Stochastic complexity measures for physiological signal analysis

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-...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 1998-09, Vol.45 (9), p.1186-1191
Main Authors: Rezek, I.A., Roberts, S.J.
Format: Article
Language:English
Subjects:
Anesthesia
Biological and medical sciences
Biomedical engineering
Biomedical measurements
Cheyne-Stokes Respiration
Electroencephalography
Entropy
Feature extraction
Fourier Analysis
Frequency measurement
Humans
Mathematical models
Medical sciences
Monitoring, Physiologic
Motor Cortex - physiology
Nonlinear Dynamics
Physiology
Power system modeling
Predictive models
Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects)
Random processes
Regression analysis
Signal analysis
Signal processing
Signal Processing, Computer-Assisted
Sleep - physiology
State space methods
Stochastic Processes
Technology. Biomaterials. Equipments. Material. Instrumentation
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Summary: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.
ISSN:0018-9294
1558-2531
DOI:10.1109/10.709563