An epileptic seizure prediction model based on a time-wise attention simulation module and a pretrained ResNet.

Abstract

Epilepsy is a neurological disorder that affects approximately 1% of the world's populations. Epilepsy prediction has been of great interest as it can identify and warn of an upcoming seizure, and to reduce the burden of the unpredictability of seizures. In this paper, we proposed a new seizure prediction model, TASM_ResNet, based on a time-wise attention simulation module and a pre-trained ResNet, using intracranial EEG signals. The simulation module with a time-wise attention was designed to convert EEG data into image like data and extract temporal features from raw data. Pre-trained ResNet was applied to reduce the amount of training data without initial training. Moreover, since the data is extremely imbalanced, we used an improved focal loss (FL) instead of the cross-entropy loss and investigated the optimal parameters for FL. Compared with a state-of-art CNN model, our proposed model achieved a better average AUC of 0.877. Moreover, our results demonstrated that EEG signals can be migrated to the image network which was pre-trained on large data set through a simulation module.