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Wavelet analysis fo...
Wavelet analysis for detection of phasic electromyographic activity in sleep : of mother wavelet and dimensionality reduction
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- Fairley, Jacqueline A. (författare)
- aDepartments of Neurology and Neurosurgery, Emory University School of Medicine, Atlanta, GA
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- Georgoulas, Georgios (författare)
- bDepartment of Informatics Engineering, Technological Educational Institution of Epirus
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- Smart, Otis L. (författare)
- Departments of Neurology and Neurosurgery, Emory University School of Medicine, Atlanta, GA
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- Dimakopoulos, George (författare)
- Department of Statistics and Actuarial Financial Mathematics, University of the Aegean, Karlovassi, Samos
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- Karvelis, Petros (författare)
- Department of Informatics Engineering, Technological Educational Institution of Epirus
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- Stylios, Chrysostomos D. (författare)
- Department of Informatics Engineering, Technological Educational Institution of Epirus
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- Rye, David B. (författare)
- Departments of Neurology and Neurosurgery, Emory University School of Medicine, Atlanta, GA
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- Bliwise, Donald L. (författare)
- Departments of Neurology and Neurosurgery, Emory University School of Medicine, Atlanta, GA
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(creator_code:org_t)
- Elsevier, 2014
- 2014
- Engelska.
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Ingår i: Computers in Biology and Medicine. - : Elsevier. - 0010-4825 .- 1879-0534. ; 48:1, s. 77-84
- Relaterad länk:
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https://europepmc.or...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
Nyckelord
- Reglerteknik
- Control Engineering
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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