Abstract
Epilepsy is a group of neurological disorders characterized by recurrent epileptic seizures. According to the World Health Organization, there are about 50 million people who have epilepsy. Electroencephalography (EEG) is used as a powerful tool for doctors in diagnosis. By visualizing the EEG recordings, experts can initiate antiepileptic drug therapy and reduce the risk of future seizure. However, this current method is time-consuming and inflexible. With the development of deep learning, the problems can be solved. In this study, a 25-channel EEG data recorded at Neurology Department of 115 Hospital was converted into images after filtering, and a one-dimensional convolutional neural network (1-D CNN) model was applied to classify seizure states as seizure or non-seizure accurately. The accuracy of the model is about 90%. With the development of deep learning, it is more convenient to distinguish between different data of seizure and non-seizure without difficulties and consumption of time.
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Acknowledgements
This research is funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under grant number C2020-20-11.
Conflicts of Interest
Vu Nguyen Phuong Quynh, Nguyen Thi Minh Huong and Do Chan Minh Hiep declare that there is no conflict of interest.
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Vu Nguyen Phuong, Q., Do Tran, M.H., Nguyen Thi Minh, H. (2022). Epileptic Seizure Detection Based on Electroencephalography Signals and One-Dimensional Convolutional Neural Network. In: Van Toi, V., Nguyen, TH., Long, V.B., Huong, H.T.T. (eds) 8th International Conference on the Development of Biomedical Engineering in Vietnam. BME 2020. IFMBE Proceedings, vol 85. Springer, Cham. https://doi.org/10.1007/978-3-030-75506-5_61
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DOI: https://doi.org/10.1007/978-3-030-75506-5_61
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