1 Introduction to Biosignal Processing

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machine learning methods can be used to identify an upcoming seizure. These stat-

istical methods are intended to distinguish stochastic fluctuations from deterministic

ones or to recognise significant changes before they occur themselves. The mathem-

atical relationships that can be used to predict the probability of a future change from

current readings, in order to detect possible signs of a developing pathology, often lie

in the theory of complex systems and non-linear dynamics. For example, in normal

heart rhythms one observes stable rhythms (trajectories in phase space), so-called at-

tractors, which, however, can sometimes pass into chaos via so-called bifurcations

and thus into pathological states such as ventricular fibrillation. Perhaps in the not

too distant future, this insight can be used for the early detection and diagnosis of

such incidents to the benefit of the patient.

This book provides an introduction to the theory and principal methodology of

biosignal processing, describes the origins of the most common human biosignals,

and teaches the techniques for measurement and modern information processing us-

ing LTSpice and Matlab/Simulink². After a brief introduction and historical review to

the individual topics of electrophysiology and analogue and digital signal processing,

the reader is introduced to the practice of biosignal processing with Matlab/Simulink

through selected applications of the methodology learned.

The reader is given an overview of the variety of human biosignals (cf.

Fig-

ure 1.1) and is introduced to the topic on the basis of selected biosignals, such as

muscle activity in the electromyogram (EMG), the activity of the heart muscle in the

electrocardiogram (ECG), the activity of the nerve cells of the brain in the electro-

encephalogram (EEG) or the measurement of the oxygen saturation of the blood in

the photoplethysmogram (PPG). In this context, the fundamentals of deriving, pre-

processing, recognising and interpreting these signals are taught with the help of the

simulation environment LTSpice and the programming language Matlab/Simulink.

Carrying out the exercises in Matlab/Simulink also teaches the necessary techniques

of practical biosignal processing and offers the opportunity to apply acquired theor-

etical knowledge in practice. For a better overview of the formula symbols, units and

constants used, a table sorted by chapter is provided in chapter 7 for reference.

The current state of research and development is presented in three fields of ap-

plication from the authors’ research topics: (i) mathematical modelling and analysis

of signals from the heart / circulatory system, and analysis of the electrical activity of

(ii) muscles and (iii) the brain. Each chapter includes a series of examples and exer-

cises, as well as a presentation of the future perspectives of the respective field.

2 The MathWorks, Inc.