Biosignal and Biomedical Image Processing MATLAB based Applications

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 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 ntroductory 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 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.