Nonlinear Biomedical Signal Processing, Fuzzy Logic, Neural Networks, and New Algorithms / Edition 1

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Overview

Biomedical / Electrical Engineering Nonlinear Biomedical Signal Processing Volume I: Fuzzy Logic, Neural Networks, and New Algorithms A volume in the IEEE Press Series on Biomedical Engineering Metin Akay, Series Editor For the first time, eleven experts in the fields of signal processing and biomedical engineering have contributed to an edition on the newest theories and applications of fuzzy logic, neural networks, and algorithms in biomedicine. Nonlinear Biomedical Signal Processing, Volume I provides comprehensive coverage of nonlinear signal processing techniques. In the last decade, theoretical developments in the concept of fuzzy logic have led to several new approaches to neural networks. This compilation delivers plenty of real-world examples for a variety of implementations and applications of nonlinear signal processing technologies to biomedical problems. Included here are discussions that combine the various structures of Kohenen, Hopfield, and multiple-layer "designer" networks with other approaches to produce hybrid systems. Comparative analysis is made of methods of genetic, back-propagation, Bayesian, and other learning algorithms. Topics covered include:
*Uncertainty management
*Analysis of biomedical signals
*A guided tour of neural networks
*Application of algorithms to EEG and heart rate variability signals
*Event detection and sample stratification in genomic sequences
*Applications of multivariate analysis methods to measure glucose concentrationNonlinear Biomedical Signal Processing, Volume I is a valuable reference tool for medical researchers, medical faculty and advanced graduate student, s as well as for practicing biomedicalengineers. Nonlinear Biomedical Signal Processing, Volume I is an excellent companion to Nonlinear Biomedical Signal Processing, Volume II: Dynamic Analysis and Modeling.
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Editorial Reviews

Booknews
To launch the series that will introduce current and emerging technologies to biomedical and electrical engineering practitioners, researchers, and students, specialists from North America, China, Australia, and Israel review applications for neural networks that have been derived from the theoretical developments in fuzzy logic over the past decade. They consider such topics as uncertainty management, analyzing biomedical signals, a guided tour of neural networks, applying algorithms to electro-encephalograms and heart-rate variability signals, event detection and sample stratification in genomic sequences, and using multivariate analysis methods to measure glucose concentration. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

Meet the Author

About the Editor Metin Akay is currently an assistant professor at Dartmouth College. A noted speaker, editor, and author, Dr. Akay has spent several years conducting research in the areas of fuzzy neural networks and signal processing, wavelet transform, and detection and estimation theory. His biomedical research areas include the autonomic nervous system, maturation, respiratory-related evoked response, noninvasive detection of coronary artery disease, and estimation of cardiac output. Dr. Akay is the founding series editor of the IEEE Press Series on Biomedical Engineering. In 1997 he received the prestigious Early Career Achievement Award from the IEEE Engineering in Medicine and Biology Society (EMBS). He is the program chair of both the annual IEEE EMBS Conference and Summer School for 2001. Dr. Akay has published several papers in the field and authored or coauthored eleven books, including Time Frequency and Wavelets in Biomedical Signal Processing (IEEE Press, 1998) and Nonlinear Biomedical Signal Processing, Volume II: Dynamic Analysis and Modeling (IEEE Press, 2000). He holds two U.S. patents.
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Table of Contents

Preface.
List of Contributors.
Uncertainty Management in Medical Applications (B. Bouchon-Meunier).
Applications of Fuzzy Clustering to Biomedical Signal Processing and Dynamic System (A. Geva).
Neural Networks: A Guided Tour (S. Haykin).
Neural Networks in Processing and Analysis of Biomedical Signals (H. Nazeran & K. Behbehani).
Rare Event Detection in Genomic Sequences by Neural Networks and Sample Stratification (W. Choe, et al.).
An Axiomatic Approach to Reformulating Radial Basis Neural Networks (N. Karayiannis).
Soft Learning Vector Quantization and Clustering Algorithms Based on Reformulation (N. Karayiannis).
Metastable Associative Network Models of Neuronal Dynamics Transition During Sleep (M. Nakao & M. Yamamoto).
Artificial Neural Networks for Spectroscopic Signal Measurement (C.-W. Lin, et al.).
Applications of Feed-Forward Neural Networks in the Electrogastrogram (Z. Lin & J. Chen).
Index.
About the Editor.
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