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Comparing a Supervised and an Unsupervised Classification Method for Burst Detection in Neonatal EEG
University of Borås, School of Engineering.
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2008 (English)In: Proceedings of Engineering in Medicine and Biology Society, EMBS 2008. 30th Annual International Conference of the IEEE, 20-24 August, 2008, IEEE , 2008, 3836-3839 p.Conference paper, Published paper (Refereed)
Abstract [en]

Hidden Markov Models (HMM) and Support Vector Machines (SVM) using unsupervised and supervised training, respectively, were compared with respect to their ability to correctly classify burst and suppression in neonatal EEG. Each classifier was fed five feature signals extracted from EEG signals from six full term infants who had suffered from perinatal asphyxia. Visual inspection of the EEG by an experienced electroencephalographer was used as the gold standard when training the SVM, and for evaluating the performance of both methods. The results are presented as receiver operating characteristic (ROC) curves and quantified by the area under the curve (AUC). Our study show that the SVM and the HMM exhibit similar performance, despite their fundamental differences.

Place, publisher, year, edition, pages
IEEE , 2008. 3836-3839 p.
Keyword [en]
EEG, classification, burst, suppression, neonatal, neonatal care, Medicinteknik
National Category
Physiology Signal Processing Biomedical Laboratory Science/Technology
Identifiers
URN: urn:nbn:se:hb:diva-5990Local ID: 2320/4180ISBN: 978-1-4244-1814-5 (print)OAI: oai:DiVA.org:hb-5990DiVA: diva2:886674
Conference
Engineering in Medicine and Biology Society, EMBS 2008. 30th Annual International Conference of the IEEE, 20-24 August, 2008
Available from: 2015-12-22 Created: 2015-12-22 Last updated: 2018-01-10Bibliographically approved

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Löfhede, JohanLindecrantz, Kaj

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf