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Titlebook: Advances in Processing and Pattern Analysis of Biological Signals; Isak Gath,Gideon F. Inbar Book 1996 Springer Science+Business Media New

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期刊全称Advances in Processing and Pattern Analysis of Biological Signals
影响因子2023Isak Gath,Gideon F. Inbar
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图书封面Titlebook: Advances in Processing and Pattern Analysis of Biological Signals;  Isak Gath,Gideon F. Inbar Book 1996 Springer Science+Business Media New
影响因子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, Process
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Compressor Instability with Integral Methodslysis of EEG signals recorded during seizure activity in comparison with on-going signals allowed us to formulate a hypothesis about the generation of epileptic activity. According to this model, epilepsy should be envisaged as a dynamical disease of neuronal networks, that may exhibit different typ
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Compressor Surge and Stall: An Introduction,rithms for segmentation, classification and compression of vector EEG are described. The statistics of the suggested measures for segmentation and classification are discussed. The algorithms were evaluated on four patients, yielding mean correct sleep staging of about 85%.
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https://doi.org/10.1007/978-3-030-94331-8ivity, as well as for mapping of epileptic foci in patients with intractable focal epilepsy who are candidates for surgery. Most processing methods applied to the EEG require linearity; but, before the question of linearity/non linearity is handled, the non stationary character of the EEG signal has
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https://doi.org/10.1007/978-3-030-94331-8l is described with different applications in the study of dynamical changes of the brain responses and in artifact removal. A study of the modifications induced in the EEG by a sensory stimulation via ARX and AR models is also described. Finally, the wavelet transform is employed for the reconstruc
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https://doi.org/10.1007/978-94-009-4021-5her there is a partial blocking of the nerve, or synchronization disorders. Two algorithms for detecting the responses and the amount of synchronization, which are insensitive to asynchronization problems, are introduced. The first is based on estimating the spectrum of the recorded evoked response,
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