Evaluation of Feature Selection Methods for Classification of Epileptic Seizure EEG Signals

Epilepsy is a disease that decreases the quality of life of patients; it is also among the most common neurological diseases. Several studies have approached the classification and prediction of seizures by using electroencephalographic data and machine learning techniques. A large diversity of feat...
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Autor*in:	Sergio E. Sánchez-Hernández [verfasserIn] Ricardo A. Salido-Ruiz [verfasserIn] Sulema Torres-Ramos [verfasserIn] Israel Román-Godínez [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	EEG epilepsy seizure detection machine learning features feature selection

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 22(2022), 8, p 3066
Übergeordnetes Werk:	volume:22 ; year:2022 ; number:8, p 3066

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s22083066

Katalog-ID:	DOAJ025688472

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520			\|a Epilepsy is a disease that decreases the quality of life of patients; it is also among the most common neurological diseases. Several studies have approached the classification and prediction of seizures by using electroencephalographic data and machine learning techniques. A large diversity of features has been extracted from electroencephalograms to perform classification tasks; therefore, it is important to use feature selection methods to select those that leverage pattern recognition. In this study, the performance of a set of feature selection methods was compared across different classification models; the classification task consisted of the detection of ictal activity from the CHB-MIT and Siena Scalp EEG databases. The comparison was implemented for different feature sets and the number of features. Furthermore, the similarity between selected feature subsets across classification models was evaluated. The best F1-score (0.90) was reported by the K-nearest neighbor along with the CHB-MIT dataset. Results showed that none of the feature selection methods clearly outperformed the rest of the methods, as the performance was notably affected by the classifier, dataset, and feature set. Two of the combinations (classifier/feature selection method) reporting the best results were K-nearest neighbor/support vector machine and random forest/embedded random forest.
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language	English
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authorswithroles_txt_mv	Sergio E. Sánchez-Hernández @@aut@@ Ricardo A. Salido-Ruiz @@aut@@ Sulema Torres-Ramos @@aut@@ Israel Román-Godínez @@aut@@
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callnumber-first	T - Technology
author	Sergio E. Sánchez-Hernández
spellingShingle	Sergio E. Sánchez-Hernández misc TP1-1185 misc EEG misc epilepsy misc seizure detection misc machine learning misc features misc feature selection misc Chemical technology Evaluation of Feature Selection Methods for Classification of Epileptic Seizure EEG Signals
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topic_title	TP1-1185 Evaluation of Feature Selection Methods for Classification of Epileptic Seizure EEG Signals EEG epilepsy seizure detection machine learning features feature selection
topic	misc TP1-1185 misc EEG misc epilepsy misc seizure detection misc machine learning misc features misc feature selection misc Chemical technology
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title	Evaluation of Feature Selection Methods for Classification of Epileptic Seizure EEG Signals
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title_sort	evaluation of feature selection methods for classification of epileptic seizure eeg signals
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title_auth	Evaluation of Feature Selection Methods for Classification of Epileptic Seizure EEG Signals
abstract	Epilepsy is a disease that decreases the quality of life of patients; it is also among the most common neurological diseases. Several studies have approached the classification and prediction of seizures by using electroencephalographic data and machine learning techniques. A large diversity of features has been extracted from electroencephalograms to perform classification tasks; therefore, it is important to use feature selection methods to select those that leverage pattern recognition. In this study, the performance of a set of feature selection methods was compared across different classification models; the classification task consisted of the detection of ictal activity from the CHB-MIT and Siena Scalp EEG databases. The comparison was implemented for different feature sets and the number of features. Furthermore, the similarity between selected feature subsets across classification models was evaluated. The best F1-score (0.90) was reported by the K-nearest neighbor along with the CHB-MIT dataset. Results showed that none of the feature selection methods clearly outperformed the rest of the methods, as the performance was notably affected by the classifier, dataset, and feature set. Two of the combinations (classifier/feature selection method) reporting the best results were K-nearest neighbor/support vector machine and random forest/embedded random forest.
abstractGer	Epilepsy is a disease that decreases the quality of life of patients; it is also among the most common neurological diseases. Several studies have approached the classification and prediction of seizures by using electroencephalographic data and machine learning techniques. A large diversity of features has been extracted from electroencephalograms to perform classification tasks; therefore, it is important to use feature selection methods to select those that leverage pattern recognition. In this study, the performance of a set of feature selection methods was compared across different classification models; the classification task consisted of the detection of ictal activity from the CHB-MIT and Siena Scalp EEG databases. The comparison was implemented for different feature sets and the number of features. Furthermore, the similarity between selected feature subsets across classification models was evaluated. The best F1-score (0.90) was reported by the K-nearest neighbor along with the CHB-MIT dataset. Results showed that none of the feature selection methods clearly outperformed the rest of the methods, as the performance was notably affected by the classifier, dataset, and feature set. Two of the combinations (classifier/feature selection method) reporting the best results were K-nearest neighbor/support vector machine and random forest/embedded random forest.
abstract_unstemmed	Epilepsy is a disease that decreases the quality of life of patients; it is also among the most common neurological diseases. Several studies have approached the classification and prediction of seizures by using electroencephalographic data and machine learning techniques. A large diversity of features has been extracted from electroencephalograms to perform classification tasks; therefore, it is important to use feature selection methods to select those that leverage pattern recognition. In this study, the performance of a set of feature selection methods was compared across different classification models; the classification task consisted of the detection of ictal activity from the CHB-MIT and Siena Scalp EEG databases. The comparison was implemented for different feature sets and the number of features. Furthermore, the similarity between selected feature subsets across classification models was evaluated. The best F1-score (0.90) was reported by the K-nearest neighbor along with the CHB-MIT dataset. Results showed that none of the feature selection methods clearly outperformed the rest of the methods, as the performance was notably affected by the classifier, dataset, and feature set. Two of the combinations (classifier/feature selection method) reporting the best results were K-nearest neighbor/support vector machine and random forest/embedded random forest.
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title_short	Evaluation of Feature Selection Methods for Classification of Epileptic Seizure EEG Signals
url	https://doi.org/10.3390/s22083066 https://doaj.org/article/75fc712a566b4e6a8336870ebf7fb40e https://www.mdpi.com/1424-8220/22/8/3066 https://doaj.org/toc/1424-8220
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Evaluation of Feature Selection Methods for Classification of Epileptic Seizure EEG Signals

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