Exploring the Neural Dynamics in Temporal Lobe Epilepsy: A Study using Transformer and Hidden Markov Models

Advancing the understanding of temporal lobe epilepsy (TLE) requires sophisticated analytical tools. In this study, we introduce a hybrid model, namely the HMM-Wavformer, aiming at identifying the phasic brain activity patterns during seizures on Stereo-electroencephalography (SEEG) records. The model is composed of a wavelet packet decomposition (WPD) based signal processing module, an embedding module for spatial feature extraction, and a Transformer module to weigh the time-series frequency importance. The model is trained with a downstream seizure detection task on the HUP-iEEG dataset, demonstrating an accuracy of 92.75%. Frequency analysis identifies the most sensitive bands in TLE seizure detection. The Hidden Markov Model (HMM) is applied for the time-series analysis, categorizing the seizures into three ictal phases. Complementary analyses using power spectra and brain networks pinpoint biomarkers for each phase. The analysis results indicate that, the HMM-Wavformer model is able to effectively depict the neural dynamics of TLE seizures, aligning with prior medical studies, and provide a more detailed description of the staged characteristics of these seizures.