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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.

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Publisher copy:
10.1109/10.709563

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
Publisher:
IEEE
Journal:
IEEE transactions on bio-medical engineering
Volume:
45
Issue:
9
Pages:
1186-1191
Publication date:
1998-09-01
DOI:
EISSN:
1558-2531
ISSN:
0018-9294
Source identifiers:
318883
Language:
English
Keywords:
Pubs id:
pubs:318883
UUID:
uuid:23557686-b58f-422d-b05f-aad5cf3e74b0
Local pid:
pubs:318883
Deposit date:
2012-12-19

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