PREFACE

From the Second to the Third Edition

The field of biomedical signal analysis has been advancing rapidly over the past few years. More and more techniques are being developed to analyze not only the well-known signals from the previous century, but new types of biomedical signals are being explored, acquired, studied, and analyzed for various novel applications. Courses on biomedical instrumentation and signal analysis are increasingly common and popular elements in engineering curricula.

The previous editions of the book were adopted for study by several students, teachers, and researchers around the world. Keeping in mind the appreciative comments received, we have maintained the first six chapters of the book with minimal change. We have also maintained the style and spirit of the original book.

The minor modifications in the new edition include the following: additional discussions and illustrations related to the neuron, action potential, and photoplethysmography (Chapter 1); the discrete Fourier transform, frequency response, pole-zero plots, and the relationships between various representations of signals, systems, and transforms (Chapter 3); and turning points, zero-crossings, and turns count (Chapter 5). Several equations and figures have been revised and reformatted for improved comprehension. A few sections have been relocated and revised for improved connection to related sections.

The major changes in the Third Edition are present in the last four chapters, which represent thoroughly revised, expanded, updated, and reorganized versions of the last three chapters in the Second Edition. Substantial new material has been added on modeling and analysis of nonstationary and multicomponent signals as well as pattern recognition and diagnostic decision. Detailed discussions have been added on the Kalman filter, dictionary learning, electrophysiological modeling, detection of epileptic seizures, analysis of ventricular fibrillation, and diagnosis of Parkinson's disease. Additional discussions on the strengths and limitations of computer-aided diagnosis are provided at the end of Chapter 10.

The following list describes some of the new topics, techniques, and applications presented in the Third Edition:

• Mathematical and electrophysiological modeling of the heart at the cellular, tissue, and organ levels.
• The Kalman filter and dictionary-learning methods for sophisticated analysis of nonstationary, multicomponent, and multisource signals.
• Detection of ventricular fibrillation and wavelet analysis for studies on cardiopulmonary resuscitation.
• Neural decoding for control of prostheses using the Kalman filter.
• Techniques for adaptive decomposition of multicomponent and multisource signals, including dictionary learning, nonnegative matrix decomposition, and decomposition-based adaptive time-frequency distributions.
• Detection of epileptic seizures using dictionary learning.
• Detection of T-wave alternans in ECG signals.
• Extraction of the fetal ECG from single-channel maternal ECG.
• EEG analysis for brain-computer interfaces.
• Detection of sleep apnea.
• Monitoring Parkinson's disease using multimodal signal analysis.

References appear at the end of each chapter to facilitate chapter-by-chapter access to pdf files through digital libraries. The pdf files include hyperlinks to sections, figures, equations, references, and websites cited for efficient navigation.

In order to control and limit the number of pages in the book, the page size has been increased. This change has facilitated improved formatting and layout of the text and figures. In spite of the addition of substantial new material, the number of pages in the Third Edition is almost the same as that in the Second Edition.

Expanded and improved teaching and learning resources are available at

the companion website: https://wiley.com/go/rangayyan3e, and also at

https://github.com/srikrishnan1972/Biomedical-Signal-Analysis

The Intended Audience

As with the previous editions, the Third Edition is directed at engineering students in their final (senior) year of undergraduate studies or in their graduate studies. Electrical Engineering students with a good background in signals and systems will be well prepared for the material in the book. Students in other engineering disciplines, or in computer science, physics, mathematics, or geosciences, should also be able to appreciate the material in the book. A course on digital signal processing or digital filters would form a useful link to the material in the present book, but a capable student without this background should be able to gain a basic understanding of the subject matter. The introductory materials on systems, filters, and transforms provided in Chapter 3 should assist the reader without formal training on the same topics. Practicing engineers, computer scientists, information technologists, medical physicists, and specialists in machine learning, artificial intelligence, and data processing working in diverse areas such as telecommunications, seismic and geophysical applications, biomedical applications, and hospital information systems may find the book useful in their quest to learn advanced techniques for signal analysis. They could draw inspiration from other applications of signal processing or analysis, and satisfy their curiosity regarding computer applications in medicine, digital healthcare, and computer-aided medical diagnosis.

Teaching and Learning Plans

The book starts with an illustrated introduction to biomedical signals in Chapter 1. Chapter 2 continues the introduction, with emphasis on the analysis of multiple channels of correlated signals.

Chapter 3 deals exclusively with filtering of signals for removal of artifacts as an important step before signal analysis. The basic properties of systems and transforms as well as signal processing techniques are reviewed and described where required. The chapter is written as a mix of theory and application so as to facilitate easy comprehension of the basics of signals, systems, and transforms. The emphasis is on the application of filters to particular problems in biomedical signal analysis. A large number of illustrations are included to provide a visual representation of the problem and the effectiveness of the various filtering methods described.

Chapter 4 presents techniques that are particularly useful in the detection of events in biomedical signals. Analysis of waveshape and waveform complexity of events and components of signals is the focus of Chapter 5. Techniques for frequency-domain characterization of biomedical signals and systems are presented in Chapter 6. A number of diverse examples are provided in all of the chapters. Attention is directed to the characteristics of the problems that are encountered when analyzing and interpreting biomedical signals, rather than to any specific diagnostic application with particular signals.

The material in the book up to and including Chapter 6 provides more than adequate material for a one-semester (13-week) course at the senior (fourth-year) engineering level. Our own teaching experience indicates that this material will require about hours of lectures. It would be desirable to augment the lectures with about 12 hours of tutorials (problem-solving sessions) and 10 laboratory sessions.

Modeling biomedical signal-generating processes and systems for parametric representation and analysis is the subject of Chapter 7. Chapters 8 and 9 deal with adaptive analysis of nonstationary, multicomponent, and multisource signals. The topics in these chapters are of high mathematical complexity and are not suitable for undergraduate courses. Some sections may be selected and included in a first course on biomedical signal analysis if there is particular interest in these topics. Otherwise, the three chapters could be left for self-study by those in need of the techniques, or included in an advanced course.

Chapter 10 presents the final aspect of biomedical signal analysis, and provides an introduction to pattern classification, diagnostic decision-making, computer-aided diagnosis, and computer-aided healthcare. Although this topic is advanced in nature and could form a graduate-level course on its own, the material is introduced so as to draw the entire exercise of biomedical signal analysis to its concluding stage of diagnostic decision and healthcare. It is recommended that a few sections from this chapter be included even in a first course on biomedical signal analysis so as to give the students a flavor of the end result.

Each chapter includes a number of study questions and problems to facilitate preparation for tests and examinations. A number of laboratory exercises are also provided at the end of each chapter, which could be used to formulate hands-on exercises with real-life signals. Data files related to the problems and exercises at the end of each chapter are available at the site

https://github.com/srikrishnan1972/Biomedical-Signal-Analysis

It is strongly recommended that the first one or two laboratory sessions in the course include visits to a local hospital, health sciences center, or clinical laboratory to view and experience procedures related to biomedical signal acquisition and analysis in a practical (clinical) setting. Signals acquired from fellow students and instructors could form interesting and motivating...