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Biosignal and Biomedical Image Processing MATLAB based Applications - John L. Semmlow

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Although great care has been taken to provide accurate and current information, neither the author(s) nor the publisher, nor anyone else associated with this publication, shall be liable for any loss, damage, or liability directly or indirectly caused or alleged to be caused by this book. The material contained herein is not intended to provide specific advice or recommendations for any specific situation.

Trademark notice: Product or corporate names may be trademarks or registered trademarks and are used only for identification and explanation without intent to infringe.

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ISBN: 0–8247-4803–4

This book is printed on acid-free paper.

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Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming, and recording, or by any information storage and retrieval system, without permission in writing from the publisher.

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Copyright 2004 by Marcel Dekker, Inc. All Rights Reserved.

Copyright 2004 by Marcel Dekker, Inc. All Rights Reserved.

Copyright 2004 by Marcel Dekker, Inc. All Rights Reserved.

To Lawrence Stark, M.D., who has shown me the many possibilities . . .

Copyright 2004 by Marcel Dekker, Inc. All Rights Reserved.

Series Introduction

Over the past 50 years, digital signal processing has evolved as a major engineering discipline. The fields of signal processing have grown from the origin of fast Fourier transform and digital filter design to statistical spectral analysis and array processing, image, audio, and multimedia processing, and shaped developments in high-performance VLSI signal processor design. Indeed, there are few fields that enjoy so many applications—signal processing is everywhere in our lives.

When one uses a cellular phone, the voice is compressed, coded, and modulated using signal processing techniques. As a cruise missile winds along hillsides searching for the target, the signal processor is busy processing the images taken along the way. When we are watching a movie in HDTV, millions of audio and video data are being sent to our homes and received with unbelievable fidelity. When scientists compare DNA samples, fast pattern recognition techniques are being used. On and on, one can see the impact of signal processing in almost every engineering and scientific discipline.

Because of the immense importance of signal processing and the fastgrowing demands of business and industry, this series on signal processing serves to report up-to-date developments and advances in the field. The topics of interest include but are not limited to the following:

Signal theory and analysis

Statistical signal processing

Speech and audio processing

Copyright 2004 by Marcel Dekker, Inc. All Rights Reserved.

Image and video processing

Multimedia signal processing and technology

Signal processing for communications

Signal processing architectures and VLSI design

We hope this series will provide the interested audience with high-quality, state-of-the-art signal processing literature through research monographs, edited books, and rigorously written textbooks by experts in their fields.

Copyright 2004 by Marcel Dekker, Inc. All Rights Reserved.

Preface

Signal processing can be broadly defined as the application of analog or digital techniques to improve the utility of a data stream. In biomedical engineering applications, improved utility usually means the data provide better diagnostic information. Analog techniques are applied to a data stream embodied as a timevarying electrical signal while in the digital domain the data are represented as an array of numbers. This array could be the digital representation of a timevarying signal, or an image. This text deals exclusively with signal processing of digital data, although Chapter 1 briefly describes analog processes commonly found in medical devices.

This text should be of interest to a broad spectrum of engineers, but it is written specifically for biomedical engineers (also known as bioengineers). Although the applications are different, the signal processing methodology used by biomedical engineers is identical to that used by other engineers such electrical and communications engineers. The major difference for biomedical engineers is in the level of understanding required for appropriate use of this technology. An electrical engineer may be required to expand or modify signal processing tools, while for biomedical engineers, signal processing techniques are tools to be used. For the biomedical engineer, a detailed understanding of the underlying theory, while always of value, may not be essential. Moreover, considering the broad range of knowledge required to be effective in this field, encompassing both medical and engineering domains, an in-depth understanding of all of the useful technology is not realistic. It is important is to know what

Copyright 2004 by Marcel Dekker, Inc. All Rights Reserved.

tools are available, have a good understanding of what they do (if not how they do it), be aware of the most likely pitfalls and misapplications, and know how to implement these tools given available software packages. The basic concept of this text is that, just as the cardiologist can benefit from an oscilloscope-type display of the ECG without a deep understanding of electronics, so a biomedical engineer can benefit from advanced signal processing tools without always understanding the details of the underlying mathematics.

As a reflection of this philosophy, most of the concepts covered in this text are presented in two sections. The first part provides a broad, general understanding of the approach sufficient to allow intelligent application of the concepts. The second part describes how these tools can be implemented and relies primarily on the MATLAB software package and several of its toolboxes.

This text is written for a single-semester course combining signal and image processing. Classroom experience using notes from this text indicates that this ambitious objective is possible for most graduate formats, although eliminating a few topics may be desirable. For example, some of the introductory or basic material covered in Chapters 1 and 2 could be skipped or treated lightly for students with the appropriate prerequisites. In addition, topics such as advanced spectral methods (Chapter 5), time-frequency analysis (Chapter 6), wavelets (Chapter 7), advanced filters (Chapter 8), and multivariate analysis (Chapter 9) are pedagogically independent and can be covered as desired without affecting the other material.

Although much of the material covered here will be new to most students, the book is not intended as an “introductory” text since the goal is to provide a working knowledge of the topics presented without the need for additional course work. The challenge of covering a broad range of topics at a useful, working depth is motivated by current trends in biomedical engineering education, particularly at the graduate level where a comprehensive education must be attained with a minimum number of courses. This has led to the development of “core” courses to be taken by all students. This text was written for just such a core course in the Graduate Program of Biomedical Engineering at Rutgers University. It is also quite suitable for an upper-level undergraduate course and would be of value for students in other disciplines who would benefit from a working knowledge of signal and image processing.

It would not be possible to cover such a broad spectrum of material to a depth that enables productive application without heavy reliance on MATLABbased examples and problems. In this regard, the text assumes the student has some knowledge of MATLAB programming and has available the basic MATLAB software package including the Signal Processing and Image Processing Toolboxes. (MATLAB also produces a Wavelet Toolbox, but the section on wavelets is written so as not to require this toolbox, primarily to keep the number of required toolboxes to a minimum.) The problems are an essential part of

Copyright 2004 by Marcel Dekker, Inc. All Rights Reserved.

this text and often provide a discovery-like experience regarding the associated topic. A few peripheral topics are introduced only though the problems. The code used for all examples is provided in the CD accompanying this text. Since many of the problems are extensions or modifications of examples given in the chapter, some of the coding time can be reduced by starting with the code of a related example. The CD also includes support routines and data files used in the examples and problems. Finally, the CD contains the code used to generate many of the figures. For instructors, there is a CD available that contains the problem solutions and Powerpoint presentations from each of the chapters. These presentations include figures, equations, and text slides related to chapter. Presentations can be modified by the instructor as desired.

In addition to heavy reliance on MATLAB problems and examples, this text makes extensive use of simulated data. Except for the section on image processing, examples involving biological signals are rarely used. In my view, examples using biological signals provide motivation, but they are not generally very instructive. Given the wide range of material to be presented at a working depth, emphasis is placed on learning the tools of signal processing; motivation is left to the reader (or the instructor).

Organization of the text is straightforward. Chapters 1 through 4 are fairly basic. Chapter 1 covers topics related to analog signal processing and data acquisition while Chapter 2 includes topics that are basic to all aspects of signal and image processing. Chapters 3 and 4 cover classical spectral analysis and basic digital filtering, topics fundamental to any signal processing course. Advanced spectral methods, covered in Chapter 5, are important due to their widespread use in biomedical engineering. Chapter 6 and the first part of Chapter 7 cover topics related to spectral analysis when the signal’s spectrum is varying in time, a condition often found in biological signals. Chapter 7 also covers both continuous and discrete wavelets, another popular technique used in the analysis of biomedical signals. Chapters 8 and 9 feature advanced topics. In Chapter 8, optimal and adaptive filters are covered, the latter’s inclusion is also motivated by the time-varying nature of many biological signals. Chapter 9 introduces multivariate techniques, specifically principal component analysis and independent component analysis, two analysis approaches that are experiencing rapid growth with regard to biomedical applications. The last four chapters cover image processing, with the first of these, Chapter 10, covering the conventions used by MATLAB’s Imaging Processing Toolbox. Image processing is a vast area and the material covered here is limited primarily to areas associated with medical imaging: image acquisition (Chapter 13); image filtering, enhancement, and transformation (Chapter 11); and segmentation, and registration (Chapter 12).

Many of the chapters cover topics that can be adequately covered only in a book dedicated solely to these topics. In this sense, every chapter represents a serious compromise with respect to comprehensive coverage of the associated

Copyright 2004 by Marcel Dekker, Inc. All Rights Reserved.