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

De Lucia, M; Fritschy, J; Dayan, P; Holder, DS; (2008) A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis. MED BIOL ENG COMPUT , 46 (3) 263 - 272. 10.1007/s11517-007-0289-4.

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Abstract

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.

Type:Article
Title:A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis
DOI:10.1007/s11517-007-0289-4
Keywords:electroencephalogram, independent component analysis, automatic classification, epileptiform events, eye-blinks artefacts, SYSTEMATIC SOURCE ESTIMATION, FUNCTIONAL MRI DATA, SPIKE DETECTION, BLIND SEPARATION, NEURAL-NETWORK, EEG, RECOGNITION, ALGORITHMS, IDENTIFICATION, LOCALIZATION
UCL classification:UCL > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neuroscience Unit
UCL > School of BEAMS > Faculty of Engineering Science > Medical Physics and Bioengineering

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