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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
Published on: June 27, 2013
Stochastic complexity measures for physiological signal analysis
1Department of Electrical and Electronic Engineering, Imperial College of Science, Technology, and Medicine, London, U.K. i.rezek@ic.ac.uk
Abstract:
Traditional feature extraction methods describe signals in terms of amplitude and frequency. This paper takes a paradigm shift and investigates four stochastic-complexity features. Their advantages are demonstrated on synthetic and physiological signals; the latter recorded during periods of Cheyne-Stokes respiration, anesthesia, sleep, and motor-cortex investigation.
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