Stochastic complexity measures for physiological signal analysis

Author

Rezek, I.A. ; Roberts, S.J.

Author_Institution

Dept. of Electr. & Electron. Eng., Imperial Coll. of Sci., Technol. & Med., London, UK

Volume

45

Issue

9

fYear

1998

Firstpage

1186

Lastpage

1191

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.

Keywords

electroencephalography; entropy; feature extraction; medical signal processing; spectral analysis; stochastic processes; Cheyne-Stokes respiration; anesthesia; motor-cortex investigation; physiological signals; signal amplitude; signal description; signal frequency; sleep; synthetic signals; traditional feature extraction methods; Anesthesia; Biomedical engineering; Biomedical measurements; Entropy; Feature extraction; Frequency measurement; Power system modeling; Predictive models; Signal analysis; Stochastic processes; Anesthesia; Cheyne-Stokes Respiration; Electroencephalography; Fourier Analysis; Humans; Monitoring, Physiologic; Motor Cortex; Nonlinear Dynamics; Signal Processing, Computer-Assisted; Sleep; Stochastic Processes;

fLanguage

English

Journal_Title

Biomedical Engineering, IEEE Transactions on

Publisher

ieee

ISSN

0018-9294

Type

jour

DOI

10.1109/10.709563

Filename

709563