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Semi-automated anno...
Semi-automated annotation of phasic electromyographic activity
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- Karvelis, Petros (författare)
- Department of Computer Engineering, Laboratory of Knowledge and Intelligent Computing, Technological Educational Institute of Epirus, Arta, Greece
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- Fairley, Jacqueline (författare)
- School of Medicine Department of Neurology, Emory University, Atlanta, USA
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- Georgoulas, Georgios (författare)
- Department of Computer Engineering, Laboratory of Knowledge and Intelligent Computing, Technological Educational Institute of Epirus, Arta, Greece
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- Stylios, Chrysostomos D. (författare)
- Department of Computer Engineering, Laboratory of Knowledge and Intelligent Computing, Technological Educational Institute of Epirus, Arta, Greece
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- Rye, David B. (författare)
- School of Medicine Department of Neurology, Emory University, Atlanta, USA
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- Bliwise, Donald L. (författare)
- School of Medicine Department of Neurology, Emory University, Atlanta, USA
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(creator_code:org_t)
- Cham : Springer, 2014
- 2014
- Engelska.
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Serie: Lecture Notes in Computer Science, 0302-9743
- Relaterad länk:
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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
- Recent research on manual/visual identification of phasic muscle activity utilizing the phasic electromyographic metric (PEM) in human polysomnograms (PSGs) cites evidence that PEM is a potentially reliable quantitative metric to assist in distinguishing between neurodegenerative disorder populations and age-matched controls. However, visual scoring of PEM activity is time consuming-preventing feasible implementation within a clinical setting. Therefore, here we propose an assistive/semi-supervised software platform designed and tested to automatically identify and characterize PEM events in a clinical setting that will be extremely useful for sleep physicians and technicians. The proposed semi-automated approach consists of four levels: A) Signal Parsing, B) Calculation of quantitative features on candidate PEM events, C) Classification of PEM and non-PEM events using a linear classifier, and D) Post-processing/Expert feedback to correct/remove automated misclassifications of PEM and Non-PEM events. Performance evaluation of the designed software compared to manual labeling is provided for electromyographic (EMG) activity from the PSG of a control subject. Results indicate that the semi-automated approach provides an excellent benchmark that could be embedded into a clinical decision support system to detect PEM events that would be used in neurological disorder identification and treatment.
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)
- kon (ämneskategori)