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Comparing a Supervised and an Unsupervised Classification Method for Burst Detection in Neonatal EEG
Högskolan i Borås, Institutionen Ingenjörshögskolan.
Vise andre og tillknytning
2008 (engelsk)Inngår i: Proceedings of Engineering in Medicine and Biology Society, EMBS 2008. 30th Annual International Conference of the IEEE, 20-24 August, 2008, IEEE , 2008, s. 3836-3839Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
IEEE , 2008. s. 3836-3839
Emneord [en]
EEG, classification, burst, suppression, neonatal, neonatal care, Medicinteknik
HSV kategori
Identifikatorer
URN: urn:nbn:se:hb:diva-5990Lokal ID: 2320/4180ISBN: 978-1-4244-1814-5 (tryckt)OAI: oai:DiVA.org:hb-5990DiVA, id: diva2:886674
Konferanse
Engineering in Medicine and Biology Society, EMBS 2008. 30th Annual International Conference of the IEEE, 20-24 August, 2008
Tilgjengelig fra: 2015-12-22 Laget: 2015-12-22 Sist oppdatert: 2018-01-10bibliografisk kontrollert

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Löfhede, JohanLindecrantz, Kaj

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