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COMPUTER DETECTION APPROACHES FOR IDENTIFICATION OF PHASIC ELECTROMYOGRAPHIC (EMG) ACTIVITY DURING HUMAN SLEEP.
MedLine Citation:
PMID:  23047598     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
BACKGROUND: Examination of spontaneously occurring phasic muscle activity from the human polysomnogram may have considerable clinical importance for patient care, yet most attempts to quantify the detection of such activity have relied upon laborious and intensive visual analyses. We describe in this study innovative signal processing approaches to this issue. METHODS: We examined multiple features of surface electromyographic signals based on 16,200 individual 1-second intervals of low impedance sleep recordings. We validated which of those features most closely mirrored the careful judgments of trained human observers in making discriminations of the presence of short-lived (100-500 msec) phasic activity, and also examined which features provided maximal differences across 1-second intervals and which features were least susceptible to residual levels of amplifier noise. RESULTS: Our data suggested particularly promising and novel features (e.g., Non-linear energy, 95(th) percentile of Spectral Edge Frequency) for developing automated systems for quantifying muscle activity during human sleep. CONCLUSIONS: The EMG signals recorded from surface electrodes during sleep can be processed with techniques that reflect the visually based analyses of the human scorer but also offer potential for discerning far more subtle effects, Future studies will explore both the clinical utility of these techniques and their relative susceptibility to and/or independence from signal artifacts.
Authors:
Jacqueline A Fairley; George Georgoulas; Nishant A Mehta; Alexander G Gray; Donald L Bliwise
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-3-28
Journal Detail:
Title:  Biomedical signal processing and control     Volume:  7     ISSN:  1746-8094     ISO Abbreviation:  Biomed Signal Process Control     Publication Date:  2012 Nov 
Date Detail:
Created Date:  2012-10-10     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101317299     Medline TA:  Biomed Signal Process Control     Country:  -    
Other Details:
Languages:  ENG     Pagination:  606-615     Citation Subset:  -    
Affiliation:
Department of Neurology and Program in Sleep Medicine, Emory University School of Medicine, Atlanta, Georgia ; Department of Computing, Georgia Institute of Technology, Atlanta, Georgia.
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