Zahra, Asmat and Kanwal, Nadia and ur Rehman, Naveed and Ehsan, Shoaib and McDonald-Maier, Klaus D (2017) 'Seizure detection from EEG signals using Multivariate Empirical Mode Decomposition.' Computers in Biology and Medicine, 88. pp. 132-141. ISSN 0010-4825
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Abstract
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 |
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Uncontrolled Keywords: | EEG signals; Epilepsy; MEMD; Time-frequency algorithm |
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 Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Elements |
Depositing User: | Elements |
Date Deposited: | 14 Jul 2017 13:44 |
Last Modified: | 15 Jan 2022 00:56 |
URI: | http://repository.essex.ac.uk/id/eprint/20082 |
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