Epileptic Seizure Classification Based on Random Neural Networks Using Discrete Wavelet Transform for Electroencephalogram Signal Decomposition
An epileptic seizure is a brief episode of symptoms and signs caused by excessive electrical activity in the brain. One of the major chronic neurological diseases, epilepsy, affects millions of individuals worldwide. Effective detection of seizure events is critical in the diagnosis and treatment of...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/2/599 |
| _version_ | 1827369873807769600 |
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| author | Syed Yaseen Shah Hadi Larijani Ryan M. Gibson Dimitrios Liarokapis |
| author_facet | Syed Yaseen Shah Hadi Larijani Ryan M. Gibson Dimitrios Liarokapis |
| author_sort | Syed Yaseen Shah |
| collection | DOAJ |
| description | An epileptic seizure is a brief episode of symptoms and signs caused by excessive electrical activity in the brain. One of the major chronic neurological diseases, epilepsy, affects millions of individuals worldwide. Effective detection of seizure events is critical in the diagnosis and treatment of patients with epilepsy. Neurologists monitor the electrical activity in the brains of patients to identify epileptic seizures by employing advanced sensing techniques, including electroencephalograms and electromyography. Machine learning-based classification of the EEG signal can help differentiate between normal signals and the patterns associated with epileptic seizures. This work presents a novel approach for the classification of epileptic seizures using random neural network (RNN). The proposed model has been trained and tested using two publicly available datasets: CHB-MIT and BONN, provided by Children’s Hospital Boston-Massachusetts Institute of Technology and the University of Bonn, respectively. The results obtained from multiple experiments highlight that the proposed scheme outperformed traditional classification schemes such as artificial neural network and support vector machine. The proposed RNN-based model achieved accuracies of 93.27% and 99.84% on the CHB-MIT and BONN datasets, respectively. |
| first_indexed | 2024-03-08T09:58:55Z |
| format | Article |
| id | doaj.art-8cd898099ce54c0e989e7779389a18fb |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T09:58:55Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8cd898099ce54c0e989e7779389a18fb2024-01-29T13:43:05ZengMDPI AGApplied Sciences2076-34172024-01-0114259910.3390/app14020599Epileptic Seizure Classification Based on Random Neural Networks Using Discrete Wavelet Transform for Electroencephalogram Signal DecompositionSyed Yaseen Shah0Hadi Larijani1Ryan M. Gibson2Dimitrios Liarokapis3School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UKSMART Technology Research Centre, Glasgow Caledonian University, Cowcaddens Road, Glasgow G4 0BA, UKSchool of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UKSchool of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UKAn epileptic seizure is a brief episode of symptoms and signs caused by excessive electrical activity in the brain. One of the major chronic neurological diseases, epilepsy, affects millions of individuals worldwide. Effective detection of seizure events is critical in the diagnosis and treatment of patients with epilepsy. Neurologists monitor the electrical activity in the brains of patients to identify epileptic seizures by employing advanced sensing techniques, including electroencephalograms and electromyography. Machine learning-based classification of the EEG signal can help differentiate between normal signals and the patterns associated with epileptic seizures. This work presents a novel approach for the classification of epileptic seizures using random neural network (RNN). The proposed model has been trained and tested using two publicly available datasets: CHB-MIT and BONN, provided by Children’s Hospital Boston-Massachusetts Institute of Technology and the University of Bonn, respectively. The results obtained from multiple experiments highlight that the proposed scheme outperformed traditional classification schemes such as artificial neural network and support vector machine. The proposed RNN-based model achieved accuracies of 93.27% and 99.84% on the CHB-MIT and BONN datasets, respectively.https://www.mdpi.com/2076-3417/14/2/599random neural networkartificial neural networkepilepsydiscrete wavelet transform |
| spellingShingle | Syed Yaseen Shah Hadi Larijani Ryan M. Gibson Dimitrios Liarokapis Epileptic Seizure Classification Based on Random Neural Networks Using Discrete Wavelet Transform for Electroencephalogram Signal Decomposition Applied Sciences random neural network artificial neural network epilepsy discrete wavelet transform |
| title | Epileptic Seizure Classification Based on Random Neural Networks Using Discrete Wavelet Transform for Electroencephalogram Signal Decomposition |
| title_full | Epileptic Seizure Classification Based on Random Neural Networks Using Discrete Wavelet Transform for Electroencephalogram Signal Decomposition |
| title_fullStr | Epileptic Seizure Classification Based on Random Neural Networks Using Discrete Wavelet Transform for Electroencephalogram Signal Decomposition |
| title_full_unstemmed | Epileptic Seizure Classification Based on Random Neural Networks Using Discrete Wavelet Transform for Electroencephalogram Signal Decomposition |
| title_short | Epileptic Seizure Classification Based on Random Neural Networks Using Discrete Wavelet Transform for Electroencephalogram Signal Decomposition |
| title_sort | epileptic seizure classification based on random neural networks using discrete wavelet transform for electroencephalogram signal decomposition |
| topic | random neural network artificial neural network epilepsy discrete wavelet transform |
| url | https://www.mdpi.com/2076-3417/14/2/599 |
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