title: Stochastic complexity measures for physiological signal analysis.
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.
authors.affiliation: Department of Electrical and Electronic Engineering, Imperial College of Science, Technology, and Medicine, London, U.K. i.rezek@ic.ac.uk
authors.forename: I A
authors.initials: IA
authors.lastname: Rezek
authors.forename: S J
authors.initials: SJ
authors.lastname: Roberts
mesh_terms: D000758:Anesthesia; D002639:Cheyne-Stokes Respiration; D004569:Electroencephalography; D005583:Fourier Analysis; D006801:Humans; D008991:Monitoring, Physiologic; D009044:Motor Cortex / Q000502:physiology; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted; D012890:Sleep / Q000502:physiology; D013269:Stochastic Processes