$Automatic epilepsy detection using fractal dimensions segmentation and GP–SVM classification$
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Automatic epilepsy detection using fractal dimensions segmentation and GP–SVM classification

Jakub Jirka,1 Michal Prauzek,1 Ondrej Krejcar,2 Kamil Kuca2,3 1Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB – Technical University of Ostrava, Ostrava Poruba, Czech Republic; 2Center for Basic and Applied Research, Facul...

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Autores principales:	Jirka J, Prauzek M, Krejcar O, Kuca K
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2018
Materias:	genetic programming adaptive segmentation SVM fractal dimensions EEG Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429
Acceso en línea:	https://doaj.org/article/e9ba33d875be4d60b1f1d27abad40d22
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spelling	oai:doaj.org-article:e9ba33d875be4d60b1f1d27abad40d222021-12-02T04:49:27ZAutomatic epilepsy detection using fractal dimensions segmentation and GP–SVM classification1178-2021https://doaj.org/article/e9ba33d875be4d60b1f1d27abad40d222018-09-01T00:00:00Zhttps://www.dovepress.com/automatic-epilepsy-detection-using-fractal-dimensions-segmentation-and-peer-reviewed-article-NDThttps://doaj.org/toc/1178-2021Jakub Jirka,1 Michal Prauzek,1 Ondrej Krejcar,2 Kamil Kuca2,3 1Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB – Technical University of Ostrava, Ostrava Poruba, Czech Republic; 2Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czech Republic; 3Biomedical Research Center, University Hospital Hradec Kralove, Hradec Kralove, Czech Republic Objective: The most important part of signal processing for classification is feature extraction as a mapping from original input electroencephalographic (EEG) data space to new features space with the biggest class separability value. Features are not only the most important, but also the most difficult task from the classification process as they define input data and classification quality. An ideal set of features would make the classification problem trivial. This article presents novel methods of feature extraction processing and automatic epilepsy seizure classification combining machine learning methods with genetic evolution algorithms.Methods: Classification is performed on EEG data that represent electric brain activity. At first, the signal is preprocessed with digital filtration and adaptive segmentation using fractal dimensions as the only segmentation measure. In the next step, a novel method using genetic programming (GP) combined with support vector machine (SVM) confusion matrix as fitness function weight is used to extract feature vectors compressed into lower dimension space and classify the final result into ictal or interictal epochs.Results: The final application of GP–SVM method improves the discriminatory performance of a classifier by reducing feature dimensionality at the same time. Members of the GP tree structure represent the features themselves and their number is automatically decided by the compression function introduced in this paper. This novel method improves the overall performance of the SVM classification by dramatically reducing the size of input feature vector.Conclusion: According to results, the accuracy of this algorithm is very high and comparable, or even superior to other automatic detection algorithms. In combination with the great efficiency, this algorithm can be used in real-time epilepsy detection applications. From the results of the algorithm’s classification, we can observe high sensitivity, specificity results, except for the Generalized Tonic Clonic Seizure (GTCS). As the next step, the optimization of the compression stage and final SVM evaluation stage is in place. More data need to be obtained on GTCS to improve the overall classification score for GTCS. Keywords: genetic programming, adaptive segmentation, SVM, fractal dimensions, EEGJirka JPrauzek MKrejcar OKuca KDove Medical Pressarticlegenetic programmingadaptive segmentationSVMfractal dimensionsEEGNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571Neurology. Diseases of the nervous systemRC346-429ENNeuropsychiatric Disease and Treatment, Vol Volume 14, Pp 2439-2449 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	genetic programming adaptive segmentation SVM fractal dimensions EEG Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429
spellingShingle	genetic programming adaptive segmentation SVM fractal dimensions EEG Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429 Jirka J Prauzek M Krejcar O Kuca K Automatic epilepsy detection using fractal dimensions segmentation and GP–SVM classification
description	Jakub Jirka,1 Michal Prauzek,1 Ondrej Krejcar,2 Kamil Kuca2,3 1Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB – Technical University of Ostrava, Ostrava Poruba, Czech Republic; 2Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czech Republic; 3Biomedical Research Center, University Hospital Hradec Kralove, Hradec Kralove, Czech Republic Objective: The most important part of signal processing for classification is feature extraction as a mapping from original input electroencephalographic (EEG) data space to new features space with the biggest class separability value. Features are not only the most important, but also the most difficult task from the classification process as they define input data and classification quality. An ideal set of features would make the classification problem trivial. This article presents novel methods of feature extraction processing and automatic epilepsy seizure classification combining machine learning methods with genetic evolution algorithms.Methods: Classification is performed on EEG data that represent electric brain activity. At first, the signal is preprocessed with digital filtration and adaptive segmentation using fractal dimensions as the only segmentation measure. In the next step, a novel method using genetic programming (GP) combined with support vector machine (SVM) confusion matrix as fitness function weight is used to extract feature vectors compressed into lower dimension space and classify the final result into ictal or interictal epochs.Results: The final application of GP–SVM method improves the discriminatory performance of a classifier by reducing feature dimensionality at the same time. Members of the GP tree structure represent the features themselves and their number is automatically decided by the compression function introduced in this paper. This novel method improves the overall performance of the SVM classification by dramatically reducing the size of input feature vector.Conclusion: According to results, the accuracy of this algorithm is very high and comparable, or even superior to other automatic detection algorithms. In combination with the great efficiency, this algorithm can be used in real-time epilepsy detection applications. From the results of the algorithm’s classification, we can observe high sensitivity, specificity results, except for the Generalized Tonic Clonic Seizure (GTCS). As the next step, the optimization of the compression stage and final SVM evaluation stage is in place. More data need to be obtained on GTCS to improve the overall classification score for GTCS. Keywords: genetic programming, adaptive segmentation, SVM, fractal dimensions, EEG
format	article
author	Jirka J Prauzek M Krejcar O Kuca K
author_facet	Jirka J Prauzek M Krejcar O Kuca K
author_sort	Jirka J
title	Automatic epilepsy detection using fractal dimensions segmentation and GP–SVM classification
title_short	Automatic epilepsy detection using fractal dimensions segmentation and GP–SVM classification
title_full	Automatic epilepsy detection using fractal dimensions segmentation and GP–SVM classification
title_fullStr	Automatic epilepsy detection using fractal dimensions segmentation and GP–SVM classification
title_full_unstemmed	Automatic epilepsy detection using fractal dimensions segmentation and GP–SVM classification
title_sort	automatic epilepsy detection using fractal dimensions segmentation and gp–svm classification
publisher	Dove Medical Press
publishDate	2018
url	https://doaj.org/article/e9ba33d875be4d60b1f1d27abad40d22
work_keys_str_mv	AT jirkaj automaticepilepsydetectionusingfractaldimensionssegmentationandgpndashsvmclassification AT prauzekm automaticepilepsydetectionusingfractaldimensionssegmentationandgpndashsvmclassification AT krejcaro automaticepilepsydetectionusingfractaldimensionssegmentationandgpndashsvmclassification AT kucak automaticepilepsydetectionusingfractaldimensionssegmentationandgpndashsvmclassification
_version_	1718401043011731456

Automatic epilepsy detection using fractal dimensions segmentation and GP&ndash;SVM classification

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