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Seizure detection from EEG signals using Multivariate Empirical Mode Decomposition

Seizure detection from EEG signals using Multivariate Empirical Mode Decomposition
Seizure detection from EEG signals using Multivariate Empirical Mode Decomposition
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.
EEG signals, Epilepsy, MEMD, Time-frequency algorithm
0010-4825
132-141
Zahra, Asmat
87a37635-2a53-4f18-ade1-cf80c3836902
Kanwal, Nadia
636e56ff-8bec-4857-815d-1d458ee81d69
Rehman, Naveed ur
8cd2ee50-73fb-4df1-9bb5-b278b911b70f
Ehsan, Shoaib
ae8922f0-dbe0-4b22-8474-98e84d852de7
McDonald-Maier, Klaus D.
d35c2e77-744a-4318-9d9d-726459e64db9
Zahra, Asmat
87a37635-2a53-4f18-ade1-cf80c3836902
Kanwal, Nadia
636e56ff-8bec-4857-815d-1d458ee81d69
Rehman, Naveed ur
8cd2ee50-73fb-4df1-9bb5-b278b911b70f
Ehsan, Shoaib
ae8922f0-dbe0-4b22-8474-98e84d852de7
McDonald-Maier, Klaus D.
d35c2e77-744a-4318-9d9d-726459e64db9

Zahra, Asmat, Kanwal, Nadia, Rehman, Naveed ur, Ehsan, Shoaib and McDonald-Maier, Klaus D. (2017) Seizure detection from EEG signals using Multivariate Empirical Mode Decomposition. Computers in Biology and Medicine, 88, 132-141. (doi:10.1016/j.compbiomed.2017.07.010).

Record type: Article

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.

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Accepted/In Press date: 6 July 2017
Published date: 15 July 2017
Related URLs:
Keywords: EEG signals, Epilepsy, MEMD, Time-frequency algorithm
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Identifiers

Local EPrints ID: 478949
URI: http://eprints.soton.ac.uk/id/eprint/478949
DOI: doi:10.1016/j.compbiomed.2017.07.010
ISSN: 0010-4825
PURE UUID: 13b3409f-2e04-463f-92f6-49026732cdbc
ORCID for Shoaib Ehsan: ORCID iD orcid.org/0000-0001-9631-1898

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Date deposited: 14 Jul 2023 17:08
Last modified: 17 Mar 2024 04:16

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Contributors

Author: Asmat Zahra
Author: Nadia Kanwal
Author: Naveed ur Rehman
Author: Shoaib Ehsan ORCID iD
Author: Klaus D. McDonald-Maier

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