Epileptic Seizure Detection Using Machine Learning: Taxonomy, Opportunities, and Challenges
Epilepsy is a life-threatening neurological brain disorder that gives rise to recurrent unprovoked seizures. It occurs due to abnormal chemical changes in our brains. For many years, studies have been conducted to support the automatic diagnosis of epileptic seizures for clinicians’ ease. For that,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/13/6/1058 |
| _version_ | 1827750329108660224 |
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| author | Muhammad Shoaib Farooq Aimen Zulfiqar Shamyla Riaz |
| author_facet | Muhammad Shoaib Farooq Aimen Zulfiqar Shamyla Riaz |
| author_sort | Muhammad Shoaib Farooq |
| collection | DOAJ |
| description | Epilepsy is a life-threatening neurological brain disorder that gives rise to recurrent unprovoked seizures. It occurs due to abnormal chemical changes in our brains. For many years, studies have been conducted to support the automatic diagnosis of epileptic seizures for clinicians’ ease. For that, several studies entail machine learning methods for early predicting epileptic seizures. Mainly, feature extraction methods have been used to extract the right features from the EEG data generated by the EEG machine. Then various machine learning classifiers are used for the classification process. This study provides a systematic literature review of the feature selection process and classification performance. This review was limited to finding the most used feature extraction methods and the classifiers used for accurate classification of normal to epileptic seizures. The existing literature was examined from well-known repositories such as MDPI, IEEE Xplore, Wiley, Elsevier, ACM, Springer link, and others. Furthermore, a taxonomy was created that recapitulates the state-of-the-art used solutions for this problem. We also studied the nature of different benchmark and unbiased datasets and gave a rigorous analysis of the working of classifiers. Finally, we concluded the research by presenting the gaps, challenges, and opportunities that can further help researchers predict epileptic seizures. |
| first_indexed | 2024-03-11T06:42:17Z |
| format | Article |
| id | doaj.art-e4e68bc7752148b28ccb1aa5106ccd0a |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T06:42:17Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-e4e68bc7752148b28ccb1aa5106ccd0a2023-11-17T10:33:45ZengMDPI AGDiagnostics2075-44182023-03-01136105810.3390/diagnostics13061058Epileptic Seizure Detection Using Machine Learning: Taxonomy, Opportunities, and ChallengesMuhammad Shoaib Farooq0Aimen Zulfiqar1Shamyla Riaz2Department of Computer Science, University of Management and Technology, Lahore 54000, PakistanDepartment of Computer Science, University of Management and Technology, Lahore 54000, PakistanDepartment of Computer Science, University of Management and Technology, Lahore 54000, PakistanEpilepsy is a life-threatening neurological brain disorder that gives rise to recurrent unprovoked seizures. It occurs due to abnormal chemical changes in our brains. For many years, studies have been conducted to support the automatic diagnosis of epileptic seizures for clinicians’ ease. For that, several studies entail machine learning methods for early predicting epileptic seizures. Mainly, feature extraction methods have been used to extract the right features from the EEG data generated by the EEG machine. Then various machine learning classifiers are used for the classification process. This study provides a systematic literature review of the feature selection process and classification performance. This review was limited to finding the most used feature extraction methods and the classifiers used for accurate classification of normal to epileptic seizures. The existing literature was examined from well-known repositories such as MDPI, IEEE Xplore, Wiley, Elsevier, ACM, Springer link, and others. Furthermore, a taxonomy was created that recapitulates the state-of-the-art used solutions for this problem. We also studied the nature of different benchmark and unbiased datasets and gave a rigorous analysis of the working of classifiers. Finally, we concluded the research by presenting the gaps, challenges, and opportunities that can further help researchers predict epileptic seizures.https://www.mdpi.com/2075-4418/13/6/1058epileptic seizuresepilepsy diagnosismachine learning electroencephalogram (EEG)feature extractionclassification |
| spellingShingle | Muhammad Shoaib Farooq Aimen Zulfiqar Shamyla Riaz Epileptic Seizure Detection Using Machine Learning: Taxonomy, Opportunities, and Challenges Diagnostics epileptic seizures epilepsy diagnosis machine learning electroencephalogram (EEG) feature extraction classification |
| title | Epileptic Seizure Detection Using Machine Learning: Taxonomy, Opportunities, and Challenges |
| title_full | Epileptic Seizure Detection Using Machine Learning: Taxonomy, Opportunities, and Challenges |
| title_fullStr | Epileptic Seizure Detection Using Machine Learning: Taxonomy, Opportunities, and Challenges |
| title_full_unstemmed | Epileptic Seizure Detection Using Machine Learning: Taxonomy, Opportunities, and Challenges |
| title_short | Epileptic Seizure Detection Using Machine Learning: Taxonomy, Opportunities, and Challenges |
| title_sort | epileptic seizure detection using machine learning taxonomy opportunities and challenges |
| topic | epileptic seizures epilepsy diagnosis machine learning electroencephalogram (EEG) feature extraction classification |
| url | https://www.mdpi.com/2075-4418/13/6/1058 |
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