TY - GEN
T1 - Exploring the Neural Dynamics in Temporal Lobe Epilepsy
T2 - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
AU - Jiang, Wenhao
AU - Lin, Zhiguo
AU - Wang, Kaixuan
AU - Ding, Shihang
AU - Fang, Chunying
AU - Bo, Hongjian
AU - Xu, Cong
AU - Yu, Shengkun
AU - Wang, Tianyu
AU - Gu, Yifei
AU - Zhao, Tiejun
AU - Li, Haifeng
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Hidden Markov Model
KW - Neural Dynamics
KW - Stereo-electroencephalography
KW - Temporal Lobe Epilepsy
KW - Transformer
UR - https://www.scopus.com/pages/publications/85217277382
U2 - 10.1109/BIBM62325.2024.10822005
DO - 10.1109/BIBM62325.2024.10822005
M3 - 会议稿件
AN - SCOPUS:85217277382
T3 - Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
SP - 1526
EP - 1531
BT - Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
A2 - Cannataro, Mario
A2 - Zheng, Huiru
A2 - Gao, Lin
A2 - Cheng, Jianlin
A2 - de Miranda, Joao Luis
A2 - Zumpano, Ester
A2 - Hu, Xiaohua
A2 - Cho, Young-Rae
A2 - Park, Taesung
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 3 December 2024 through 6 December 2024
ER -