasive blood pressure monitors based on cuff occlusion are in wide use both inside and outside of care facilities (Schoot et al. 2016). However, they have some disadvantages including discomfort for the patient because of painful cuff inflation (which may also influence blood pressure outcome) and the unfeasibility of continuous or semicontinuous blood pressure monitoring due to the necessity for repeated cuff inflation and deflation (Arakawa 2018). They are not very practical for use in constant monitoring during driving or in monitoring of healthy people in their daily routines. As a response to finding a more practical method of providing regular blood pressure monitoring, noninvasive cuffless blood pressure measurement devices have begun to be developed and mass-produced. In fact, the IEEE published a standard for wearable cuffless blood pressure measuring device systems that can measure blood pressure based on pulse wave propagation time (IEEE1708 certified on 26 August 2014) (Arakawa et al. 2018). Our survey shows that the development of cuffless blood pressure measurement monitors has been thriving since the standard was published and the worldwide proliferation of cuffless blood pressure measurement monitors has increased from the point of Google patents (Arakawa 2018).

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In this chapter, the two types of cuffless blood pressure monitoring systems used in this study are presented, and their efficiency and usability are discussed. The first is a steering-type blood pressure monitoring system (Arakawa et al. 2016, 2018), and the second is an infrared blood pressure monitoring system. Usually, these systems are designed for in-vehicle systems, which can monitor driver’s blood pressure in real time in order to manage the driver’s health and decrease the number of traffic accidents. The steering-type blood pressure monitoring system was developed first; it was followed by a more versatile infrared system designed with ultrasonic sonar which can be used both inside and outside vehicles. The remainder of this chapter is organized as follows. Section “Steering-Type Blood Pressure Monitoring System” introduces the steering-type blood pressure monitoring system, and section “Infrared Blood Pressure Monitoring System” introduces the infrared blood pressure monitoring system. Section “Conclusion” summarizes this chapter.

Steering-Type Blood Pressure Monitoring System System Development As mentioned in section “Introduction,” a system for managing driver’s health is needed in order to decrease the number of traffic accidents. However, such a system must not restrict the driver’s driving position, noncontact systems are desirable for the comfort of the driver. Steering-type blood pressure monitoring systems fit this requirement, because the driver needs to grasp steering wheel while driving and they provide unconscious measurement of the driver’s blood pressure. If a driver is highly conscious of a monitoring system, the driver could be so conscious that his or her driving may be distracted, so it is important that a practical monitoring system be imperceptible to the driver. The features of this system are that it is cuffless and responds robustly to body movement so that it is not affected as the steering wheel is turned. The development of this monitor was completed as part of a feasibility study; therefore the system was created for a desktop driving simulator and not for a real vehicle. The system used to measure blood pressure was developed and tested, while the participant played a racing game on a PlayStation 4 ®. A steering controller for a PlayStation 4® (Hori Co., Ltd.) was used as the steering wheel. This steering-type sphygmomanometer was connected to a tablet PC (Dell latitude 10 (Dell Inc.)) through Bluetooth. The blood pressure data measured by the sphygmomanometer were transmitted to the tablet PC, which showed the driver’s blood pressure in real time. Figure 1 shows the developed system and Fig. 2 illustrates a block chart of the same system. Two sensors (the red LED parts in Fig. 1) are attached to the steering wheel spokes because the steering wheel turns from 90 to +90 and the driver places their hands at almost the same location on a real steering wheel when driving. The sensors are therefore attached at positions of 10 and 2 o’clock on the wheel and

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Fig. 1 Outline of the developed system

Fig. 2 A block chart of the developed system

can detect the driver’s pulse even if the driver removes one hand from the wheel. Figure 3 shows a driver grasping the steering and controlling the vehicle. With this system, infrared light is emitted from a sensor unit attached to the steering wheel ring and is aimed at the skin of the driver’s finger. The transition of the finger plethysmogram, which is the integral value of the photoplethysmogram, is calculated for every pulse beat, and the reference light quantity is used to determine the average blood pressure. In addition, blood flow, the condition of the hemoglobin,

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Fig. 3 A driver grasping the steering and controlling the vehicle

and the vascular elasticity rate are calculated. The systolic and diastolic blood pressures can be continuously calculated based on these results. The algorithm for calculating the blood pressure is shown in the following (Kondo et al. 2008, 2010). Photoplethysmography is applied to a pipe flow in a viscous fluid (Hagen-Poiseuille flow, the algorithm of which follows the equation below): Q ¼ π  R2  V ¼

π  R4 P1  P2  8η L

ð1Þ

where Q (m3/s) is the flow volume, R (m) is the radius of the pipe, V (m/s) is the flow velocity, η (Pas) is the viscosity of the fluid, and (P1  P2)/L (Pa/m) is the pressure gradient between the two points (L (m)). In short, the pressure correlates to the flow volume in a pipe. The above equation was applied for photoplethysmography. The following assumptions were made in the logic of the estimation: 1. The blood pressure correlates positively with blood flow. 2. The mural pressure correlates positively with the pressure against a tissue (e.g., the cuff pressure or application of the probe pressure). 3. A constant probe pressure is applied to the tissue. 4. The pressure difference is higher for the arteries than for the venous vessels. 5. The photoplethysmographic signals are sensitive only to the hemoglobin dynamics. Therefore, when adequate pressure and adequate light emission are applied to the tissue, the transmitted light is hypothesized to correlate with the blood pressure, i.e., the systolic pressure was estimated for the peak of the light transmitted, and the diastolic pressure was estimated for the through-transmission of the light.

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Figure 4 shows a schema of the blood pressure pulsation and time course. The following equations were hypothesized for measuring blood pressure: sp  d p t 2

s1 ¼

ð2Þ

s2 ¼ d p  t

ð3Þ

S ¼ s1 þ s2

ð4Þ

K¼

s1 s2

ð5Þ

where sp (mmHg) denotes the systolic pressure, dp (mmHg) denotes the diastolic pressure, and t (s) denotes the wave period. The following equations were then hypothesized for the photoplethysmographic pulsation: ps1 ¼

p1  p2 t 2

ð6Þ

ps2 ¼ p2  t

ð7Þ

pS ¼ ps1 þ ps2

ð8Þ

pK ¼

ps1 ps2

ð9Þ

where p1 (mW) denotes the maximum photoplethysmographic signal intensity, p2 (mW) denotes the minimum photoplethysmographic signal intensity, and a denotes an arbitrary constant.

Fig. 4 (a) Schema of blood pressure pulsation and time course and (b) schema of photoplethysmographic pulsation and time course

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The following equations were deduced when K is hypothesized as pK: ps1 ¼

K  pK 1þK

ð10Þ

ps2 ¼

1  pK 1þK

ð11Þ

ð2K þ aÞ  pK ð1 þ K Þ  t

ð12Þ

pK ð1 þ K Þ  t

ð13Þ

p1 ¼

p2 ¼

Therefore, p1 (mmHg) and p2 (mmHg) are estimated as the systolic and diastolic pressures. Figure 5 shows a flow chart of blood pressure detection using the developed system. Next, the calibration procedure is described as shown in Fig. 5. Fig. 5 Flow chart estimating continuous blood pressure using a photoplethysmogram

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1. As a reference to determine the upper and lower limits of their blood pressure, the drivers input their systolic and diastolic blood pressures into the tablet PC based on past diagnostics. 2. The pulse waves recorded when holding the steering wheel are detected to determine the reference finger plethysmogram, and the finger plethysmogram is tuned to the specified gain based on the amount of light and sensitivity. 3. The tuned photoplethysmogram is considered a standard plethysmogram regarding average blood pressure. The ratio of diastolic blood pressure to the average blood pressure is added to the plethysmogram of the average blood pressure, which is determined as a plethysmogram of the diastolic blood pressure. A plethysmogram of the systolic blood pressure is calculated similarly based on the ratio of systolic blood pressure to the average blood pressure and is considered a standard plethysmogram. 4. The observed plethysmogram and plethysmogram of the standard blood pressure are compared, and the current average blood pressure is calculated. Next, the systolic and diastolic blood pressures are calculated based on the pulse pressure ratio calculated based on the ratio of height of the plethysmogram. Photoplethysmographic detection by sensors attached to the steering wheel branches into a circuit to reduce the noise from body movements and a circuit for filtering. A photoplethysmogram passes through a circuit to reproduce a pulse wave after passing through the circuit for filtering, and the blood pressure is calculated based on the photoplethysmogram by applying digital processing. Finally, a pulse wave is obtained, and the value of the blood pressure is output.

Experiment and Evaluation Seven people participated in the experiment. The average age of these people was 22 years old, and these people were selected randomly from male college students. First, their systolic and diastolic blood pressures were measured using a commercial electronic sphygmomanometer, which measures blood pressure based on a conventional method (also known as the Riva Rocci Korotkoff method, for blood pressure measurements) (Maley 2019). Their blood pressure was then measured using our system for a 2-min period. The participants then rested for 5 min; after which their blood pressure was measured, they then rested for another 2 min and the blood pressure test was repeated. The results of this experiment are shown in Fig. 6, along with the measurement results of the systolic and diastolic blood pressures using a commercial conventional electronic sphygmomanometer for comparison. A boxand-whisker plot comparing a “conventional” method shows the blood pressure as determined using a commercial electronic sphygmomanometer, the range of error of which is 10 mmHg. The first and second box-and-whisker plots show the average blood pressure of the first and second measurements taken by our system and two standard deviations (SD), respectively. It should be noted that outliers from failed measurements were omitted from the data. In addition, as shown in Fig. 6, the horizontal dotted line indicates the range of average blood pressure, namely, 10 to

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Fig. 6 Measurement results using a commercial electronic sphygmomanometer through a conventional method and our proposed system

+10 mmHg, which is within the range of error of the commercial electronic sphygmomanometer. Thus, if the average 2SD of the blood pressure determined by our system is between the dotted lines, it suggests that the blood pressure measured has almost the same accuracy as a commercial electronic sphygmomanometer. Figure 7 shows the idea behind the verification of the validity of our developed system. Based on Fig. 7, if the blood pressure range of the developed system, which is between the

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Fig. 7 Evaluation the validity of the developed system: (a) difference in blood pressure +10 [mmHg] of the conventional method and blood pressure +2SD [mmHg] of the developed system and (b) difference in blood pressure 2SD [mmHg] of the developed system and the blood pressure 10 [mmHg] of the conventional method

average 2SD, includes the blood pressure range of the conventional system, which is between the average 10, then it can be stated that this system is valid (Arakawa et al. 2018). As a reference, Table 1 shows the difference between the diastolic blood pressure, 2SD mmHg, of the test system and the diastolic blood pressure, 10 mmHg, using the commercial electronic sphygmomanometer, as well as the difference between the systolic blood pressure, +10 mmHg, by the commercial electronic sphygmomanometer, and the diastolic blood pressure, +2SD mmHg, of the test system, based on Fig. 7. From Table 1, it can be said that the test system achieves the same level of performance as a commercial electronic sphygmomanometer based on the range of blood pressure shown when all values are positive. From Fig. 6 and Table 1, it appears that the systolic blood pressure does not seem to be within the proper range. However, the diastolic blood pressures of participants D and E do seem to be within the proper range. By contrast, the systolic blood pressures of participants A, C, and F seem to be within the proper range, whereas the diastolic and systolic blood pressures of participants B and G seem to be outside the proper range. Based on these results, it was determined that the blood pressure captured by our system tends to be affected by the differences in the individual, and it is necessary to make further refinements to the algorithm used in the blood pressure estimation.

Infrared Blood Pressure Monitoring System Section “Steering-Type Blood Pressure Monitoring System” presented a prototype blood pressure monitoring system built into a vehicle steering wheel. However, steering-like blood pressure monitoring systems are specialized for use in-vehicles; thus it is necessary to consider versatility in order to provide new technology for a wider portion of the population. In addition, if only in-vehicle use is considered, users cannot choose and buy steering wheel-based blood pressure system as optional

Participant A Participant B Participant C Participant D Participant E Participant F Participant G

Diastolic blood pressure First (a) (b) 15.1 9.38 30.2 42.6 6.20 14.1 9.23 20.6 3.23 14.6 11.0 9.87 72.8 16.9 Second (a) 14.5 32.6 2.59 0.24 6.24 10.2 26.9 (b) 8.50 30.0 16.5 13.7 7.70 5.43 38.1

Systolic blood pressure First (a) (b) 0.94 8.00 20.0 22.2 7.96 6.90 0.25 0.85 13.8 15.0 1.67 8.75 44.0 33.7

Second (a) 2.42 17.6 5.44 12.9 32.6 2.52 36.8

7.46 22.5 6.64 6.19 22.2 9.22 8.00

(b)

Table 1 Validity evaluation of developed system. If the values in (a) and (b) are both positive, it indicates that the developed system has the same level of validity as the conventional method

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parts after they buy a vehicle, making it quite inconvenient. Thus, an infrared blood pressure monitoring system that can be attached as a small shield over vehicle gauges was developed. If these infrared blood pressure monitoring systems are mass-produced, users can buy them and easily attach them to their vehicle. In this section, development of this infrared blood pressure monitoring system is introduced.

System Development The infrared blood pressure monitoring system developed has a transmitter and receiver. The transmitter emits infrared light onto a person, and the receiver receives the weak reflected infrared light from a person’s skin. The reflected infrared light hits the receiver, its signal is processed through an amplifier and a filter circuit, and then the reflected infrared light is output as detected signal (called a continuous pulse wave). The signal changes based on the relative change of blood flow, which of course is based on the volume change of blood. The flowchart shown in Fig. 8 illustrates how calculation of blood pressure is performed using this information. First, calibration of the blood pressure value must be done. The user’s systolic blood pressure value Ps and diastolic blood pressure value Pd are set manually, and the average blood pressure value Pm is calculated. Then, stability of the continuous pulse wave is detected, and this is calculated as standard pulse wave area V0. Next, the coefficient of average blood pressure, the ratio of systolic blood pressure and diastolic blood pressure, as well as the ratio of diastolic blood pressure are calculated as calibration value based on Eqs. (14), (15), and (16): L¼

V0 Pm

ð14Þ

O¼

Ps Pm

ð15Þ

P¼

Pd Pm

ð16Þ

where L is the coefficient of average blood pressure, O is the ratio of systolic blood pressure value and average blood pressure value, and P is the ratio of diastolic blood pressure value and average blood pressure value. After calibration, values L, O, and P are calculated and the actual blood pressure value is measured. At actual measurement, V0n, which is the pulse wave area of every heartbeat based on the continuous pulse wave, is calculated using the same method as V0, which is the standard pulse wave area. Next, the average blood pressure value Pmn (this is called average blood pressure of every heartbeat) from Eq. (17) is based on pulse wave area and coefficient of average blood pressure every heartbeat:

34 Fig. 8 Flowchart of calculating blood pressure

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Pmn ¼

V 0n L

35

ð17Þ

After calculation of the average blood pressure of every heartbeat, maximum blood pressure Psn and minimum blood pressure Pdn are calculated from Eqs. (18) and (19) based on the ratio of maximum blood pressure and average blood pressure, the ratio of minimum blood pressure and average blood pressure, and average blood pressure: Psn ¼

Pmn O

ð18Þ

Pdn ¼

Pmn P

ð19Þ

The developed infrared blood pressure monitoring system is shown in Fig. 9. Figure 10 shows a block diagram of the developed infrared blood pressure monitoring system.

Experiment and Evaluation The accuracy of continuous blood pressure monitoring systems is evaluated in section “Experiment and Evaluation.” However, time lag to detect blood pressure must be considered if using the developed infrared blood pressure monitoring system because of the distance from system to user. Here, the experiment was to evaluate the effect of time lag when using infrared blood pressure monitoring system in vehicles. A driving simulator, DS-nano- (Advanced Solutions Technology Japan), was used for this experiment. This driving simulator has a real vehicle-like exterior and interior. The developed blood pressure monitoring system, introduced in section “System Development,” was attached to a small shield over the driving simulator’s gauges. Nine male participants (from 21 to 50 years old) joined this experiment. All participants were briefed on the experiment, and an informed consent was obtained from all the participants. All experiments required the participants to drive in an urban area-like simulation environment, which has many intersections of low visibility. They were asked to Fig. 9 The developed infrared blood pressure monitoring system

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Fig. 10 Block diagram of the developed infrared blood pressure monitoring system

Fig. 11 Driving situation. After about 3-min driving, an unexpected vehicle appears

drive at approximately 50 km/h for about 4 min. After driving of approximately 3 min, a vehicle appeared to run into the road from a hidden location (Fig. 11). Here, the direction from which the vehicle ran (whether from left or from right) was random. The participant’s blood pressure and driver behavior (velocity, steering angle, and brake pressure) were measured. For the purpose of evaluating the time lag, a contact-type continuous blood pressure monitoring system (μBP-mp, KANDS, Inc.) was also attached to the participant, and his blood pressure was measured. In addition, perspiration sensors were also attached to their left and right thumbs to detect perspiration that occurred by the sudden appearance of the additional vehicle. Figure 12 shows the details of the experiment. Figure 12a shows a participant’s driving situation, and Fig. 12b shows an outline of the driving simulator as it records the participant.

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Fig. 12 Situation of experiment. (a) Participant’s driving situation and (b) outline of driving simulator on the participant’s driving Fig. 13 Average increasing time 1SD of all participants

The timing that the average blood pressure increased from the point of an unexpected vehicle was first seen as it appeared was calculated. The changepoint method was applied in order to detect the timing. From the result of the calculation by the changepoint method, the average increase in time 1SD of all participants is shown in Fig. 13. Here, in Fig. 13, “μBP-mp” means the timing based on blood pressure measured with contact-type continuous blood pressure monitoring system (μBP-mp), and “IR” means the timing based on blood pressure measured with infrared blood pressure monitoring system. Time lag to detect blood pressure is important because this infrared blood pressure measurement system is a noncontact measurement system. However, it was found that the detection time by the developed systems is about 2.7 s longer than that for contact continuous blood pressure measurement systems, which is not a significant difference. Thus, delay of the detection timing of blood pressure increase may not affect the actual operation if psychological tension of a vehicle’s sudden appearances is detected with infrared blood pressure measuring systems.

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Conclusion In this paper, two blood pressure monitoring systems were introduced: the first is a steering-type blood pressure monitoring system, and the second is infrared blood pressure monitoring system. Both have advantages particularly for the drivers of vehicles; however, there are still many problems that need to be addressed to achieve downsizing and improvement of accuracy. However, these blood pressure measurements could dramatically support improved lifestyles and dynamically increase driver safety. Acknowledgments The authors would like to thank Misaki Design LCC for developing the cabin of the driving simulator.

References Arakawa T (2018) Recent research and developing trends of wearable sensors for detecting blood pressure. Sensors 18(9):2772. https://doi.org/10.3390/s18092772 Arakawa T, Kaminaga K, Sakakibara N et al (2016) Development and evaluation steering-type blood pressure measuring monitor based on plethysmography. In: Proceedings of the IIAE annual conference 2016, Kyoto, 8–12 Sept 2016, pp 3–4. (In Japanese) Arakawa T, Sakakibara N, Kondo S (2018) Development of non-invasive steering-type blood pressure sensor for driver state detection. Int J Innov Comput Inf Control 14:1301–1310 CDC, National Center for Health Statistics (2016) Multiple cause of death 1999–2015. CDC WONDER online database. http://wonder.cdc.gov/mcd-icd10.html. Accessed 10 June 2017 Constant AF, Geladari EV, Geladari CV (2016) The economic burden of hypertension. Chapter 21. In: Andreadis EA (ed) Hypertension and cardiovascular disease. Springer International Publishing, Switzerland. https://doi.org/10.1007/978-3-319-39599-9_21 Godman H (2018) Checking blood pressure at home pays off. Available online: https://wwwhealth harvardedu/blog/checking-blood-pressure-at-home-pays-off-201307036436. Accessed 1 June 2018 Kondo S, Shimoyama S, Yoshida A et al (2008) Minimal invasive estimation of blood pressure for continuous monitoring. Chiba Med J 84:15–25 Kondo S, Shimoyama I, Masuda K et al (2010) Evaluation of blood pressure and cardiac output by non-invasive volume pulse wave continuous blood pressure measurement method and invasive continuous blood pressure measurement method. Bull Jpn Soc Ther Eng 22:3–9. (In Japanese) Maley C (2019) Intro to blood pressure. https://www.adctoday.com/blog/intro-blood-pressure. Accessed 28 Sept 2019 Meguro S (2001) Marketing in health care—strategic insight into new business development. Reitaku Int J Econ Stud 19:56–71. (In Japanese) Merai R, Siegel C, Rakotz M et al (2016) CDC grand rounds: a public health approach to detect and control hypertension. MMWR Morb Mortal Wkly Rep 65(45):1261–1264. https://doi.org/ 10.15585/mmwr.mm6545a3 Monge A, Lajous M, Ortiz-Panozo E et al (2018) Western and modern Mexican dietary patterns are directly associated with incident hypertension in Mexican women: a prospective follow-up study. Nutr J 17:21 Moser M (1992) High blood pressure. In: Yale University School of Medicine heart book. William Morrow & Co., New York Muntner P, Carey RM, Gidding S et al (2018) Potential US population impact of the 2017 American College of Cardiology/American Heart Association high blood pressure guideline. J Am Coll Cardiol 71(2):109–118

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Rai SK, Fung TT, Lu N et al (2017) The dietary approaches to stop hypertension (DASH) diet, Western diet, and risk of gout in men: prospective cohort study. BMJ 357:j1794 Sadri G (2015) A model of bus drivers’ diseases: risk factors and bus accidents. Iran J Med Sci 27(1):39–41 Saei A, Rahmani A, Ebadi A et al (2018) Traffic accidents and health of the driver. Trauma Mon 23(2):e12963, 1–12. https://doi.org/10.5812/traumamon.12963 Schoot TS, Weenk M, van de Belt TH et al (2016) A new cuffless device for measuring blood pressure: a real-life validation study. J Med Int Res 18:e85 Stroke Association. High blood pressure and stroke. Available online: https://www.stroke.org.uk/ sites/default/files/high_blood_pressure_and_stroke.pdf. Accessed 16 Dec 2017 William JE (2003) The economic impact of hypertension. J Clin Hypertens 5:3–13

3

Electrical Biosensors: Biopotential Amplifiers Fan Zhang, Tan Yang, Jeremy Holleman, and Brian Otis

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . System Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Energy-Efficiency of Amplifier Topologies and the Noise Efficiency Factor . . . . . . . . . . . . . . . . . . . State-of-the-Art Biopotential Amplifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Design Example: A Closed-Loop Fully Differential Telescopic-Cascode Amplifier . . . . . . . . . . . Design Example: An Open-Loop Complementary-Input Amplifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Design of a Closed-Loop Fully Differential Complementary-Input Amplifier . . . . . . . . . . . . . . . . . . Measurement Results of the Biopotential Amplifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Design Example: A High-Input-Impedance Low-Noise Instrumentation Amplifier . . . . . . . . . . . . Overall Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Noise Analysis of the Instrumentation Amplifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

42 42 44 47 48 50 51 55 58 59 61 62 62 63

F. Zhang Marvell Semiconductor, Santa Clara, CA, USA e-mail: [email protected] T. Yang Analog Devices, Raleigh, NC, USA e-mail: [email protected] J. Holleman (*) Electrical and Computer Engineering, University of North Carolina, Charlotte, Charlotte, NC, USA e-mail: [email protected] B. Otis Low Power Chip Design. Wireless Biosensors. Electrical Engineering, University of Washington, Seattle, WA, USA e-mail: [email protected] © Springer Science+Business Media, LLC, part of Springer Nature 2022 M. Sawan (ed.), Handbook of Biochips, https://doi.org/10.1007/978-1-4614-3447-4_24

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Abstract

Recent advances in semiconductor technology and microelectrode fabrication have made possible the development of implantable neural interfaces with large numbers of recording channels. Signal fidelity depends on the performance of the initial amplification stage. With many recording channels, the power efficiency of the amplifier also becomes critical. In this chapter, we discuss some of the requirements for biopotential amplifiers and the design tradeoffs. We also describe several example designs to give the reader a quantitative sense of the tradeoffs involved.

Introduction Implantable systems for chronic use require ultra-low power operation to minimize heat dissipation, avoid frequent battery replacement, and enable operation from wirelessly delivered or harvested energy. The usefulness of these systems depends on the capability of the analog front end to acquire signals from the neural tissue without contributing excess electrical noise. The biopotential amplifiers (BPAs) must also be robust to interference from the power supply or from other interfering sources such as line power and other instrumentation. When the number of channels is large or the power budget is extremely constrained, power consumption of the biopotential amplifiers is also critical. Because of these constraints, the designer of the analog front end must consider issues ranging from choice of technology and supply voltage up to system-level architectural considerations. In this chapter, we first attempt to frame the problem with a typical system illustration and a description of the characteristics of some of the more common neural recording modalities. We then examine the fundamental considerations regarding choice of amplifier topology and the most common figures of merit used to describe the noise performance and power efficiency of neural amplifiers. We then consider several example amplifier designs in detail.

System Considerations An example of a biopotential recording system is illustrated in Fig. 1. The acquisition of microvolt-level neural signals requires amplification and signal conditioning. The amplified signals may be processed to extract the most salient information and reduce the data rate. The signal is then transmitted to an external device, where the information is used to diagnose neural disorders or infer neural state, for example. Monolithic amplifiers have been used for electrophysiological recording signals for decades (Steyaert and Sansen 1987; Najafi and Wise 1986). The large time constants inherent in the amplifier dynamics typically preclude timesharing of a single amplifier between multiple electrodes (Harrison and Charles 2003). Therefore,

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Electrical Biosensors: Biopotential Amplifiers

BPA

VGA

MUX

BPA

43

VGA

ADC

Adaptive threshold

Digital Signal Processing

TX

Energy Detection Analog Signal Processing

Fig. 1 A generic block diagram for a biopotential-recording system

Table 1 Characteristics of electrophysiological signals Single-Unit LFP (local field potential) ECoG EEG

Bandwidth 100–7000 Hz