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Seizure detection from EEG signals using Multivariate Empirical Mode Decomposition

Zahra, A and Kanwal, N and ur Rehman, N and Ehsan, S and McDonald-Maier, KD (2017) 'Seizure detection from EEG signals using Multivariate Empirical Mode Decomposition.' Computers in Biology and Medicine, 88. 132 - 141. ISSN 0010-4825

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Abstract

© 2017 Elsevier Ltd We present a data driven approach to classify ictal (epileptic seizure) and non-ictal EEG signals using the multivariate empirical mode decomposition (MEMD) algorithm. MEMD is a multivariate extension of empirical mode decomposition (EMD), which is an established method to perform the decomposition and time-frequency (T−F) analysis of non-stationary data sets. We select suitable feature sets based on the multiscale T−F representation of the EEG data via MEMD for the classification purposes. The classification is achieved using the artificial neural networks. The efficacy of the proposed method is verified on extensive publicly available EEG datasets.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 14 Jul 2017 13:44
Last Modified: 08 Jul 2018 01:00
URI: http://repository.essex.ac.uk/id/eprint/20082

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