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Wavelet analysis for detection of phasic electromyographic activity in sleep: of mother wavelet and dimensionality reduction
aDepartments of Neurology and Neurosurgery, Emory University School of Medicine, Atlanta, GA .
bDepartment of Informatics Engineering, Technological Educational Institution of Epirus.ORCID iD: 0000-0001-9701-4203
Departments of Neurology and Neurosurgery, Emory University School of Medicine, Atlanta, GA .
Department of Statistics and Actuarial Financial Mathematics, University of the Aegean, Karlovassi, Samos.
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2014 (English)In: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 48, no 1, 77-84 p.Article in journal (Refereed) Published
Abstract [en]

Phasic electromyographic (EMG) activity during sleep is characterized by brief muscle twitches (duration 100-500. ms, amplitude four times background activity). High rates of such activity may have clinical relevance. This paper presents wavelet (WT) analyses to detect phasic EMG, examining both Symlet and Daubechies approaches. Feature extraction included 1. s epoch processing with 24 WT-based features and dimensionality reduction involved comparing two techniques: principal component analysis and a feature/variable selection algorithm. Classification was conducted using a linear classifier. Valid automated detection was obtained in comparison to expert human judgment with high (>90%) classification performance for 11/12 datasets

Place, publisher, year, edition, pages
Elsevier, 2014. Vol. 48, no 1, 77-84 p.
National Category
Control Engineering
Research subject
Control Engineering
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URN: urn:nbn:se:ltu:diva-66717DOI: 10.1016/j.compbiomed.2013.12.011PubMedID: 24657906Scopus ID: 2-s2.0-84896539224OAI: oai:DiVA.org:ltu-66717DiVA: diva2:1159567
Available from: 2017-11-23 Created: 2017-11-23 Last updated: 2017-11-24Bibliographically approved

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