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Semi-automated annotation of phasic electromyographic activity
Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, Technological Educational Institute of Epirus.
Emory University, School of Medicine, Department of Neurology.
Laboratory of Knowledge and Intelligent Computing, Technological Educational Institute of Epirus, Department of Computer Engineering, Arta.ORCID iD: 0000-0001-9701-4203
Laboratory of Knowledge and Intelligent Computing, Technological Educational Institute of Epirus, Department of Computer Engineering, Arta.
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2014 (English)Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer, 2014. p. 532-543
Series
Lecture Notes in Computer Science, ISSN 0302-9743
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-67888DOI: 10.1007/978-3-319-07064-3_46Scopus ID: 2-s2.0-84900538273ISBN: 9783319070636 (print)OAI: oai:DiVA.org:ltu-67888DiVA, id: diva2:1188695
Conference
8th Hellenic Conference on Artificial Intelligence: Methods and Applications, SETN 2014, Ioannina, Greece, 15-17 May 2014
Available from: 2018-03-08 Created: 2018-03-08 Last updated: 2018-03-08Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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