A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis.

Détails

ID Serval
serval:BIB_504EF199FB67
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis.
Périodique
Medical and Biological Engineering and Computing
Auteur(s)
De Lucia M., Fritschy J., Dayan P., Holder D.S.
ISSN
0140-0118[print], 0140-0118[linking]
Statut éditorial
Publié
Date de publication
2008
Volume
46
Numéro
3
Pages
263-272
Langue
anglais
Notes
Publication types: Evaluation Studies ; Journal Article
Publication Status: ppublish
Résumé
Diagnosis of several neurological disorders is based on the detection of typical pathological patterns in the electroencephalogram (EEG). This is a time-consuming task requiring significant training and experience. Automatic detection of these EEG patterns would greatly assist in quantitative analysis and interpretation. We present a method, which allows automatic detection of epileptiform events and discrimination of them from eye blinks, and is based on features derived using a novel application of independent component analysis. The algorithm was trained and cross validated using seven EEGs with epileptiform activity. For epileptiform events with compensation for eyeblinks, the sensitivity was 65 +/- 22% at a specificity of 86 +/- 7% (mean +/- SD). With feature extraction by PCA or classification of raw data, specificity reduced to 76 and 74%, respectively, for the same sensitivity. On exactly the same data, the commercially available software Reveal had a maximum sensitivity of 30% and concurrent specificity of 77%. Our algorithm performed well at detecting epileptiform events in this preliminary test and offers a flexible tool that is intended to be generalized to the simultaneous classification of many waveforms in the EEG.
Mots-clé
Algorithms, Artifacts, Blinking, Brain/physiopathology, Data Interpretation, Statistical, Electroencephalography/methods, Epilepsy/diagnosis, Humans, Principal Component Analysis, Signal Processing, Computer-Assisted
Pubmed
Web of science
Création de la notice
25/02/2011 12:17
Dernière modification de la notice
03/03/2018 17:08
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