Detection of epileptic seizures through EEG signals using entropy features and ensemble learning
IntroductionEpilepsy is a disorder of the central nervous system that is often accompanied by recurrent seizures. World health organization (WHO) estimated that more than 50 million people worldwide suffer from epilepsy. Although electroencephalogram (EEG) signals contain vital physiological and pat...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Human Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnhum.2022.1084061/full |
| _version_ | 1797924409836568576 |
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| author | Mahshid Dastgoshadeh Zahra Rabiei |
| author_facet | Mahshid Dastgoshadeh Zahra Rabiei |
| author_sort | Mahshid Dastgoshadeh |
| collection | DOAJ |
| description | IntroductionEpilepsy is a disorder of the central nervous system that is often accompanied by recurrent seizures. World health organization (WHO) estimated that more than 50 million people worldwide suffer from epilepsy. Although electroencephalogram (EEG) signals contain vital physiological and pathological information of brain and they are a prominent medical tool for detecting epileptic seizures, visual interpretation of such tools is time-consuming. Since early diagnosis of epilepsy is essential to control seizures, we present a new method using data mining and machine learning techniques to diagnose epileptic seizures automatically.MethodsThe proposed detection system consists of three main steps: In the first step, the input signals are pre-processed by discrete wavelet transform (DWT) and sub-bands containing useful information are extracted. In the second step, the features of each sub-band are extracted by approximate entropy (ApEn) and sample entropy (SampEn) and then these features are ranked by ANOVA test. Finally, feature selection is done by the FSFS technique. In the third step, three algorithms are used to classify seizures: Least squared support vector machine (LS-SVM), K nearest neighbors (KNN) and Naive Bayes model (NB).Results and discussionThe average accuracy for both LS-SVM and NB was 98% and it was 94.5% for KNN, while the results show that the proposed method can detect epileptic seizures with an average accuracy of 99.5%, 99.01% of sensitivity and 100% of specificity which show an improvement over most similar methods and can be used as an effective tool in diagnosing this complication. |
| first_indexed | 2024-04-10T15:01:05Z |
| format | Article |
| id | doaj.art-855cde00685a4e309ef5abde2bfaf930 |
| institution | Directory Open Access Journal |
| issn | 1662-5161 |
| language | English |
| last_indexed | 2024-04-10T15:01:05Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj.art-855cde00685a4e309ef5abde2bfaf9302023-02-15T12:00:43ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612023-02-011610.3389/fnhum.2022.10840611084061Detection of epileptic seizures through EEG signals using entropy features and ensemble learningMahshid DastgoshadehZahra RabieiIntroductionEpilepsy is a disorder of the central nervous system that is often accompanied by recurrent seizures. World health organization (WHO) estimated that more than 50 million people worldwide suffer from epilepsy. Although electroencephalogram (EEG) signals contain vital physiological and pathological information of brain and they are a prominent medical tool for detecting epileptic seizures, visual interpretation of such tools is time-consuming. Since early diagnosis of epilepsy is essential to control seizures, we present a new method using data mining and machine learning techniques to diagnose epileptic seizures automatically.MethodsThe proposed detection system consists of three main steps: In the first step, the input signals are pre-processed by discrete wavelet transform (DWT) and sub-bands containing useful information are extracted. In the second step, the features of each sub-band are extracted by approximate entropy (ApEn) and sample entropy (SampEn) and then these features are ranked by ANOVA test. Finally, feature selection is done by the FSFS technique. In the third step, three algorithms are used to classify seizures: Least squared support vector machine (LS-SVM), K nearest neighbors (KNN) and Naive Bayes model (NB).Results and discussionThe average accuracy for both LS-SVM and NB was 98% and it was 94.5% for KNN, while the results show that the proposed method can detect epileptic seizures with an average accuracy of 99.5%, 99.01% of sensitivity and 100% of specificity which show an improvement over most similar methods and can be used as an effective tool in diagnosing this complication.https://www.frontiersin.org/articles/10.3389/fnhum.2022.1084061/fullepileptic seizuresmachine learningensemble learningentropy featuresdiscrete wavelet transformANOVA |
| spellingShingle | Mahshid Dastgoshadeh Zahra Rabiei Detection of epileptic seizures through EEG signals using entropy features and ensemble learning Frontiers in Human Neuroscience epileptic seizures machine learning ensemble learning entropy features discrete wavelet transform ANOVA |
| title | Detection of epileptic seizures through EEG signals using entropy features and ensemble learning |
| title_full | Detection of epileptic seizures through EEG signals using entropy features and ensemble learning |
| title_fullStr | Detection of epileptic seizures through EEG signals using entropy features and ensemble learning |
| title_full_unstemmed | Detection of epileptic seizures through EEG signals using entropy features and ensemble learning |
| title_short | Detection of epileptic seizures through EEG signals using entropy features and ensemble learning |
| title_sort | detection of epileptic seizures through eeg signals using entropy features and ensemble learning |
| topic | epileptic seizures machine learning ensemble learning entropy features discrete wavelet transform ANOVA |
| url | https://www.frontiersin.org/articles/10.3389/fnhum.2022.1084061/full |
| work_keys_str_mv | AT mahshiddastgoshadeh detectionofepilepticseizuresthrougheegsignalsusingentropyfeaturesandensemblelearning AT zahrarabiei detectionofepilepticseizuresthrougheegsignalsusingentropyfeaturesandensemblelearning |