Exploring the Neural Dynamics in Temporal Lobe Epilepsy: A Study using Transformer and Hidden Markov Models
Wenhao Jiang, Zhiguo Lin, Kaixuan Wang, Shihang Ding, Chunying Fang, Hongjian Bo, Cong Xu, Shengkun Yu, Tianyu Wang, Yifei Gu, Tiejun Zhao, Haifeng Li · 2024
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.