EEG Seizure Prediction Based on Empirical Mode Decomposition and Convolutional Neural Network

Epilepsy is a common neurological disease characterized by recurrent seizures. Electroencephalography (EEG), which records neural activity, is commonly used to diagnose epilepsy. This paper proposes an Empirical Mode Decomposition (EMD) and Deep Convolutional Neural Network epileptic seizure prediction method. First, the original EEG signals are segmented using 30s sliding windows, and the segmented EEG signal is decomposed into Intrinsic Mode Functions (IMF) and residuals. Then, the entropy features which can better express the signal are extracted from the decomposed components. Finally, a deep convolutional neural network is used to construct the epileptic seizure prediction model. This experiment was conducted on the CHB-MIT Scalp EEG dataset to evaluate the performance of our proposed EMD-CNN epileptic EEG seizure detection model. The experimental results show that, compared with some previous EEG classification models, this model is helpful to improving the accuracy of epileptic seizure prediction.