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Comparison of Three Methods for Classifying Burst and Suppression in the EEG of Post Asphyctic Newborns

Johan Löfhede (Institutionen för signaler och system, Medicinska signaler och system) ; Nils Löfgren ; Magnus Thordstein ; Anders Flisberg ; Ingemar Kjellmer ; Kaj Lindecrantz (Institutionen för signaler och system, Medicinska signaler och system)
Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE (1557-170X ). p. 5136 - 5139. (2007)
[Konferensbidrag, refereegranskat]

Fisher's linear discriminant, a feed-forward neural network (NN) and a support vector machine (SVM) are compared with respect to their ability to distinguish bursts from suppression in burst-suppression electroencephalogram (EEG) signals using five features inherent in the EEG as input. The study is based on EEG signals from six full term infants who have suffered from perinatal asphyxia, and the methods have been trained with reference data classified by an experienced electroencephalographer. The results are summarized as area under the curve (AUC) values derived from receiver operating characteristic (ROC) curves for the three methods, and show that the SVM is slightly better than the others, at the cost of a higher computational complexity.

Nyckelord: Biomedical Signal Processing, Classification, Algorithms, Artificial Intelligence, Asphyxia Neonatorum, complications, diagnosis, Brain Damage, Chronic, diagnosis, etiology, Diagnosis, Computer-Assisted, methods, Electroencephalography, methods, Humans, Infant, Newborn, Male, Pattern Recognition, Automated, methods, Reproducibility of Results, Sensitivity and Specificity

Denna post skapades 2007-11-20. Senast ändrad 2010-06-21.
CPL Pubid: 61948


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Institutioner (Chalmers)

Institutionen för signaler och system, Medicinska signaler och system
Institutionen för neurovetenskap och fysiologi, sektionen för klinisk neurovetenskap och rehabilitering (2006-2016)
Institutionen för kliniska vetenskaper, sektionen för kvinnors och barns hälsa (GU)


Medicinsk teknik
Klinisk neurofysiologi

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