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A discriminative approach to automatic seizure detection in multichannel EEG signals

James, David, Xie, Xianghua and Eslambolchilar, Parisa ORCID: 2014. A discriminative approach to automatic seizure detection in multichannel EEG signals. Presented at: 22nd European Signal Processing Conference, EUSIPCO 2014, Lisbon, Portugal, September 1-5, 2014. Proceedings of the 22nd European Signal Processing Conference (EUSIPCO), 2014. IEEE, pp. 2010-2014.

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The aim of this paper is to introduce the application of Random Forests to the automated analysis of epileptic EEG data. Feature extraction is performed using a discrete wavelet transform to give time-frequency representations, from which statistical features based on the wavelet decompositions are formed and used for training and classification. We show that Random Forests can be used for the classification of ictal, inter-ictal and healthy EEG with a high level of accuracy, with 99% sensitivity and 93.5% specificity for classifying ictal and inter-ictal EEG, 90.6% sensitivity and 95.7% specificity for the windowed data and 93.9% sensitivity for seizure onset classification.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Uncontrolled Keywords: automatic seizure detection; seizure onset classification; inter-ictal EEG; wavelet decompositions; statistical features; time frequency representations; discrete wavelet transform; feature extraction; epileptic EEG data; automated analysis; random forests
Publisher: IEEE
ISSN: 2076-1465
Last Modified: 21 Oct 2022 07:10

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