Simple Detection of Epilepsy From EEG Signal Using Local Binary Pattern Transition Histogram

This paper proposed a simple but highly accurate feature extraction method for epilepsy detection from electroencephalogram (EEG) signals. Based on the combination of Discrete Wavelet Transform (DWT) and the newly proposed features Local Binary Pattern Transition Histogram (LBPTH) and Local Binary P...

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Autores principales:	Muhammad Yazid, Fahmi Fahmi, Erwin Sutanto, Wervyan Shalannanda, Ruhush Shoalihin, Gwo-Jiun Horng, Aripriharta
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Biomedical disability and family support health eeg epilepsy bonn Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/3bd9e2edacd446a88124e798f98013ac
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spelling	oai:doaj.org-article:3bd9e2edacd446a88124e798f98013ac2021-11-18T00:09:23ZSimple Detection of Epilepsy From EEG Signal Using Local Binary Pattern Transition Histogram2169-353610.1109/ACCESS.2021.3126065https://doaj.org/article/3bd9e2edacd446a88124e798f98013ac2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9605664/https://doaj.org/toc/2169-3536This paper proposed a simple but highly accurate feature extraction method for epilepsy detection from electroencephalogram (EEG) signals. Based on the combination of Discrete Wavelet Transform (DWT) and the newly proposed features Local Binary Pattern Transition Histogram (LBPTH) and Local Binary Pattern Mean Absolute Deviation (LBPMAD), our proposed feature extraction method can efficiently extract features from EEG signals for machine learning classification of epilepsy, achieving high classification accuracy with a feature size of only 18 for each signal. Tested on the publicly available University of Bonn Epilepsy EEG Dataset using a signal length of 4097 data points (23.61 seconds), the proposed method achieved larger than 99.6% accuracy results for Support Vector Machine (SVM) and k-Nearest Neighbor (KNN) classification of ictal (set E) against any non-ictal (set A, B, C, or D) or combinations of non-ictal (set <inline-formula> <tex-math notation="LaTeX">$\text{A}+\text{B}$ </tex-math></inline-formula>, set <inline-formula> <tex-math notation="LaTeX">$\text{C}+\text{D}$ </tex-math></inline-formula>, or set <inline-formula> <tex-math notation="LaTeX">$\text{A}+\text{B}+\text{C}+\text{D}$ </tex-math></inline-formula>) EEG signals, which is among the best of currently published works. Our method can maintain high classification accuracy even with short input signals, achieving more than 99.1% SVM classification accuracy when input signal length is reduced to 512 data points (2.95 seconds). The high accuracy, small feature size, ability to work with short input signals and low computing requirements made the proposed method suitable for mobile, low power, and low-cost wearable medical devices.Muhammad YazidFahmi FahmiErwin SutantoWervyan ShalannandaRuhush ShoalihinGwo-Jiun Horng AriprihartaIEEEarticleBiomedicaldisability and family supporthealtheegepilepsybonnElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 150252-150267 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biomedical disability and family support health eeg epilepsy bonn Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Biomedical disability and family support health eeg epilepsy bonn Electrical engineering. Electronics. Nuclear engineering TK1-9971 Muhammad Yazid Fahmi Fahmi Erwin Sutanto Wervyan Shalannanda Ruhush Shoalihin Gwo-Jiun Horng Aripriharta Simple Detection of Epilepsy From EEG Signal Using Local Binary Pattern Transition Histogram
description	This paper proposed a simple but highly accurate feature extraction method for epilepsy detection from electroencephalogram (EEG) signals. Based on the combination of Discrete Wavelet Transform (DWT) and the newly proposed features Local Binary Pattern Transition Histogram (LBPTH) and Local Binary Pattern Mean Absolute Deviation (LBPMAD), our proposed feature extraction method can efficiently extract features from EEG signals for machine learning classification of epilepsy, achieving high classification accuracy with a feature size of only 18 for each signal. Tested on the publicly available University of Bonn Epilepsy EEG Dataset using a signal length of 4097 data points (23.61 seconds), the proposed method achieved larger than 99.6% accuracy results for Support Vector Machine (SVM) and k-Nearest Neighbor (KNN) classification of ictal (set E) against any non-ictal (set A, B, C, or D) or combinations of non-ictal (set <inline-formula> <tex-math notation="LaTeX">$\text{A}+\text{B}$ </tex-math></inline-formula>, set <inline-formula> <tex-math notation="LaTeX">$\text{C}+\text{D}$ </tex-math></inline-formula>, or set <inline-formula> <tex-math notation="LaTeX">$\text{A}+\text{B}+\text{C}+\text{D}$ </tex-math></inline-formula>) EEG signals, which is among the best of currently published works. Our method can maintain high classification accuracy even with short input signals, achieving more than 99.1% SVM classification accuracy when input signal length is reduced to 512 data points (2.95 seconds). The high accuracy, small feature size, ability to work with short input signals and low computing requirements made the proposed method suitable for mobile, low power, and low-cost wearable medical devices.
format	article
author	Muhammad Yazid Fahmi Fahmi Erwin Sutanto Wervyan Shalannanda Ruhush Shoalihin Gwo-Jiun Horng Aripriharta
author_facet	Muhammad Yazid Fahmi Fahmi Erwin Sutanto Wervyan Shalannanda Ruhush Shoalihin Gwo-Jiun Horng Aripriharta
author_sort	Muhammad Yazid
title	Simple Detection of Epilepsy From EEG Signal Using Local Binary Pattern Transition Histogram
title_short	Simple Detection of Epilepsy From EEG Signal Using Local Binary Pattern Transition Histogram
title_full	Simple Detection of Epilepsy From EEG Signal Using Local Binary Pattern Transition Histogram
title_fullStr	Simple Detection of Epilepsy From EEG Signal Using Local Binary Pattern Transition Histogram
title_full_unstemmed	Simple Detection of Epilepsy From EEG Signal Using Local Binary Pattern Transition Histogram
title_sort	simple detection of epilepsy from eeg signal using local binary pattern transition histogram
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/3bd9e2edacd446a88124e798f98013ac
work_keys_str_mv	AT muhammadyazid simpledetectionofepilepsyfromeegsignalusinglocalbinarypatterntransitionhistogram AT fahmifahmi simpledetectionofepilepsyfromeegsignalusinglocalbinarypatterntransitionhistogram AT erwinsutanto simpledetectionofepilepsyfromeegsignalusinglocalbinarypatterntransitionhistogram AT wervyanshalannanda simpledetectionofepilepsyfromeegsignalusinglocalbinarypatterntransitionhistogram AT ruhushshoalihin simpledetectionofepilepsyfromeegsignalusinglocalbinarypatterntransitionhistogram AT gwojiunhorng simpledetectionofepilepsyfromeegsignalusinglocalbinarypatterntransitionhistogram AT aripriharta simpledetectionofepilepsyfromeegsignalusinglocalbinarypatterntransitionhistogram
_version_	1718425218681143296

Simple Detection of Epilepsy From EEG Signal Using Local Binary Pattern Transition Histogram

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