Abstract
Epilepsy is one of the dangerous and a serious issues in the field of biomedical application. The exact prediction of seizures enables the patients to get over the weakness and ambiguity. Electroencephalogram (EEG) is a method that is generally used to diagnose conditions like epilepsy, sleep disorder, and brain tumor along with that it studies the changes in frequency and electrical activity of the brain during seizures. According to traditional approach, the features were extracted from EEG signals manually and conventional Machine learning (ML) based techniques have been used to identify this neurological disorder. Though the scientists succeeded in terms of effectiveness but multiple class classification using automatic feature extraction could not be achieved. In this paper, we have analyzed the five different EEG datasets and implemented the dataset which is recording the seizure activity using Deep learning model with Keras in Python to make the detection process more memory efficient and time-efficient. An accuracy of 97.46% is achieved with the help of proposed model and classification of other EEG datasets using deep learning would be analyzed in near future.
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Sowmya, N., Pradhan, S., Biswal, P.K., Panda, S.K., Misra, V.P. (2022). Epileptic Seizure Detection Using Deep Learning Architecture. In: Patnaik, S., Kountchev, R., Jain, V. (eds) Smart and Sustainable Technologies: Rural and Tribal Development Using IoT and Cloud Computing. Advances in Sustainability Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-19-2277-0_22
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DOI: https://doi.org/10.1007/978-981-19-2277-0_22
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