Just as a cardiologist can benefit from an oscilloscope-type display of the ECG without a deep understanding of electronics, an engineer can benefit from advanced signal processing tools without always understanding the details of the underlying mathematics. Through the use of extensive MATLAB examples and problems, "Biosignal and Medical Image Processing" provides readers with the necessary knowledge to successfully evaluate and apply a wide range of signal and image processing tools. The book begins with an extensive introductory section and a review of basic concepts before delving into more complex areas. Topics discussed include classical spectral analysis, basic digital filtering, advanced spectral methods, spectral analysis for time-variant spectrums, continuous and discrete wavelets, optimal and adaptive filters, and principal and independent component analysis. In addition, image processing is discussed in several chapters with examples taken from medical imaging. Finally, new to this second edition are two chapters on classification that review linear discriminators, support vector machines, cluster techniques, and adaptive neural nets. Comprehensive yet easy to understand, this revised edition of a popular volume seamlessly blends theory with practical application. Most of the concepts are presented first by providing a general understanding, and second by describing how the tools can be implemented using the MATLAB software package. Through the concise explanations presented in this volume, readers gain an understanding of signal and image processing that enables them to apply advanced techniques to applications without the need for a complex understanding of the underlying mathematics.

This third edition of a bestseller offers comprehensive coverage of the major approaches in biomedical signal and image processing. It provides a complete set of signal processing tools, including diagnostic decision-making tools, and classification methods. Thoroughly revised and updated, it supplies important new material on nonlinear methods for describing and classifying signals, including entropy-based methods and scaling methods. This edition covers data "cleaning" methods commonly used in such areas as heart rate variability studies, along with actual examples. It also includes new end-of-chapter problems.