Hybrid LSTM-Transformer Model for the Prediction of Epileptic Seizure Using Scalp EEG

Epilepsy is a recurrent neurological disorder, and nearly 30% of patients with epilepsy continue to experience symptoms despite taking anti-epileptic drugs. Predicting epileptic seizures enables patients to proactively take preventive measures against potential harm. Higher accuracy (ACC) of seizure prediction would lead to a reduced incidence rate and decreased labor and resource consumption. In this study, we propose a hybrid long short-term memory (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 the 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 Children's Hospital Boston-MIT (CHB-MIT) dataset and conduct quantitative comparisons with recent methods. The results demonstrate that our method achieves a sensitivity of 99.75%, a false prediction rate (FPR) of 0/h, and an area under the curve (AUC) of 99.39%. This novel approach provides valuable insights for epilepsy prediction.