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A Hybrid Study for Epileptic Seizure Detection Based on Deep Learning using EEG Data

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Τίτλος: A Hybrid Study for Epileptic Seizure Detection Based on Deep Learning using EEG Data
Συγγραφείς: Abdulkadir Buldu, Kaplan Kaplan, Melih Kuncan
Πηγή: Journal of Universal Computer Science, Vol 30, Iss 7, Pp 909-934 (2024)
JUCS-Journal of Universal Computer Science 30(7): 909-934
Στοιχεία εκδότη: Pensoft Publishers, 2024.
Έτος έκδοσης: 2024
Θεματικοί όροι: STFT, transfer learn, CWT, Electronic computers. Computer science, epilepsy diagnosis, EEG, QA75.5-76.95, 0102 computer and information sciences, transfer learning, 01 natural sciences, 3. Good health
Περιγραφή: Epilepsy, a neurological disease characterized by recurrent seizures, can be diagnosed using Electroencephalogram (EEG) signals. Traditional diagnostic methods often face limitations, leading to delays and potential misdiagnoses. In response, researchers have been developing low-cost assistive systems to enhance diagnostic accuracy and reduce life-threatening risks for epilepsy patients. In this study, a hybrid approach is proposed to diagnose epilepsy disease. To validate the success of the proposed algorithm, Hauz Khas and Bonn data sets were used. AlexNet, GoogleNet, VGG19, ResNet50, and ResNet101 classifiers were employed in this study along with the Continuous Wavelet Transform (CWT) and Short Time Fourier Transform (STFT). To increase the generalization capability, 10-fold cross-validation method was used in the classification process. Firstly, the preictal and ictal moments in the Hauz Khas dataset was classified with 99.5% success rate by CWT method and Resnet101. Similarly, 99.8% accuracy was achieved in the binary classification of the Bonn dataset using the CWT method with Resnet101. Finally, for the classification with the AB-CD-E group, 99.33% classification success rate was achieved by using the CWT method with the Resnet-101 model. These findings underscore the potential of the proposed assistive system to significantly improve the diagnosis and management of epilepsy, demonstrating high accuracy and reliability across different datasets and classification techniques. 
Τύπος εγγράφου: Article
Περιγραφή αρχείου: text/html
ISSN: 0948-6968
0948-695X
DOI: 10.3897/jucs.109933
Σύνδεσμος πρόσβασης: https://doaj.org/article/de600eb129de4d64a2e0c6c629c32c4e
https://avesis.kocaeli.edu.tr/publication/details/f3aaf232-61cc-4e76-9b5c-b840d0fbf761/oai
https://hdl.handle.net/20.500.12604/8342
https://lib.jucs.org/article/109933/download/pdf/
https://lib.jucs.org/article/109933/
https://doi.org/10.3897/jucs.109933
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  Data: Epilepsy, a neurological disease characterized by recurrent seizures, can be diagnosed using Electroencephalogram (EEG) signals. Traditional diagnostic methods often face limitations, leading to delays and potential misdiagnoses. In response, researchers have been developing low-cost assistive systems to enhance diagnostic accuracy and reduce life-threatening risks for epilepsy patients. In this study, a hybrid approach is proposed to diagnose epilepsy disease. To validate the success of the proposed algorithm, Hauz Khas and Bonn data sets were used. AlexNet, GoogleNet, VGG19, ResNet50, and ResNet101 classifiers were employed in this study along with the Continuous Wavelet Transform (CWT) and Short Time Fourier Transform (STFT). To increase the generalization capability, 10-fold cross-validation method was used in the classification process. Firstly, the preictal and ictal moments in the Hauz Khas dataset was classified with 99.5% success rate by CWT method and Resnet101. Similarly, 99.8% accuracy was achieved in the binary classification of the Bonn dataset using the CWT method with Resnet101. Finally, for the classification with the AB-CD-E group, 99.33% classification success rate was achieved by using the CWT method with the Resnet-101 model. These findings underscore the potential of the proposed assistive system to significantly improve the diagnosis and management of epilepsy, demonstrating high accuracy and reliability across different datasets and classification techniques.&nbsp
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