Enhanced Detection of Epileptic Seizure Using EEG Signals in Combination With Machine Learning Classifiers
Electroencephalogram (EEG) is one of the most powerful tools that offer valuable information related to different abnormalities in the human brain. One of these abnormalities is the epileptic seizure. A framework is proposed for detecting epileptic seizures from EEG signals recorded from normal and...
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doaj-ad9745b95aa14d5b841f36adfc5ebd1a2021-03-30T01:15:23ZengIEEEIEEE Access2169-35362020-01-018240462405510.1109/ACCESS.2020.29700128972376Enhanced Detection of Epileptic Seizure Using EEG Signals in Combination With Machine Learning ClassifiersWail Mardini0https://orcid.org/0000-0002-4840-2290Muneer Masadeh Bani Yassein1https://orcid.org/0000-0001-5030-6196Rana Al-Rawashdeh2https://orcid.org/0000-0003-0051-2340Shadi Aljawarneh3https://orcid.org/0000-0001-5748-4921Yaser Khamayseh4https://orcid.org/0000-0002-8334-3281Omar Meqdadi5https://orcid.org/0000-0001-9504-4230Computer Science Department, Jordan University of Science and Technology, Irbid, JordanComputer Science Department, Jordan University of Science and Technology, Irbid, JordanComputer Science Department, Jordan University of Science and Technology, Irbid, JordanSoftware Engineering Department, Jordan University of Science and Technology, Irbid, JordanComputer Science Department, Jordan University of Science and Technology, Irbid, JordanSoftware Engineering Department, Jordan University of Science and Technology, Irbid, JordanElectroencephalogram (EEG) is one of the most powerful tools that offer valuable information related to different abnormalities in the human brain. One of these abnormalities is the epileptic seizure. A framework is proposed for detecting epileptic seizures from EEG signals recorded from normal and epileptic patients. The suggested approach is designed to classify the abnormal signal from the normal one automatically. This work aims to improve the accuracy of epileptic seizure detection and reduce computational costs. To address this, the proposed framework uses the 54-DWT mother wavelets analysis of EEG signals using the Genetic algorithm (GA) in combination with other four machine learning (ML) classifiers: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Naive Bayes (NB). The performance of 14 different combinations of two-class epilepsy detection is investigated using these four ML classifiers. The experimental results show that the four classifiers produce comparable results for the derived statistical features from the 54-DWT mother wavelets; however, the ANN classifier achieved the best accuracy in most datasets combinations, and it outperformed the other examined classifiers.https://ieeexplore.ieee.org/document/8972376/Electroencephalogram (EEG)discrete wavelet transform (DWT)epilepsyartificial neural networkk-nearest neighbor (k-NN)support vector machine (SVM) |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wail Mardini Muneer Masadeh Bani Yassein Rana Al-Rawashdeh Shadi Aljawarneh Yaser Khamayseh Omar Meqdadi |
spellingShingle |
Wail Mardini Muneer Masadeh Bani Yassein Rana Al-Rawashdeh Shadi Aljawarneh Yaser Khamayseh Omar Meqdadi Enhanced Detection of Epileptic Seizure Using EEG Signals in Combination With Machine Learning Classifiers IEEE Access Electroencephalogram (EEG) discrete wavelet transform (DWT) epilepsy artificial neural network k-nearest neighbor (k-NN) support vector machine (SVM) |
author_facet |
Wail Mardini Muneer Masadeh Bani Yassein Rana Al-Rawashdeh Shadi Aljawarneh Yaser Khamayseh Omar Meqdadi |
author_sort |
Wail Mardini |
title |
Enhanced Detection of Epileptic Seizure Using EEG Signals in Combination With Machine Learning Classifiers |
title_short |
Enhanced Detection of Epileptic Seizure Using EEG Signals in Combination With Machine Learning Classifiers |
title_full |
Enhanced Detection of Epileptic Seizure Using EEG Signals in Combination With Machine Learning Classifiers |
title_fullStr |
Enhanced Detection of Epileptic Seizure Using EEG Signals in Combination With Machine Learning Classifiers |
title_full_unstemmed |
Enhanced Detection of Epileptic Seizure Using EEG Signals in Combination With Machine Learning Classifiers |
title_sort |
enhanced detection of epileptic seizure using eeg signals in combination with machine learning classifiers |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Electroencephalogram (EEG) is one of the most powerful tools that offer valuable information related to different abnormalities in the human brain. One of these abnormalities is the epileptic seizure. A framework is proposed for detecting epileptic seizures from EEG signals recorded from normal and epileptic patients. The suggested approach is designed to classify the abnormal signal from the normal one automatically. This work aims to improve the accuracy of epileptic seizure detection and reduce computational costs. To address this, the proposed framework uses the 54-DWT mother wavelets analysis of EEG signals using the Genetic algorithm (GA) in combination with other four machine learning (ML) classifiers: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Naive Bayes (NB). The performance of 14 different combinations of two-class epilepsy detection is investigated using these four ML classifiers. The experimental results show that the four classifiers produce comparable results for the derived statistical features from the 54-DWT mother wavelets; however, the ANN classifier achieved the best accuracy in most datasets combinations, and it outperformed the other examined classifiers. |
topic |
Electroencephalogram (EEG) discrete wavelet transform (DWT) epilepsy artificial neural network k-nearest neighbor (k-NN) support vector machine (SVM) |
url |
https://ieeexplore.ieee.org/document/8972376/ |
work_keys_str_mv |
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1724187402086383616 |