Biomedical Signal Analysis: A Case-Study Approach

✍ Scribed by Rangaraj M. Rangayyan

• Publisher: Wiley-IEEE Press
• Year: 2001
• Tongue: English
• Leaves: 552
• Series: IEEE Press Series on Biomedical Engineering
• Edition: 1
• Category: Library

✦ Synopsis

An authoritative assessment of the problems and applications of biomedical signals, rooted in practical case studies The development of techniques to analyze biomedical signals, such as electro-cardiograms, has dramatically affected countless lives by making possible improved noninvasive diagnosis, online monitoring of critically ill patients, and rehabilitation and sensory aids for the handicapped. Rangaraj Rangayyan supplies a practical, hands-on field guide to this constantly evolving technology in Biomedical Signal Analysis, focusing on the diagnostic challenges that medical professionals continue to face. Dr. Rangayyan applies a problem-solving approach to his study. Each chapter begins with the statement of a different biomedical signal problem, followed by a selection of real-life case studies and the associated signals. Signal processing, modeling, or analysis techniques are then presented, starting with relatively simple ''textbook'' methods, followed by more sophisticated research approaches. The chapter concludes with one or more application solutions; illustrations of real-life biomedical signals and their derivatives are included throughout. Among the topics addressed are: * Concurrent, coupled, and correlated processes * Filtering for removal of artifacts * Event detection and characterization * Frequency-domain characterization * Modeling biomedical systems * Analysis of nonstationary signals * Pattern classification and diagnostic decision The chapters also present a number of laboratory exercises, study questions, and problems to facilitate preparation for class examinations and practical applications. Biomedical Signal Analysis provides a definitive resource for upper-level under-graduate and graduate engineering students, as well as for practicing engineers, computer scientists, information technologists, medical physicists, and data processing specialists.