Hybrid LSTM–Transformer Model for the Prediction of Epileptic Seizure Using Scalp EEG
Lili Xia, Ruiqi Wang, Haiming Ye, Bochang Jiang, Guang Li, Chao Ma, Zhongke Gao
Abstract
Epilepsy is a recurrent neurological disorder, and nearly 30% of epilepsy patients continue to experience symptoms despite taking anti-epileptic drugs. Predicting epileptic seizures enables patients to proactively take preventive measures against potential harm. Higher accuracy of seizure prediction would lead to a reduced incidence rate and decreased labor and resource consumption. In this study, we propose a hybrid LSTM-Transformer model for predicting epileptic seizures using scalp electroencephalogram (EEG) data. Time-frequency features are extracted through the short-time Fourier transform (STFT) applied to EEG signals, which are then inputted into the model to distinguish the interictal state and the preictal state. Our approach combines the long-distance dependence capability of Transformer with the advantages of LSTM in processing variable-length information, resulting in more robust and informative feature extraction. We evaluate our proposed method on the CHB-MIT dataset and conduct quantitative comparisons with recent methods. The results demonstrate that our method achieves the sensitivity of 99.75%, the false prediction rate (FPR) of 0, and the area under curve (AUC) of 99.39%. This novel approach provides valuable insights for epilepsy prediction.