Stochastic complexity measures for physiological signal analysis

被引：192

作者：

Rezek, IA ^{[1
]}

Roberts, SJ ^{[1
]}

机构：

[1] Univ London Imperial Coll Sci Technol & Med, Dept Elect & Elect Engn, London SW7 2BT, England

来源：

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING | 1998年 / 45卷 / 09期

关键词：

approximate entropy; autoregressive (AR) model order; spectral entropy (SE); state-space embedding; stochastic complexity;

D O I：

10.1109/10.709563

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

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.

引用

页码：1186 / 1191

页数：6

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