Biomedical Signals and Sensors I PDF

biological and medical physics, biomedical engineering For further volumes: http://www.springer.com/series/3740 biological and medical physics, biomedical engineering The ﬁelds of biological and medical physics and biomedical engineering are broad, multidisciplinary and dynamic. They lie at the crossroads of frontier research in physics, biology, chemistry, and medicine. The Biological and Medical Physics, Biomedical Engineering Series is intended to be comprehensive, covering a broad range of topics important to the study of the physical, chemical and biological sciences. Its goal is to provide scientists and engineers with textbooks, monographs, and reference works to address the growing need for information. Books in the series emphasize established and emergent areas of science including molecular, membrane, and mathematical biophysics; photosynthetic energy harvesting and conversion; information processing; physical principles of genetics; sensory communications; automata networks, neural networks, and cellu- lar automata. Equally important will be coverage of applied aspects of biological and medical physics and biomedical engineering such as molecular electronic components and devices, biosensors, medicine, imag- ing, physical principles of renewable energy production, advanced prostheses, and environmental control and engineering. Editor-in-Chief: Elias Greenbaum, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA Editorial Board: Masuo Aizawa, Department of Bioengineering, Mark S. Humayun, Doheny Eye Institute, Tokyo Institute of Technology, Yokohama, Japan Los Angeles, California, USA Olaf S. Andersen, Department of Physiology, Pierre Joliot, Institute de Biologie Biophysics & Molecular Medicine, Physico-Chimique, Fondation Edmond Cornell University, New York, USA de Rothschild, Paris, France Robert H. Austin, Department of Physics, Lajos Keszthelyi, Institute of Biophysics, Hungarian Princeton University, Princeton, New Jersey, USA Academy of Sciences, Szeged, Hungary James Barber, Department of Biochemistry, Robert S. Knox, Department of Physics Imperial College of Science, Technology and Astronomy, University of Rochester, Rochester, and Medicine, London, England New York, USA Howard C. Berg, Department of Molecular Aaron Lewis, Department of Applied Physics, and Cellular Biology, Harvard University, Hebrew University, Jerusalem, Israel Cambridge, Massachusetts, USA Stuart M. Lindsay, Department of Physics Victor Bloomf ield, Department of Biochemistry, and Astronomy, Arizona State University, University of Minnesota, St. Paul, Minnesota, USA Tempe, Arizona, USA Robert Callender, Department of Biochemistry, David Mauzerall, Rockefeller University, Albert Einstein College of Medicine, New York, New York, USA Bronx, New York, USA Eugenie V. Mielczarek, Department of Physics Steven Chu, Lawrence Berkeley National and Astronomy, George Mason University, Fairfax, Laboratory, Berkeley, California, USA Virginia, USA Louis J. DeFelice, Department of Pharmacology, Markolf Niemz, Medical Faculty Mannheim, Vanderbilt University, Nashville, Tennessee, USA University of Heidelberg, Mannheim, Germany Johann Deisenhofer, Howard Hughes Medical V. Adrian Parsegian, Physical Science Laboratory, Institute, The University of Texas, Dallas, National Institutes of Health, Bethesda, Texas, USA Maryland, USA George Feher, Department of Physics, Linda S. Powers, University of Arizona, University of California, San Diego, La Jolla, Tucson, Arizona, USA California, USA Earl W. Prohofsky, Department of Physics, Hans Frauenfelder, Purdue University, West Lafayette, Indiana, USA Los Alamos National Laboratory, Andrew Rubin, Department of Biophysics, Moscow Los Alamos, New Mexico, USA State University, Moscow, Russia Ivar Giaever, Rensselaer Polytechnic Institute, Michael Seibert, National Renewable Energy Troy, New York, USA Laboratory, Golden, Colorado, USA Sol M. Gruner, Cornell University, David Thomas, Department of Biochemistry, Ithaca, New York, USA University of Minnesota Medical School, Minneapolis, Minnesota, USA Judith Herzfeld, Department of Chemistry, Brandeis University, Waltham, Massachusetts, USA Eugenijus Kaniusas Biomedical Signals and Sensors I Linking Physiological Phenomena and Biosignals With 125 Figures 123 A"o. Univ.-Prof. Dipl.-Ing. habil. Dr. Eugenijus Kaniusas Head of research group 'Biomedical Sensors' Vienna University of Technology Institute of Electrodynamics, Microwave and Circuit Engineering Gusshausstr. 27–29, 1040 Wien, Austria E-mail: [email protected] Biological and Medical Physics, Biomedical Engineering ISSN 1618-7210 ISBN 978-3-642-24842-9 e-ISBN 978-3-642-24843-6 DOI 10.1007/978-3-642-24843-6 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2012930477 © Springer-Verlag Berlin Heidelberg 2012 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specif ically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microf ilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specif ic statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) Preface The present two volume set focuses on the interface between physiologic mechanisms and diagnostic human engineering. A multitude of biomedical sensors are commonplace in clinical practice today. The registered biomedical signals, which will be referred to as biosignals, reflect vital physiologic phenomena and are relevant not only for the pre-screening and diagnosis of maladies but also for therapy and follow-up treatment. For instance, the diagnosis of sleep apnea, i.e., abnormal cessation of respiration during sleep, requires the monitoring of a complete set of sleep and respiratory variables with at least eight different sensors distributed over the entire body. In order to adequately apply biomedical sensors and reasonably interpret the corresponding biosignals, a proper understanding of the physiologic phenomena involved, their influence on the registered biosignals, and the technology behind the sensors is critical. Moreover, a nearly unlimited diversity of biosignals emphasizes the need for a strategic approach in the genesis of biosignals, including a profound understanding of fundamentally different mechanisms in a biosignal’s generation. From a strategic point of view, biosignal generation involves the biosignal formation path from the biosignal source at the physiological level, to biosignal propagation in the body, to biosignal transmission in the sensor up to its conversion to a, usually electric, signal. To give an example, heart sounds, an acoustic biosignal, are created by the closure of heart valves, which constitutes the biosignal source. Sound attenuation in the thoracic tissue represents the propagation mechanism. Amplification and filtering of the heart sounds in the chestpiece (of the stethoscope) reflect biosignal transmission effects in the sensor, with biosignal conversion being performed by a microphone at the output of the chestpiece. The first volume is focused on the interface between physiologic mechanisms and the resultant biosignals, whereas the second volume is devoted to the interface between biosignals and biomedical sensors. Unlike other contributions, this book deals differently on the subject of either specific physiologic mechanisms or specific engineering aspects pertaining to particular biomedical sensors, since it emphasizes the interface between them. Both volumes systematically describe basic v vi Preface mechanisms of biosignal formation while electric, acoustic, optic, and mechanic biosignals are considered in depth. In the given volume, the physiologic mechanisms determining biosignals are described from the basic cellular level—as the place of origin of each and every biosignal—up to their advanced mutual coordination level, e.g., during sleep. It allows a physiologically accurate interpretation and comprehensive analysis of the biosignals. The resultant biosignals are discussed within the scope of vital and common physiologic phenomena to foster their understanding and comprehensive analysis. This book is directed primarily at graduate and postgraduate students in biomedical engineering and biophysics. It should also appeal to those who are studying or are interested in physical, engineering, and life sciences, since expected background knowledge is minimal and many basic phenomena are explained in depth within the numerous footnotes. Furthermore, the book should serve engineers and practitioners who have an interest in aspects of biomedical engineering. This book attempts to provide a blinding glimpse of the obvious, in spite of the issues that appear rather complex at first glance. It is important to note that this book was mainly inspired by my lectures entitled “Biomedical Sensors and Signals,” “Biomedical Instrumentation,” and “Biophysics” which constitute a significant part of a master’s degree program “Biomedical Engineering” at the Vienna University of Technology. Vienna, Austria Eugenijus Kaniusas Acknowledgments A number of personalities have shaped me and my educational background within the scope of the present work. First of all, I wish to express my appreciation to Univ.-Prof. Helmut Pfützner from the Institute of Electrodynamics, Microwave and Circuit Engineering (EMCE), Vienna University of Technology, who has guided me in the field of biomedical engineering and magnetism. I gratefully acknowledge the support from Univ.-Prof. Adalbert Prechtl from the EMCE for unlimited scientific advice in the field of electrical engineering. I would like to express my sincerest thanks to Univ.-Prof. Giedrius Varoneckas from the Institute of Psychophysiology and Rehabilitation (IPR), Medicine Uni- versity of Kaunas, Lithuania, for an almost infinite number of fruitful discussions, inspiration, and close cooperation in clinical investigations. Furthermore, I am grateful for the cooperation of Univ.-Prof. Bernd Saletu from the Department of Psychiatry, University of Vienna, who has supported me in an unrestricted way concerning numerous clinical investigations. Indeed my understanding and appreciation of biomedical issues have been boosted by collaborative research and interdisciplinary studies. Here I would like to place on record my gratitude to my colleagues from the EMCE as Dr. Lars Mehnen, Dr. Jürgen Kosel, Dr. Karl Futschik, Dr. Stefan Traxler, and Dr. Peter Schönhuber. Special thanks go to Linas Zakarevicius and Audrius Alonderis from the IPR. In particular, I thank my students Stefan Kampusch, Florian Thürk, and Jillian Haac for careful proofreading. The book has significantly benefited from countless small and large projects in which numerous diligent students of mine have been involved. I give sincere thanks to all of them. I would like to thank my family which has been the source of inspiration and recharging all the time. While supporting me with their love and affection, they have had to bear the loss of my time and effort at home. I express my deep gratitude to my parents and other relatives for providing me with an inner strength and solid background to meet challenges and achieve goals. vii Contents 1 Fundamentals of Biosignals................................................. 1 1.1 Definition and Model of Biosignals.................................... 1 1.2 Historical Aspects....................................................... 6 1.2.1 The Very First Biosignals...................................... 6 1.2.2 Problems and Solutions........................................ 11 1.3 Classification of Biosignals............................................. 15 1.4 Trends in Biosignals Monitoring....................................... 19 References..................................................................... 25 2 Physiological and Functional Basis........................................ 27 2.1 Cell...................................................................... 27 2.1.1 Functional Structures........................................... 27 2.1.2 Cell Membrane.................................................. 36 2.1.2.1 Passive Properties.................................... 37 Transport of Substances.............................. 37 Transport of Potential Difference.................... 41 2.1.2.2 Active Mechanisms.................................. 49 Regulatory Mechanisms............................. 49 Active Transport...................................... 53 2.1.3 Cell Membrane Potential....................................... 55 2.1.3.1 Quiescent Cell........................................ 56 2.1.3.2 Excited Cell........................................... 62 Cell Stimulation...................................... 63 Cell Response........................................ 65 Response to Different Stimuli....................... 71 2.1.4 Propagation of Excitation...................................... 74 2.1.4.1 Axon Propagation.................................... 74 2.1.4.2 Synaptic Propagation................................. 81 ix x Contents 2.2 Neurons and Receptors................................................. 89 2.2.1 Structure......................................................... 90 2.2.2 Function......................................................... 96 2.3 Muscle................................................................... 107 2.3.1 Structure......................................................... 108 2.3.2 Function......................................................... 113 2.4 Heart..................................................................... 121 2.4.1 Structure......................................................... 121 2.4.2 Function......................................................... 125 2.5 Circulatory System...................................................... 133 2.5.1 Functional Structure............................................ 134 2.5.2 Phenomena...................................................... 142 2.5.2.1 Arterial Behavior..................................... 142 2.5.2.2 Steady Flow........................................... 144 2.5.2.3 Pulsatile Flow......................................... 147 Pulse Propagation.................................... 147 Blood Pressure and Flow............................. 151 Pulse Waveforms of Pressure and Flow............. 159 Reflected Pulse Propagation......................... 163 2.6 Respiratory System..................................................... 173 2.6.1 Structure......................................................... 173 2.6.2 Function......................................................... 175 References..................................................................... 180 3 Physiological Phenomena and Biosignals................................. 183 3.1 Vital Phenomena and Their Parameters................................ 184 3.1.1 Heartbeat........................................................ 184 3.1.2 Respiration...................................................... 194 3.1.3 Blood Circulation............................................... 201 3.1.3.1 Blood Pressure........................................ 203 Estimation from Arterial Radius..................... 211 Estimation from Pulse Running Time............... 212 3.1.3.2 Blood Flow........................................... 215 3.1.3.3 Arterial Radius....................................... 218 3.1.4 Blood Oxygenation............................................. 221 3.1.5 Body Temperature.............................................. 225 3.2 Parameter Behavior..................................................... 230 3.2.1 Cardiorespiratory Interrelations................................ 232 3.2.1.1 Phenomenological Physiology....................... 233 Normal Respiration................................... 233 Ceased Respiration................................... 239 3.2.1.2 Biosignals and Parameters........................... 242 Normal Respiration................................... 242 Ceased Respiration................................... 251 Contents xi 3.2.2 Cardiovascular Interrelations................................... 253 3.2.2.1 Phenomenological Physiology....................... 254 3.2.2.2 Biosignals and Parameters........................... 258 3.2.3 Biological Rhythms............................................. 263 3.2.4 Sleep............................................................. 270 References..................................................................... 277 Index............................................................................... 283 Symbols and Abbreviations Note: Variables used within limited contexts are not listed, for they are described within the relevant section. The different types of biosignals are separately listed below. A Surface area, signal amplitude AM Maximum cross section of the artery ATP Adenosine triphosphate c (Molar) concentration, constants C Capacitance C0 Length related capacitance C 00 Area related capacitance CBT Core body temperature CHA Central sleep hypopnea CSA Central sleep apnea d Membrane thickness D Axon diameter, electric flux density DF Diffusion coefficient e Elementary charge E Electric field, Young’s modulus EDR Electrocardiogram derived respiration EP Pressure-strain modulus f Frequency fA Activation rate fC Heart rate fR Respiratory rate F Force g Arbitrary function G0 Length-related electrical conductance G 00 Area-related electrical conductance h Wall thickness HF High frequency HRV Heart rate variability xiii xiv Symbols and Abbreviations i Current iC Capacitive current iE (Electric) ionic current I Current amplitude, intensity of sensation IA Augmentation index IT Threshold current amplitude jD Chemical diffusion rate jE Electric diffusion rate J Current density JC Capacitive current density JD (Chemical) diffusion current density JE (Ionic) electric current density k Index l Vessel/tube length, propagation distance LF Low frequency m Ionic mobility MHA Mixed sleep hypopnea MSA Mixed sleep apnea NA Avogadro constant NREM Nonrapid eye movement OHA Obstructive sleep hypopnea OSA Obstructive sleep apnea p Power spectral density, membrane permeability, probability, (intraarterial) blood pressure P (Complex) blood pressure amplitude pD Diastolic blood pressure pE External pressure outside the blood vessel pO2 Partial pressure of oxygen in blood pCO2 Partial pressure of carbon dioxide in blood pI Incident pressure wave pIF Inflection point in pressure wave pR Reflected pressure wave pS Systolic blood pressure pS;D Systolic–diastolic deflection of the blood pressure pT Transmural pressure pH pH value PNS Parasympathetic nervous system PSG Polysomnography q Blood flow, air flow, cardiac output, charge Q (Complex) blood flow amplitude, electric charge qI Incident blood flow wave qR Reflected blood flow wave r Blood vessel radius, (ion) radius rD Diastolic artery radius rM Maximum radius of the artery rS Systolic artery radius rT Artery radius at zero transmural pressure R Fluid/vascular longitudinal resistance, gas constant RT Total peripheral resistance Symbols and Abbreviations xv R0 Length related electrical resistance RR Interbeat interval from electrocardiogram REM Rapid eye movement s Biosignal, see below sS;D Systolic–diastolic deflection of the cardiac component S Hemoglobin oxygen saturation, stimulus strength SNS Sympathetic nervous system t Time T Absolute temperature, duration, period u Voltage, blood flow velocity U (Membrane) voltage amplitude UR Resting (membrane) voltage amplitude v Pulse wave velocity, nerve conduction (propagation) velocity, drift velocity V Volume VS Left ventricular stroke volume VLF Very low frequency W Energy x Coordinate, distance y Coordinate z Valence Z (Complex) electrical impedance, (complex) longitudinal vascular impedance, vascular impedance Z0 (Complex) characteristic vascular impedance ZI (Complex) input vascular impedance ˛ Attenuation coefficient Reflection factor " Dielectric permittivity # Temperature Module of volume elasticity Wavelength, (membrane) length constant Dynamic viscosity of the liquid Specific resistance, density Standard deviation, mechanical stress Pulse running (arrival, transit) time, (membrane) time constant A Membrane time constant for axial currents D Diastolic transit time S Systolic transit time PW Width of the pulse wave R Membrane time constant for radial currents Phase angle, electrical conductivity I Electrical conductivity of the intracellular medium M Electrical conductivity of the membrane ' Phase angle, electric potential Poisson ratio ! Angular frequency Symbols of Biosignals The types of biosignals discussed and their short descriptions. Symbol Name Biosignal Phenomena reflected class sBCG Barocardiogram signal permanent Mechanic Arterial blood pressure sECG Electrocardiogram signal permanent Electric Electrical excitation of heart muscles sMRG Mechanorespirogram signal permanent Mechanic Circumference changes of the abdomen or chest during breathing sMSG Mechanospirogram signal permanent Mechanic Air flow through the mouth sPCG Phonocardiogram signal permanent Acoustic Sounds emitted by consecutive closures of heart valves sTG Thermogram signal permanent Thermal Skin temperature from proximal and distal body regions sTRG Thermorespirogram signal permanent Thermal Air temperature in front of the nostrils during breathing sBG Barogram signal induced Mechanic Pressure in the cuff on the upper arm sOPG Optoplethysmogram signal induced Optic Pulsatile blood absorption of artificial light sPG Phonogram signal induced Acoustic Sounds emitted by local turbulence in the blood flow of the brachial artery (Korotkoff sounds) xvii Chapter 1 Fundamentals of Biosignals If you sharpen your electrical sense to generators in the body, If you listen to body sounds emerging from depths of the body, If you look through a fragment of the body, If you feel the skin pulsation of the body, You are to gain a valuable knowledge of the body’s well-being... Sensing technologies in physiology gain a lot of importance for the assessment of the human functional state. The registered biomedical signals—referred to as biosignals here—are important not only for timeless classical applications concern- ing medical diagnosis and subsequent therapy, but also for future applications such as daily driver monitoring. Thus, this chapter starts by giving a definition of biosignals and its very gen- eral model, considering biosignal generation, propagation, and its conversion for application-specific analysis. This model offers a solid basis for each type of biosignal, which will accompany us throughout the book. Then the very beginning steps of biosignal registration, the history of biosignal assessment, are discussed. The problems encountered (at that time) are described, as well as applied methods to solve them, with some of these methods having outlasted many centuries and are in use even today. Possible classifications of commonly used biosignals (state of the art biosignals) are introduced in this chapter to perceive a nearly unlimited diversity of biosignals. Lastly, a few ubiquitous applications of biosignal assessment are given, followed by future trends in biosignal monitoring. 1.1 Definition and Model of Biosignals Within the scope of biomedical signals and sensors, a biosignal can be defined as a description of a physiological phenomenon, irrespective of the nature of this description. Since there is a nearly unlimited number of physiological mechanisms E. Kaniusas, Biomedical Signals and Sensors I, Biological and Medical Physics, 1 Biomedical Engineering, DOI 10.1007/978-3-642-24843-6 1, © Springer-Verlag Berlin Heidelberg 2012 2 1 Fundamentals of Biosignals of interest, the number of possible biosignals is very large. In the broadest sense, the variety of biosignals extends from a visual inspection of the patient (Sect. 1.2) up to signals recorded from the human body using sensors, e.g., electrocardiography, compare Fig. 1.1. The huge diversity of biosignals can be best demonstrated by the fact that there are numerous kinds of biosignal classification, as discussed later in Sect. 1.3. To give an example of a biosignal from its generation up to its registration, Fig. 1.2 depicts the formation of acoustic biosignals which are used, for instance, for the assessment of cardiorespiratory pathologies. The corresponding biosignal source in the heart is given by the periodic closure of heart valves, which yields Fig. 1.1 Basic procedures for biosignal assessment from (a) visual appraisal of patient by a physician to (b) application of a biomedical sensor on the patient Coupling Conversion Output channel Microphone sPCG Bell Diaphragm Body Propagation Weak Strong intensity intensity decay decay Heart sounds Lung sounds Snoring sounds Sources Body sound sources Fig. 1.2 The biomedical sensor on the chest for the registration of body sounds. The generation phenomena of the acoustic biosignals are depicted, along biosignal’s propagation, coupling, and registration 1.1 Definition and Model of Biosignals 3 a Coupling and Propagation losses conversion losses Source of Z1 Z2 Registration of biosignal biosignal A I U Body b Coupling and Propagation Coupling and conversion losses losses conversion losses Applied Z 2’ Z1 Z2 Registration of signal biosignal U A I Body Fig. 1.3 Model of biosignal generation, propagation, coupling, and registration. (a) Permanent biosignal. (b) Induced biosignal heart sounds. In addition, the lung sounds are generated by air turbulences in the branching airways of the lung, whereas the snoring sounds arise in the upper airways due to elastic oscillation of the pharyngeal walls. The sounds propagate throughout the tissue and undergo attenuation due to increasing distance from the source and damping by the medium itself. As indicated in Fig. 1.2 by intensity decay, the attenuation is different for different sounds, since their spectral components differ. In particular, the attenuation is less for the heart sounds than for the lung and snoring sounds, since the latter sounds exhibit more high-frequency components facing a stronger damping. The coupling (and amplification) of sounds is performed by a stethoscope chestpiece with an oscillating diaphragm and a resonating volume. Lastly, the conversion of the acoustical pressure vibrations into an electric signal is carried out by an electroacoustic transducer, a microphone. Thus, the principle behavior in the formation of an arbitrary biosignal can be modeled as an equivalent circuit according to Fig. 1.3a. That is the source of the biosignal is represented by a sinusoidal1 voltage source u.t/ D U cos.!t C 'U / 1 Usually the source of the biosignal exhibits nonsinusoidal behavior. However, the nonsinusoidal waveform can be represented as a sum of sinusoidal functions (according to Footnote 150), thus the equivalent circuit from Fig. 1.3a is also applicable here. 4 1 Fundamentals of Biosignals with complex amplitude U D U ej'U ; (1.1) magnitude U , angular frequency !.D 2 f with f as oscillating frequency), and phase 'U , satisfying u.t/ D ReŒU ej!t . The propagation losses are represented by a series impedance Z1 D Z1 ej'1 ; (1.2) the coupling and conversion losses by another series impedance Z2 D Z2 ej'2 ; (1.3) and the registered biosignal by the resulting current i.t/ D I cos.!t C 'I / with complex amplitude I D I ej'I ; (1.4) satisfying i.t/ D ReŒI ej!t . According to Ohm’s law,2 U I D : (1.5) Z1 C Z2 In other words, the higher the losses, e.g., the magnitudes Z1.¤ 0/ and Z2.¤ 0/ of usually capacitive-ohmic losses, the weaker the registered biosignal will be, i.e., the magnitude I. In general, 'I ¤ 'U provided that '1 ¤ 0 or '2 ¤ 0; likewise, if all losses can be modeled by real resistances then 'I D 'U and I D U=.Z1 C Z2 /. It should be noted that physiological phenomena of interest are hidden not only in U but also in Z 1 , for the propagation may influence the resulting I in a significant and even advantageous way (Sect. 5). If the acoustic biosignal (Fig. 1.2) is considered in the light of the above model (Fig. 1.3a), the temporal behavior of an acoustical source can be described by u.t/ and its intensity by U. The strength of the propagation losses of the body sounds can be given as Z1 (1.2) while the capacitive behavior of the propagating 2 Georg Simon Ohm (1789–1854) was a German physicist after which Ohm’s law was named. The law states that the strength of electric current I through a conductor is directly proportional to the voltage U across the conductor divided by the impedance Z of the conductor, if a constant Z is given, e.g., over conductor temperature or oscillation frequency of the current. For complex values, it can be written as U I D : Z For the continuum form of Ohm’s law see Footnote 45. 1.1 Definition and Model of Biosignals 5 medium can be described by the corresponding phase angle '1.¤ 0/. Alternatively, the strength of the coupling and conversion losses in the acoustical sensor can be defined as Z2 , whereas the corresponding '2.¤ 0/ can describe the time delay in the chestpiece and the conversion delay in the microphone (1.3). The output sPCG.t/ of the microphone—as schematically shown later in Fig. 1.15c—corresponds then to i.t/ [compare (1.5)]. While the model from Fig. 1.3a applies to permanent biosignals with their source already inside the body, Fig. 1.3b depicts a model of an induced biosignal (Sect. 1.3). Here, the biosignal is generated outside the body with an artificial signal source with its complex amplitude U. After coupling and conversion losses Z 0 2 on the input side, the induced signal undergoes propagating losses Z 1 in the body, which are modulated by a physiologic phenomena of interest. On the output side, the coupling and conversion losses Z 2 co-determine the resulting induced biosignal I according to U I D : (1.6) Z 1 C Z 2 C Z 02 To give an example, U could characterize an incident artificial light beam coupled into a finger, whereas Z 1 varies by the changing light absorption due to pulsat- ing blood volume (Sect. 6). Since blood pulsations carry cardiac and respiratory information, the transmitted light characterized by I reflects cardio-respiratory activity, as depicted later in Fig. 1.15c, which can be used advantageously in clinical applications. In accordance with the origin of the biosignals, the biosignals are used in both diagnosis and therapy. While the diagnosis3 is concerned with an assessment of health status based on biosignals (Fig. 1.3), the therapy4 utilizes the biosignals as an objective feedback for selecting appropriate therapeutic measures, continuously monitoring their impact, and improving their efficiency, as depicted in Fig. 1.4. In the latter case, the biosignal registered by a diagnostic device and represented by I controls a therapeutic device by adjusting its stimulus given by U. From a practical point of view, the aforementioned acoustic biosignals (Fig. 1.2) could serve as an example for the diagnostic application of biosignals, as will be discussed in Sect. 5 in detail. The therapeutic application of biosignals could be demonstrated by functional muscle stimulation (e.g., on the leg) or functional nerve stimulation (e.g., on the ear auricle). While the stimulation (i.e., therapy) is performed by the use of electric impulses in both cases—compare Fig. 1.4—the respective feedback is given, for instance, by electromyography or force/torque measurement to assess the muscle response in the former case and by heart rate 3 Generally, the diagnostic area of biomedical technologies can be classified into functional evaluation of the physiological state, clinical evaluation, and bioimaging (Turchetti et al. 2010); compare Footnote 4. 4 The therapeutic area of biomedical technologies can be classified into noninvasive treatments, invasive treatments (minimally invasive and surgical), artificial organs and prosthesis, and rehabil- itation (Turchetti et al. 2010); compare Footnote 3. 6 1 Fundamentals of Biosignals Therapeutic Body Diagnostic device device U Z1 Z2 I Z 2’ A Feedback for adaptive treatment Fig. 1.4 Diagnostic application of biosignals (compare to Fig. 1.2b) variability to assess the response of the autonomic nervous system (Sect. 3.1.1) in the latter case. 1.2 Historical Aspects The registration of human biosignals underwent a long-lasting development over many centuries. It began with visual inspections without the use of any instruments, moved to the application of technical tools for signal registration, and is now in an implementation stage of pervasive, almost imperceptible, monitoring. Obviously this development has been driven by patient and physician needs as well as by problems that were encountered, interestingly not always relevant from a pure diagnostic point of view. As was recognized centuries ago concerning biosignal analysis in Mahomed (1872): “... surely it must be to our advantage to appreciate fully all it tells us, and to draw from it all that it is capable of imparting....” 1.2.1 The Very First Biosignals The very first diagnoses were made on the patient’s verbal account of his illness with the unaided senses. Forthcoming investigations yielded the first biosignals which were used for the diagnostic purposes only. The methods applied here encompassed mainly inspection, palpation, percussion, and auscultation (Fig. 1.5): Inspection (latin inspectio scrutiny) is the thorough visualization of the patient by the use of the naked eye. The physician may judge, for instance, body features, nutritional state, or skin color (Fig. 1.1a). Palpation (latin palpare feeling by touch) involves feeling the surface of the body with the hands to determine the size, shape, stiffness, or location of the organs beneath the skin (Fig. 1.5a). Often, applying a small amount of pressure to the surface of the skin or superficial artery to partially constrict it facilitates an easy observation of mechanical changes. 1.2 Historical Aspects 7 a b c Fig. 1.5 Primary diagnosis methods besides inspection from Fig. 1.1a. (a) Palpation. (b) Percus- sion. (c) Auscultation Percussion (latin percussio striking) is a procedure that involves striking the body directly or indirectly with short, sharp taps of a finger or a hammer (Fig. 1.5b). The sounds produced display a resonant or dull character, indicating the presence of a solid mass or hollow, air-containing structures, respectively. The sounds are helpful in determining the size and position of various internal organs, in localizing fluid or air in the chest and abdomen, and in aiding in the diagnosis of certain lung disorders. Auscultation (latin ausculto hear attentively) describes a diagnostic procedure in which the physician listens to inner body sounds to detect pathologies or the state of health (Fig. 1.5c). The body sounds may be comprised of heart sounds due to closure of the heart valves or lung sounds due to air turbulences in the branching airways. Hippocrates of Cos (around 460 BC–377 BC), ancient Greek physician regarded as the father of medicine, emphasized a simple visual inspection: “It is necessary to begin with the most important things and those most easily recognized. It is necessary to study all that one can see, feel, and hear, everything that one can recognize and use” (Castiglioni 1941). For instance, he noted that good humor, quiet sleep, clear mind, and mobility were descriptive of a favorable prognosis. By contrast, lying with the mouth and eyes open with legs spread apart, insomnia, and intense movements, indicated an unfavorable prognosis (Marinella 2008). Palpation was also used by Hippocrates as a method for clinical examination, as demonstrated in Fig. 1.6. For instance, in his work “Diseases of Women” he writes “... And if you then palpate the uterus....” In particular, palpation of the arterial pulse has been recognized from antiquity as the most fundamental sign of life,5 a periodic expansion of an artery (e.g., radial artery on the wrist) is felt in response to a periodic rise in blood pressure. Galen of Pergamum (around 129–200), Greek physician and philosopher, was one of the first great authorities on the pulse, 5 Erasistratus (about 310 BC –250 BC ), Greek physician, regarded by some as the “father of physiology,” already used the pulse in clinical diagnosis. As a curiosity, the lover’s pulse or love- sickness became a well-documented clinical entity and an integral part of pulse lore through the centuries. The love-sickness was described as pulse quickening in the presence of a beloved person (Hajar 1999). 8 1 Fundamentals of Biosignals Fig. 1.6 Hippocrates is pictured palpating a young patient (painting from Christian Medical College 2008) admired by his patron, the emperor Marcus Aurelius. He described the pulsation as “The feeling of the artery striking against the fingers” and characterized it in many details as “the worm-like pulse, feeble and beating quickly; the ant-like pulse that has sunk to extreme limits of feebleness” (Hajar 1999). Centuries later, Dr. Leopold Auenbrugger (1722–1809), Austrian physician, introduced the percussion technique as a diagnostic tool in medicine in 1761 in Vienna, Austria. Percussion was described as “a slow tapping with the fingers, brought close together and extended, on the fingers of the other hand laid on the chest” (Auenbrugger 1761). However, this technique was widely disseminated only decades later by Dr. Jean-Nicolas Corvisart (1755–1821), French physician and primary physician of Napoleon Bonaparte, who translated Auenbrugger’s book into French (Auenbrugger and Corvisart 1808) in 1808, as illustrated in Fig. 1.7. The direct auscultation of body sounds (Fig. 1.8) was also already employed more than twenty centuries ago, as suggested in Hippocrates work “de Morbis”: “If you listen by applying the ear to the chest... ” (Rappaport and Sprague 1941). However, only at the beginning of the nineteenth century did the body sounds gain adequate relevance and recognition among physicians. A few decades later, after the wide acceptance of the percussion, which also involves an auscultation of artificially produced sounds, the auscultation tech- nique was fundamentally improved by Dr. Rene Theophile Hyacinthe Laennec (1781–1826). The French internist and a student of Dr. Corvisart made in 1816 an epoch making observation with a wooden cylinder, which was primarily sought to avoid embarrassment. “I was consulted,” says Laennec, “by a young women who presented some general symptoms of disease of heart, in whose case the application of the hand and percussion gave but slight indications, on account of her corpulency. On account of the age and sex of the patient, the common modes of exploration (i.e., immediate application of the ear) being inapplicable, I was led to recollect a well 1.2 Historical Aspects 9 Fig. 1.7 Title page of Corvisart translation about percussion as a diagnostic tool (Auenbrugger and Corvisart 1808) known acoustic phenomenon... I took a quire of paper which I rolled together as closely as possible, and applied one end to the precordial region; by placing my ear at the other end, I was agreeably surprised at hearing the pulsation of the heart much more clearly and distinctly than I had ever been able to do by the immediate application of the ear” (Rappaport and Sprague 1941; Abdulla 2001). A precursor of the stethoscope (greek stetos chest and skopein explore) was born—as shown in Fig. 1.9—viewed by many as the very symbol of medicine, for conduction of the sounds generated inside the body between the body surface and the ears, as depicted in Fig. 1.10. An oil painting is shown in Fig. 1.11 with Laennec among students holding his stethoscope in the hand, while applying his ear to the chest of a patient. Later, in 1894, A. Bianchi introduced a rigid diaphragm over the part of the (wooden) cylinder, i.e., the chestpiece, that was applied to the chest (Hollins 1971; Rappaport and Sprague 1941), compare Fig. 1.2. The modern stethoscope consists of a bell-type chestpiece for sound amplification (Welsby et al. 2003; Abdulla et al. 1992), rubber tube for sound transmission, and earpieces for conducting the sound into ears (Ertel et al. 1971). 10 1 Fundamentals of Biosignals Fig. 1.8 Direct auscultation of body sounds Fig. 1.9 Drawings of the original Laennec’s stethoscpe (Laennec 1819) 1.2 Historical Aspects 11 Fig. 1.10 Indirect auscultation of body sounds with Laennec’s stethoscope (Thom 1954) At the end of the nineteenth century, Laennec’s stethoscope was still not used on a regular basis. The introduction of the stethoscope forced physicians to undergo a cardinal reorientation, for the stethoscope altered both the physician’s perception of disease and his relation to the patient. Despite the clear superiority of the instrument in the sound auscultation, it was accepted with some antagonism even by prominent chest physicians. Among others, the amusing critics concluded that “The stethoscope is a largely decorative instrument insofar as its value in diagnosis... Nevertheless, it occupies an important place in the art of medicine. Apprehensive patients with functional complaints are often relieved as soon as they feel the chestpiece on their pectoral muscles... ” or physicians complained that “they heard too much” (Loudon and Murphy 1984). 1.2.2 Problems and Solutions The main problems faced by the original biosignal acquisition methods—inspection, palpation, percussion, and auscultation (Sect. 1.2.1)—were related to an objective evaluation of the diagnostic results. In particular, Proof of biosignals Analysis of biosignals Comparison of biosignals Circulation of biosignals 12 1 Fundamentals of Biosignals Fig. 1.11 Laennec, inventor of the stethoscope, applies his ear to the chest of a patient (Chartran 1849–1907) were impossible due to the subjective nature of the diagnosis. In other words, repro- ducibility of the biosignal observation was not possible because of the observer’s variability and no means for the biosignal’s archival storage, as is self-evident for today’s applications. Analysis of the biosignals was restricted to an instantaneous impression by the physician, with the impression being strongly affected by the physician’s personal experience. The classification of biosignals was impeded by nomenclature difficulties. The comparison of two biosignals was hardly possible, as they were restricted to a single physician and recent impressions. Circulating the accumulated biosignal data was also impossible because of the lack of archives. Obviously the above problems and limitations were recognized early, with an attempt to circumvent them in a contemporary manner. The most notable 1.2 Historical Aspects 13 Table 1.1 Approaches to objectify the biosignals from a historical point of view Description Subjective impact Quality Verbal Strong Qualitative Musical notes Weak Qualitative and quantitative Technical No Quantitative approaches to objectify and characterize the attained biosignals, given roughly in chronological order, were Verbal descriptions Musical notes Technical tools As summarized in Table 1.1, the verbal descriptions had the most subjective impact from the author of the description, since it is purely qualitative. A variety of quali- fying adjectives were used as well as vague subjective terms. Avicenna (980–1037), Muslim polymath and Islam’s “Prince of Physicians,” ingeniously compares more than 50 identifiable pulses with natural objects and human actions: “irregular pulse as the flight of a gazelle; stone bullet shot out of a crossbow; scattered leaves” (Hajar 1999). In order to accommodate the difficulties in describing lung sounds, familiar sound descriptions (at that time) were chosen to clarify the distinguishing characteristics (Loudon and Murphy 1984). Descriptive and illustrative sounds were used such as “crepitation of salts in a heated dish,” “noise emitted by healthy lung when compressed in the hand,” “bass note of a musical instrument,” “wet, dry, crackling sound,” or even “cooing of wood pigeon.” As another example, percussion sounds were described as being sonorous, morbid, or dull (Murray and Neilson 1975). The difficulties in the verbal description could be best viewed in terms of Laennec’s observation that the sounds heard with this “cylinder” were easier to distinguish than to describe (Loudon and Murphy 1984), yielding a need for better methodologies. The proposed use of musical notes obviously reduced the subjectivity and provided—for the first time—a quantitative means to objectify biosignals (Table 1.1). While the height of the note could be used for a qualitative coding of biosignals, the rhythm of the successive notes could be used for a quantitative coding. A very nice example is given by notable attempts to objectively describe the pulsatile behavior of the blood pressure with music rhythm. The flute teacher Francois Nicolas Marquet(1687–1759) made pulse to a natural metronome (Marquet 1769), as demonstrated in Fig. 1.12. Up to 30 different pulses were documented by music notes. The last and clearly most successful approach implements the use of technical tools (Table 1.1). Historical progress in technical tools is shortly but conclusively summarized in Geddes and Roeder (2009). They eliminate subjective influence from the observer, since the approach is intrinsically based on quantitative data. With each new advance in these novel techniques, new vistas with previously unforeseen opportunities became exposed. Since there is an enormous diversity of technical 14 1 Fundamentals of Biosignals Fig. 1.12 Coding of heart pulses with musical notes (Marquet 1769). (a) Natural regulated pulse. (b) Three different abnormal pulses including, from top to bottom, discontinuous pulse, irregular intermittent pulse, and irregular pulse arising in between normal pulses tools being introduced, only two historically relevant developments will be shortly mentioned. Representative of tools applied in clinical praxis, Fig. 1.13 demonstrates an ancestor of a sphygmomanometer (greek sphygmos pulse, manometer pressure measuring device) used for recording pulse and blood pressure on, e.g., radial artery. The device stems from developments in the nineteenth century and is acknowledged as the first diagnostic instrument introduced for artificial palpation of the pulse if the thermometer and stethoscope are regarded as clinical aids only. It used cuff-based recording, the methodology that is still nearly unrivaled up to current times, see Sect. 3.1.3.1. The advent of portable technical tools for diagnosis is demonstrated by a sphygmograph (greek sphygmos pulse, grapho write), as shown in Fig. 1.14. This instrument was devised by Dr. Robert Ellis Dudgeon (1820 – 1904) for graphically recording features of the radial pressure pulse, which was beautifully compact and found its way into medical practice around the world. It consists of a lever with an elastic spring placed on the radial artery. The other end of the lever carries a stylus for recording of the pulse on a moving smoked paper. 1.3 Classification of Biosignals 15 Fig. 1.13 The ancestor of the sphygmomanometer for clinical applications (Marey 1858) 1.3 Classification of Biosignals The variety of biosignals is nearly unlimited, as shown in Sects. 1.1 and 1.2. This circumstance makes a unique classification of biosignals impossible. However, there are at least three ways of defining their (overlapping) strategic classification, as demonstrated in Fig. 1.15 and described below. As a first classification method, the existence of biosignals could be taken as a basis for their classification. In particular, Permanent biosignals Induced biosignals 16 1 Fundamentals of Biosignals Fig. 1.14 A sphygmograph according to Dr. Dudgeon for portable application (Dudgeon 1882) would comprise the corresponding classification groups. Permanent biosignals exist without any artificial impact, trigger, or excitation from outside the body and are available at any time (compare Fig. 1.3a). The source of the biosignal is already inside the body. To give some examples, an electrocardiographic signal (Delectrocardiogram) induced by electrical heart muscle excitation (Sect. 4) with the typical peaks P–Q–R–S–T (Fig. 1.15a) and the aforementioned acoustic biosig- nal (D phonocardiogram) induced by the consecutive heart valve closures (Sect. 5) with the typical first and second heart sounds (Fig. 1.15c) belong to the group of permanent biosignals. The group of induced biosignals considers biosignals that are artificially trig- gered, excited, or induced (compare Fig. 1.3b). In contrast to permanent biosignals, induced biosignals exist roughly for the duration of the excitation. That is, as soon as the artificial impact is over, the induced biosignal decays with a certain time constant determined by the body properties. The interaction of the tissue with the induced stimulus, irrespective of the stimulus nature, is then recorded as an induced biosignal. A corresponding example could be given by electric plethysmography, in which an artificial current is induced in the tissue and a voltage along the current path reflects tissue impedance changes (Sect. 4). The voltage is then registered as an induced biosignal (Delectroplethysmogram) with discernible cardiac and respiratory components (Fig. 1.15a). Alternatively, optical oximetry uses artificially induced light while the transmitted light intensity is mainly governed by light absorption through local pulsatile blood volume (Sect. 6). The transmitted light is detected as an induced biosignal, showing a steep systolic increase and a slow diastolic decrease (Fig. 1.15c). In general, the origin of the induced stimulus, e.g., magnetic field from coils above the head for magnetic stimulation, may be different from that of the registered biosignal, e.g., generated electric potentials from electrodes on the head. 1.3 Classification of Biosignals 17 a Existence Permanent Induced plethysmogram R T cardiogram P 1 /fC Electro- Electro- Q S 1 /f C 1 /fR 0s 0.5 s Time 0s 3s Time b Dynamic (Quasi) static Dynamic day night temperature Heart rate Body core 1 /fR 0 h p.m. 12 h p.m. Daytime 0s 20 s Time c Origin Electric Magnetic Muscle R contractions T cardiogram Magneto- myogram Electro- 1 /fC 0s 2s Time 0s 0.5 s Time Mechanic Optic plethysmogram Inspiration Expiration Systole Diastole respirogram Mechano- Opto- 1 /fR 1 /fC 0s 3s Time 0s 0.5 s Time Acoustic Chemical First sound Second sound cardiogram Cortisol amount Phono- 1 /fC 0s 0.5 s Time 6 h a.m. Daytime Thermal Fig. 1.15 The possible classifications of biosignals according to their (a) existence, (b) dynamic, and (c) origin, with indicated heart rate fC , respiratory rate fR , and additional information 18 1 Fundamentals of Biosignals The second classification method considers the dynamic nature of biosignals. Accordingly, (Quasi) Static biosignals Dynamic biosignals can be differentiated. A (quasi) static biosignal carries information in its steady- state level which may exhibit relatively slow changes over time. By contrast, dynamic biosignals yield extensive changes in the time domain, with dynamic processes conveying the physiological information of interest. For instance, the core body temperature would be a (quasi) static biosignal, exhibiting relatively slow circadian changes over 24 h (Sect. 3.1.5). As shown in Fig. 1.15b, it increases during the morning hours and decreases before the onset of sleep (Sect. 3.2.4). On the other hand, the instantaneous beat-to-beat changes of the heart rate would constitute a highly dynamic biosignal (Sect. 3.1.1). The course of the heart rate (Fig. 1.15b) reveals respiratory related oscillation, i.e., an increase during inspiration and a corresponding decrease during expiration. The third classification method uses the origin of biosignals as a basis for their classification. The most prominent origins encompass Electric biosignals Magnetic biosignals Mechanic biosignals Optic biosignals Acoustic biosignals Chemical biosignals Thermal biosignals Other biosignals Correspondingly, electric biosignals comprise, for instance, the aforementioned electrocardiogram (Fig. 1.15a), electroencephalogram, which reflects electrical activity of neurons in the brain, or electromyogram, which reflects electrical activation of muscles. Figure 1.15c schematically depicts an electromyogram which shows bursts of electrical impulses yielding muscle contractions of different strengths. Magnetic biosignals reflect a magnetic field induced by usually nonsta- tionary currents which convey physiological information. As an example, Fig. 1.15c shows a magnetocardiogram reading of magnetic fields emitted by currents during electrical heart excitation (compare peaks in electrocardiogram and magnetocardio- gram in Fig. 1.15). Mechanic biosignals reflect, for instance, body deformations or local body skin vibrations unveiling physiological data. An example is given in Fig. 1.15c by a mechanorespirogram, showing a respiratory cycle from abdominal circumference changes. Optic biosignals benefit from light absorption and scattering, which are related to propagation volume and medium, both changing in a physiologically relevant way. Here, an artificial light is used within the scope of induced biosignals, as already described. As demonstrated in Fig. 1.15c, cardiac pulsations with a clinically relevant time course can be clearly recognized in an optoplethysmogram. 1.4 Trends in Biosignals Monitoring 19 Acoustic biosignals remain for the assessment of diverse body sounds, ranging from cardiac sounds to snoring sounds to swallowing sounds. A phonocardio- gram, as shown in Fig. 1.15c and discussed earlier in Sect. 1.1, mirrors cardiac activity. It is comprised of two discernable heart sounds corresponding to two consecutive heart valve closures. The oscillation amplitude and frequency indicate the closure strength and the valve’s stiffness, respectively. Chemical biosignals reflect chemical composition and its temporal changes in body solids, liquids, and gases. To demonstrate their relevance, Fig. 1.15c shows a typical course of cortisol (D stress hormone) over 24 h in humans, with a peak during the morning hours in order to prepare the body for awakening. Lastly, thermal biosignals usually assess highly heterogeneous mechanisms of heat loss and heat absorption in the body. For instance, the aforementioned body core temperature in Fig. 1.15b constitutes a thermal biosignal. For the sake of completeness, it should be mentioned that the above list of biosignals—classified according to their origin—is obviously not complete. 1.4 Trends in Biosignals Monitoring Biosignals were first employed more than twenty centuries ago, as exemplified in Sect. 1.2.1, and became even more prominent in the twenty-first century. Though having been used since time immemorial, a further advancement of their acquisition, interpretation, and use in the diagnostic approaches was still never out of question. Their developmental history is marked with revolutions rather than continuous improvements, with revolutions usually followed by antagonism.6 Even today, their proper assessment and analysis are the focal point of many research groups worldwide. The obvious reason for these never ending improvements in biosignal monitoring is that the biosignals reflect human health and wellbeing. Biosignals are essential for mankind and not just for increased comfort. In particular, biosignals detail vital physiological phenomena and are relevant not only for the pre-screening of the human functional state and diagnosis of illness but also for subsequent therapy, follow-up treatment, and appraisal of its efficiency. Future trends in biosignal monitoring could be partly deduced from the history of biosignals and the current state-of-the-art technology, as aimed at in Fig. 1.16. From a technical point of view, a qualitative relation exists between the comfort of the sensor system, approximated as the number of applied sensors (horizontal axis), 6 For instance, Kurt Karl Stephan Semm (1927–2003), German gynecologist, who performed the first appendicectomy in 1980 in a laparoscopic way, was heavily criticized by his colleagues and public. Later it was recognized that it not only helps patients recover faster and with less pain, but also prevents deaths in the operating room. Another example would be Ignaz Semmelweiss (1818–1865), Hungarian physician, who was largely ignored or ridiculed when in 1847 he suggested that childbed fever could be drastically reduced if doctors sterilized their hands. 20 1 Fundamentals of Biosignals Significance Multiple Standard technique (= number of multi-parametric sensors Technique under introduction parameters) Novel/upcoming technique C D Multiple sensors Single Multiple parameters Multi-parametric sensor B A E Multiple sensors Single sensor Single/wireless sensor Single parameter Single parameter Single parameter Comfort (= unobtrusiveness) Fig. 1.16 Future vision of physiologic monitoring including standard and novel techniques. Qualitative relationship is given between the significance and comfort of the different monitoring systems, i.e., number of physiological parameters attained versus number of sensors needed, including novel multiparametric sensors. Bold letters refer to cases discussed in the text and the significance of attained biosignals, the latter quantified as the total number of physiologic parameters available to derive (vertical axis). Obviously the oldest and most commonly used systems follow the rule that a single physiological parameter is attained per single sensor (case A in Fig. 1.16). For instance, respiratory rate is usually assessed by a respiratory belt around the thorax, which monitors circumference changes related to breathing. In many cases, a single sensor may not be sufficient to determine a single parameter; thus, two or more sensors might be needed (D multisite recording), as depicted in case B. Here, the common arterial blood pressure recording could be an example, in which decreasing cuff pressure on the upper arm is recorded in parallel to sounds (D Korotkoff sounds) recorded by a microphone over the brachial artery; audible sounds arise due to blood flow turbulences at cuff pressure values corresponding to systolic and diastolic blood pressure. In comparison with case A, case B shows reduced comfort but the same significance because the assessed number of physiologic parameters is the same. If two or more single-parameter sensors (from case A) are applied, then obviously multiple parameters are provided (case C). For instance, sleep monitoring in sleep labs includes the monitoring of a large number of brain, cardiac, and respiratory parameters with the use of the corresponding single-parameter sensors. Numerous parameters are needed here for a comprehensive sleep assessment, e.g., for sleep staging. 1.4 Trends in Biosignals Monitoring 21 Consequently, the technique of multiparametric monitoring could be deduced from cases A and C if multiple parameters are derived through the use of a single sensor, namely, a multiparametric sensor (case D). The multiparametric sensor yields the comfort of a single sensor (case A) in combination with the significance of multiple parameters (case C), as demonstrated in Fig. 1.17. An acoustic body sound sensor on the chest offers this type of monitoring, yielding cardiac activity, respiratory activity, and breathing obstruction from a single spot. In order to achieve an adequate realization of the multiparametric monitoring, a concerted effort should be taken on the part of Novel sensor concepts, e.g., based on advances in technology as miniaturization Optimized sensor location, e.g., proximal instead of distal to increase physiolog- ical content of biosignal Type of recorded signals, e.g., optic instead of electric to get a higher spatial resolution Mutual interrelations and clinical correlations of physiologic parameters to derive, e.g., use of cardiorespiratory interrelations Advanced signal processing methods, e.g., decomposition of signals into its components based on their independence Within this concept, a thorough understanding of the mechanisms of generation and transmission of biosignals, physiologic factors that affect them, a priori knowledge about biosignal characteristics and their appropriate decomposition are necessary. Finally, as shown in case E in Fig. 1.16, the comfort of the subject is significantly increased by the use of wireless data transfer which bypasses the need of an elec- tronic hook-up to the subject. In particular, portable devices for home monitoring profit from cable-free operation. Single sensor e.g., body sound sensor Recorded biosignals Multiple parameters Signal e.g., cardiac activity, Clinical processing& respiratory activity, correlations decomposition breathing obstruction Fig. 1.17 Principle of multiparametric physiologic monitoring 22 1 Fundamentals of Biosignals In contrast to technical considerations in Fig. 1.16, paradigm changes from an application point of view are depicted in Fig. 1.18. As already discussed in Sect. 1.2, the registration of biosignals outlasted centuries, Beginning with a basic inspection (Fig. 1.18) without any (or with simple) instruments. Established clinical applications (Fig. 1.18) followed showing the highest reli- ability but requiring a large effort in all three: applied devices, attending physicians, and laboratory premises. Furthermore, the laboratory window of observation is limited in time, i.e., infrequent (usually vital) physiologic events are easy to miss. Then portable applications (Fig. 1.18) start to emerge which is not only sought in response to the above economic imperatives and need of improved access to diagnosis but also because it may provide a more realistic appraisal of 24-h pathology and more complete information about the physiologic state of the patient. In addition, unattended studies conducted in a home environment allow for improved comfort and familiarity. However, portable recording usually suffers from several problems, such as difficult hook-up of patients, poor assessment of signal quality and data loss, as well as insufficient experience required for proper interpretation of portable data records. PRESENCE Clinical Portable HISTORY FUTURE Basic Pervasive ? Spectacles Garments Watch Fig. 1.18 Paradigm changes from history, which brought basic monitoring functions, to present times, which emphasize advanced functionality in both clinical settings and portable home applications, to the future, which may yield integrated biomedical monitoring not perceivable by patient but easily usable by the physician. The portable LifeShirt system shown is taken from RAE Systems (2011). 1.4 Trends in Biosignals Monitoring 23 Lastly, pervasive applications (Fig. 1.18) seem to govern the research trends in biomedical engineering. The goal of pervasive health care is to provide continuous personalized health monitoring of patients and healthy individuals at any time without constraints of space, time, and physician availability. Unob- trusive monitoring settings include not only daily activities but also demanding circumstances such as physical training or observation of medical treatment. During examination, the presence of the medical staff should be avoided, reducing involuntary stress of the individual and providing a realistic appraisal of pathology or process of recovery. In order to realize a pervasive monitoring system, several Hardware-related System-related requirements must be (ideally) met. The hardware-related requirements include minimal obtrusiveness and compactness, nonhazardous and inexpensive design of the system (Ahamed et al. 2006; Kollmann et al. 2006), resulting in a minimum number of spatially distributed sensors and avoiding tethering patients in a tangle of cables. An inconspicuous and nonstigmatizing design is needed to allow for long-term monitoring (Poh et al. 2010). In particular, unnoticeable monitoring is demanded, with capacitive, magnetic, and optical technologies being especially relevant because of their noncontact physical nature. It is imperative that recorded signals contain a large amount of physiological information. A compromise should be made between long-term wearability and reliable sensor application (Asada et al. 2003). The recorded biosignal should be robust, i.e., its resistance to prevalent environmental impacts such as body motions, temperature changes, or external interference (noise) while wirelessly communicating. In addition, a purposeful preprocessing of the biosignal, its storage and transmission under (very) low- power consumption comprise the most important design characteristics. Differential architectures gain attractiveness for attenuating external interference, with the architecture including one sensing unit for the biosignal and another one only for the environmental interference. Obviously safety and security risks should be accounted for and the risks should be acceptable in relation to an expected monitoring benefit and health regulations (Leitgeb 2010). For instance, economical energy efficient encryption techniques are a prerequisite for data transmission from/to the sensor (Kailas et al. 2010). System-related requirements (design paradigms) include real time, robust, reli- able, and sensitive data interpretation (besides fixed thresholds) to minimize false alarms which frighten the user and increase costs. In addition, bidirectional data transfer is necessary for sensing and (adaptive) therapy, e.g., diagnosis of cardiac state and urgent therapy by defibrillation if necessary. Interaction with the user has to be minimal and must result in a meaningful representation of the state of health. In particular, a context-aware health representation is needed (Kailas et al. 2010), whereas the collected data is presented in different ways to the physician 24 1 Fundamentals of Biosignals (e.g., more details included) and the user (e.g., less details but personalized with a visual representation of health lifestyle tendencies). A distinctive feature of pervasive application is, in contrast to all chronologically preceding applications (Fig. 1.18), that it should be readily accessible to physicians, patients, and even to healthy individuals. In particular, high-risk patients (e.g., with apneas given by a temporal cessation of breathing during sleep) and chronic patients (e.g., chronic heart failure) profit from pervasive monitoring, as well as athletes (interested in cardiorespiratory feedback during rest or training), the elderly (with restricted mobility), or even specialized occupations (e.g., professional drivers) forced to undergo preventive medical checkup to receive more timely treatment.7 That is, the user-friendliness of pervasive systems would play an even more important role, for it is a more relevant issue for healthy individuals than for ill clinical patients. In addition, demographic evolution of the population will be a limitation on the physician’s workload associated with diagnostic examinations and thus raises the need for smart tools in pervasive assistance. Figure 1.18 indicates possible realizations of pervasive monitoring by hardware integration into spectacles, garment, watch, or mobile phone, i.e., by integration into indispensable objects of everyday use. For instance, an electrocardiographic system was newly developed which is integrated in a shirt and operates fully autonomously by thermal and optical energy harvesting from the ambient environment (Leonov et al. 2009). Pervasive applications can be expected to reduce total medical costs,8 increase continuity9 and improve availability10 of health care even in the leading European countries and facilitate the work of physicians. Interestingly, physicians, patients, and healthy subjects appear to accept information technology to assist in their decision making (Ahamed et al. 2006), to turn the physician’s attention to the person if necessary (Kollmann et al. 2006), and to be an objective guide through the positive way of life (Connelly et al. 2006). It appears that there is much room for radical improvements of the conven- tional physiological sensing and monitoring techniques because of the inflexible application of classical sensors as well as established signal processing. However, it cannot be expected that future revolutions or multiparametric sensors will replace established sensors anytime soon or even the stethoscope-bearing clinician, but 7 There is data from the UK (Flemons et al. 2003) which suggest that the wait for investigation with the polysomnography (D comprehensive clinical monitoring in sleep lab, Sect. 3.2.4) versus portable monitoring was reduced from a median of 47 days to 18 days. 8 As reported in Flemons et al. 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IEEE Engineering in Medicine and Biology Magazine 29(3), 70–76 (2010). P.D. Welsby, G. Parry, D. Smith: The stethoscope: Some preliminary investigations. Postgraduate Medical Journal 79, 695–698 (2003). Chapter 2 Physiological and Functional Basis An introduction will be given into physiological structures and fundamental mechanisms behind these structures that are involved in the genesis of biosignals in humans. We start with the cells, the smallest units of life, go over the larger structures of vital organs, and end up with the circulatory system which involves all the preceding structures. Profound knowledge of the physiological situation is crucial for a proper understanding of a biosignal’s generation phenomena and a correct interpretation of biosignals—assessed by technical means—from a physiological point of view. It should be noted that physiological and functional structure will be considered only from a biosignal perspective, since extensive literature is available on general physiological and functional structure in humans. 2.1 Cell The cell is the smallest autonomous unit of life and represents the functional and structural basic unit in multicellular organisms, such as humans with about 1014 cells (Silverthorn 2009). In actuality, the cell represents the only origin of any biosignal in the widest sense. Thus an overview will be given about the cell’s basic structure and the cell’s most relevant bioelectric phenomena. 2.1.1 Functional Structures The morphological structure of the cell is tightly related to its function. There is a huge variety of cells, as illustrated in Fig. 2.1. However, each cell shows a certain basic structure, as depicted in Fig. 2.2, that is given by: E. Kaniusas, Biomedical Signals and Sensors I, Biological and Medical Physics, 27 Biomedical Engineering, DOI 10.1007/978-3-642-24843-6 2, © Springer-Verlag Berlin Heidelberg 2012 28 2 Physiological and Functional Basis a b Golgi Endoplasmic Extracellular Lysosome apparatus Nucleus reticulum space Nucleus Mitochondria Microvilli 2µm 2µm Mitochondria Red blood cell Cell membrane Fig. 2.1 Microscope images of different types of mammalian cells. (a) Monocyte in mouse spleen. (b) Epithelial cells from the proximal tubule of a mouse kidney. The photographs were taken by transmission electron microscopy (Wang and Sougrat 2011). The basic structure of the cell is indicated; compare with Fig. 2.2 Outer cell membrane enclosing Cell content, i.e., cytoplasm, with different Specialized subunits within, i.e., organelles The size of the human cell varies between 5 and 150 m with a typical size of about 10 m while the shape varies considerably from cell to cell. However, some cells, such as nerve cells (known as neurons), attain impressive dimensions of about 1 m if the cell’s appendages (dendrites and axons) are considered. Figure 2.1 demonstrates two different types of mammalian cells, whereas Fig. 2.20 depicts a nerve cell embedded into a network of surrounding nerve cells via connecting dendrites and axons. The membrane of the cell separates the interior of the cell (intracellular space) from the outside environment (extracellular space) and has a typical thickness of about 7–8 nm.11 The membrane serves not only as a barrier between intracellular and extracellular space anchoring the cytoskeleton, but also controls mass transfer in and out of the cytoplasm and provides a means of communication between the cell and neighboring cells or its environment. A specific electrical behavior of the membrane and its components (e.g., channel proteins) plays a crucial role here (Sect. 2.1.2), especially for cell communication and induction of biosignals. 11 It is interesting to note that the membrane thickness is less than the size of the cell by factor of about 1,000 so that the cell, in relation to its membrane and membrane fluidity, can be imagined as a thin-walled balloon filled with water. 2.1 Cell 29 a b Fig. 2.2 (a) Simplified illustration of a section of a cell with different organelles floating in the cytoplasm and enfolded by the cell membrane. (b) Microscope image of a comparable cell section, namely, epithelial cell from the proximal tubule of a mouse kidney. The micrograph was taken by transmission electron microscopy (Wang and Sougrat 2011) As schematically depicted in Fig. 2.3, lipid molecules make up the bulk of the membrane, arranged in two layers forming a bilayer. In fact, this bilayer structure spontaneously results from electrostatic interactions between 30 2 Physiological and Functional Basis Fig. 2.3 Simplified Glycoproteins illustration of cell membrane Negative with an embedded channel charge - protein (transport protein), H20 adhered glycoproteins, and molecules - hydrated ions passing the Ion Extracellular channel Glycolipids space 7-8 nm Intracellular Lipid Channel space molecules Membrane protein protein Aqueous milieu full of ional and polar structures,12 which enclose the membrane from both sides and Unique electrostatic b

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