Abstract
The paper proposes a generalized approach for epileptic seizure prediction rather than a patient-specific approach. The early diagnosis of seizures may assist in reducing the severity of damage and can be utilized to aid in the treatment of epilepsy patients. Developing a patient-independent model is more challenging than a patient-specific model due to the EEG variability across patients. Our objective is to predict seizure accurately by detecting the pre-ictal state that occurs prior to a seizure. We have used the “CHB-MIT Scalp EEG Dataset” for our research and implemented the research work using Butterworth Bandpass Filter and simple 2D Convolutional Neural Network to differentiate pre-ictal and inter-ictal signals. We have achieved accuracy of 89.5%, sensitivity 89.7%, precision 89.0% and AUC, the area under the curve is 89.5% with our proposed model. In addition, we have addressed several researchers’ seizure prediction models, sketched their core mechanism, predictive effectiveness, and compared them with ours.
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Mustaqeem, N., Rahman, T., Priyo, J.F.B.K., Parvez, M.Z., Ahmed, T. (2023). Epileptic Seizure Prediction Using Bandpass Filtering and Convolutional Neural Network. In: Satu, M.S., Moni, M.A., Kaiser, M.S., Arefin, M.S. (eds) Machine Intelligence and Emerging Technologies. MIET 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 491. Springer, Cham. https://doi.org/10.1007/978-3-031-34622-4_2
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