Epileptic Seizure Prediction using Stacked CNN-BiLSTM: A Novel Approach

In this work, we propose a novel hybrid architecture for epileptic seizure prediction, utilizing a deep learning approach by stacking the Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) layers. The proposed approach employs a series of one dimensional (1D) convolution layers, each with several filters with lengths varying exponentially. The Deep Bi-LSTM layers are subsequently integrated to the design to create a densely connected feed-forward structure. The model effectively prioritizes spatio-temporal information, thus extracting key insights for identification of inter-ictal and preictal features. The Boston Children’s Hospital–MIT datasets (CHB-MIT) are utilized and 5-fold cross validation is applied for training the model. The proposed model has undergone comprehensive evaluations, with sensitivity of 97.63%, precision of 98.30%, F1-Score of 98.25%, and an AUC-ROC of 0.9 across six patients. It can predict seizures 30 minutes before their onset, allowing individuals ample time to take preventive measures. Compared to the state-of-the-art approach, our model achieves a higher accuracy by 3.44% and demonstrating improved prediction times. IEEE