Advances In Processing And Pattern Analysis Of Biological Signals

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Advances in Processing and Pattern Analysis of Biological Signals

Advances in Processing and Pattern Analysis of Biological Signals
Author :
Publisher : Springer Science & Business Media
Total Pages : 429
Release :
ISBN-10 : 9781475790986
ISBN-13 : 1475790988
Rating : 4/5 (988 Downloads)

Book Synopsis Advances in Processing and Pattern Analysis of Biological Signals by : I. Gath

Download or read book Advances in Processing and Pattern Analysis of Biological Signals written by I. Gath and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years there has been rapid progress in the development of signal processing in general, and more specifically in the application of signal processing and pattern analysis to biological signals. Techniques, such as parametric and nonparametric spectral estimation, higher order spectral estimation, time-frequency methods, wavelet transform, and identifi cation of nonlinear systems using chaos theory, have been successfully used to elucidate basic mechanisms of physiological and mental processes. Similarly, biological signals recorded during daily medical practice for clinical diagnostic procedures, such as electroen cephalograms (EEG), evoked potentials (EP), electromyograms (EMG) and electrocardio grams (ECG), have greatly benefitted from advances in signal processing. In order to update researchers, graduate students, and clinicians, on the latest developments in the field, an International Symposium on Processing and Pattern Analysis of Biological Signals was held at the Technion-Israel Institute of Technology, during March 1995. This book contains 27 papers delivered during the symposium. The book follows the five sessions of the symposium. The first section, Processing and Pattern Analysis of Normal and Pathological EEG, accounts for some of the latest developments in the area of EEG processing, namely: time varying parametric modeling; non-linear dynamic modeling of the EEG using chaos theory; Markov analysis; delay estimation using adaptive least-squares filtering; and applications to the analysis of epileptic EEG, EEG recorded from psychiatric patients, and sleep EEG.


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