Classification of 5-S Epileptic EEG Recordings Using Distribution Entropy and Sample Entropy

Classification of 5-S Epileptic EEG Recordings Using Distribution Entropy and Sample Entropy

Epilepsy is an electrophysiological disorder of the brain, the hallmark of which is recurrent and unprovoked seizures. Electroencephalogram (EEG) measures electrical activity of the brain that is commonly applied as a non-invasive technique for seizure detection. Although a vast number of publicatio...

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Main Authors: Li, Peng, Karmakar, Chandan, Yan, Chang, Palaniswami, Marimuthu, Liu, Changchun
Format: Online
Language:English
Published: Frontiers Media S.A. 2016
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4830849/
id pubmed-4830849
recordtype oai_dc
spelling pubmed-48308492016-05-04 Classification of 5-S Epileptic EEG Recordings Using Distribution Entropy and Sample Entropy Li, Peng Karmakar, Chandan Yan, Chang Palaniswami, Marimuthu Liu, Changchun Physiology Epilepsy is an electrophysiological disorder of the brain, the hallmark of which is recurrent and unprovoked seizures. Electroencephalogram (EEG) measures electrical activity of the brain that is commonly applied as a non-invasive technique for seizure detection. Although a vast number of publications have been published on intelligent algorithms to classify interictal and ictal EEG, it remains an open question whether they can be detected using short-length EEG recordings. In this study, we proposed three protocols to select 5 s EEG segment for classifying interictal and ictal EEG from normal. We used the publicly-accessible Bonn database, which consists of normal, interical, and ictal EEG signals with a length of 4097 sampling points (23.6 s) per record. In this study, we selected three segments of 868 points (5 s) length from each recordings and evaluated results for each of them separately. The well-studied irregularity measure—sample entropy (SampEn)—and a more recently proposed complexity measure—distribution entropy (DistEn)—were used as classification features. A total of 20 combinations of input parameters m and τ for the calculation of SampEn and DistEn were selected for compatibility. Results showed that SampEn was undefined for half of the used combinations of input parameters and indicated a large intra-class variance. Moreover, DistEn performed robustly for short-length EEG data indicating relative independence from input parameters and small intra-class fluctuations. In addition, it showed acceptable performance for all three classification problems (interictal EEG from normal, ictal EEG from normal, and ictal EEG from interictal) compared to SampEn, which showed better results only for distinguishing normal EEG from interictal and ictal. Both SampEn and DistEn showed good reproducibility and consistency, as evidenced by the independence of results on analysing protocol. Frontiers Media S.A. 2016-04-14 /pmc/articles/PMC4830849/ /pubmed/27148074 http://dx.doi.org/10.3389/fphys.2016.00136 Text en Copyright © 2016 Li, Karmakar, Yan, Palaniswami and Liu. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Li, Peng
Karmakar, Chandan
Yan, Chang
Palaniswami, Marimuthu
Liu, Changchun
spellingShingle Li, Peng
Karmakar, Chandan
Yan, Chang
Palaniswami, Marimuthu
Liu, Changchun
Classification of 5-S Epileptic EEG Recordings Using Distribution Entropy and Sample Entropy
author_facet Li, Peng
Karmakar, Chandan
Yan, Chang
Palaniswami, Marimuthu
Liu, Changchun
author_sort Li, Peng
title Classification of 5-S Epileptic EEG Recordings Using Distribution Entropy and Sample Entropy
title_short Classification of 5-S Epileptic EEG Recordings Using Distribution Entropy and Sample Entropy
title_full Classification of 5-S Epileptic EEG Recordings Using Distribution Entropy and Sample Entropy
title_fullStr Classification of 5-S Epileptic EEG Recordings Using Distribution Entropy and Sample Entropy
title_full_unstemmed Classification of 5-S Epileptic EEG Recordings Using Distribution Entropy and Sample Entropy
title_sort classification of 5-s epileptic eeg recordings using distribution entropy and sample entropy
description Epilepsy is an electrophysiological disorder of the brain, the hallmark of which is recurrent and unprovoked seizures. Electroencephalogram (EEG) measures electrical activity of the brain that is commonly applied as a non-invasive technique for seizure detection. Although a vast number of publications have been published on intelligent algorithms to classify interictal and ictal EEG, it remains an open question whether they can be detected using short-length EEG recordings. In this study, we proposed three protocols to select 5 s EEG segment for classifying interictal and ictal EEG from normal. We used the publicly-accessible Bonn database, which consists of normal, interical, and ictal EEG signals with a length of 4097 sampling points (23.6 s) per record. In this study, we selected three segments of 868 points (5 s) length from each recordings and evaluated results for each of them separately. The well-studied irregularity measure—sample entropy (SampEn)—and a more recently proposed complexity measure—distribution entropy (DistEn)—were used as classification features. A total of 20 combinations of input parameters m and τ for the calculation of SampEn and DistEn were selected for compatibility. Results showed that SampEn was undefined for half of the used combinations of input parameters and indicated a large intra-class variance. Moreover, DistEn performed robustly for short-length EEG data indicating relative independence from input parameters and small intra-class fluctuations. In addition, it showed acceptable performance for all three classification problems (interictal EEG from normal, ictal EEG from normal, and ictal EEG from interictal) compared to SampEn, which showed better results only for distinguishing normal EEG from interictal and ictal. Both SampEn and DistEn showed good reproducibility and consistency, as evidenced by the independence of results on analysing protocol.
publisher Frontiers Media S.A.
publishDate 2016
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4830849/
_version_ 1613565944940986368

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