Detection of epileptic seizure in EEG signals using machine learning and deep learning techniques
Abstract Around 50 million individuals worldwide suffer from epilepsy, a chronic, non-communicable brain disorder. Several screening methods, including electroencephalography, have been proposed to identify epileptic episodes. EEG data, which are frequently utilised to enhance epilepsy analysis, off...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-01-01
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| Series: | Journal of Engineering and Applied Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s44147-023-00353-y |
| _version_ | 1797349966183661568 |
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| author | Pankaj Kunekar Mukesh Kumar Gupta Pramod Gaur |
| author_facet | Pankaj Kunekar Mukesh Kumar Gupta Pramod Gaur |
| author_sort | Pankaj Kunekar |
| collection | DOAJ |
| description | Abstract Around 50 million individuals worldwide suffer from epilepsy, a chronic, non-communicable brain disorder. Several screening methods, including electroencephalography, have been proposed to identify epileptic episodes. EEG data, which are frequently utilised to enhance epilepsy analysis, offer essential information on the electrical processes of the brain. Prior to the emergence of deep learning (DL), feature extraction was accomplished by standard machine learning techniques. As a result, they were only as good as the people who made the features by hand. But with DL, both feature extraction and classification are fully automated. These methods have significantly advanced several fields of medicine, including the diagnosis of epilepsy. In this paper, the works focused on automated epileptic seizure detection using ML and DL techniques are presented as well as their comparative analysis is done. The UCI-Epileptic Seizure Recognition dataset is used for training and validation. Some of the conventional ML and DL algorithms are used with a proposed model which uses long short-term memory (LSTM) to find the best approach. Post that comparative analysis is performed on these algorithms to find the best approach for epileptic seizure detection. As a result, the proposed model LSTM gives a validation accuracy of 97% giving the most appropriate and precise result as compared to other mentioned algorithms used in this study. |
| first_indexed | 2024-03-08T12:37:59Z |
| format | Article |
| id | doaj.art-fcd8aa4afd4440a78f0a11850532d4f3 |
| institution | Directory Open Access Journal |
| issn | 1110-1903 2536-9512 |
| language | English |
| last_indexed | 2024-03-08T12:37:59Z |
| publishDate | 2024-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Engineering and Applied Science |
| spelling | doaj.art-fcd8aa4afd4440a78f0a11850532d4f32024-01-21T12:23:27ZengSpringerOpenJournal of Engineering and Applied Science1110-19032536-95122024-01-0171111510.1186/s44147-023-00353-yDetection of epileptic seizure in EEG signals using machine learning and deep learning techniquesPankaj Kunekar0Mukesh Kumar Gupta1Pramod Gaur2Department of CSE, Swami Keshvanand Institute of Technology, Management & GramothanDepartment of CSE, Swami Keshvanand Institute of Technology, Management & GramothanBITS PilaniAbstract Around 50 million individuals worldwide suffer from epilepsy, a chronic, non-communicable brain disorder. Several screening methods, including electroencephalography, have been proposed to identify epileptic episodes. EEG data, which are frequently utilised to enhance epilepsy analysis, offer essential information on the electrical processes of the brain. Prior to the emergence of deep learning (DL), feature extraction was accomplished by standard machine learning techniques. As a result, they were only as good as the people who made the features by hand. But with DL, both feature extraction and classification are fully automated. These methods have significantly advanced several fields of medicine, including the diagnosis of epilepsy. In this paper, the works focused on automated epileptic seizure detection using ML and DL techniques are presented as well as their comparative analysis is done. The UCI-Epileptic Seizure Recognition dataset is used for training and validation. Some of the conventional ML and DL algorithms are used with a proposed model which uses long short-term memory (LSTM) to find the best approach. Post that comparative analysis is performed on these algorithms to find the best approach for epileptic seizure detection. As a result, the proposed model LSTM gives a validation accuracy of 97% giving the most appropriate and precise result as compared to other mentioned algorithms used in this study.https://doi.org/10.1186/s44147-023-00353-yEpilepsy analysisElectroencephalogramEpileptic seizure detectionLSTMComparative analysisUCI dataset |
| spellingShingle | Pankaj Kunekar Mukesh Kumar Gupta Pramod Gaur Detection of epileptic seizure in EEG signals using machine learning and deep learning techniques Journal of Engineering and Applied Science Epilepsy analysis Electroencephalogram Epileptic seizure detection LSTM Comparative analysis UCI dataset |
| title | Detection of epileptic seizure in EEG signals using machine learning and deep learning techniques |
| title_full | Detection of epileptic seizure in EEG signals using machine learning and deep learning techniques |
| title_fullStr | Detection of epileptic seizure in EEG signals using machine learning and deep learning techniques |
| title_full_unstemmed | Detection of epileptic seizure in EEG signals using machine learning and deep learning techniques |
| title_short | Detection of epileptic seizure in EEG signals using machine learning and deep learning techniques |
| title_sort | detection of epileptic seizure in eeg signals using machine learning and deep learning techniques |
| topic | Epilepsy analysis Electroencephalogram Epileptic seizure detection LSTM Comparative analysis UCI dataset |
| url | https://doi.org/10.1186/s44147-023-00353-y |
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