生物医学工程研究2026，Vol.45Issue(1)：1-6,6.DOI:10.19529/j.cnki.1672-6278.2026.01.01
基于迁移学习的癫痫脑电时空预测模型研究
Epilepsy electroencephalogram spatio-temporal prediction model based on transfer learning
摘要
Abstract
To address the deficiencies in cross-subject generalizability and robustness of existing epileptic electroencephalogram(EEG)prediction models,we proposed a transfer learning-based epileptic prediction model with multi-scale spatio-temporal features(TLEP-MST)by integrating signal analysis and deep learning technology.Firstly,the raw data was analyzed through independent com-ponent analysis(ICA)to remove artifacts,and the temporal feature extraction module and wavelet convolutional layer were used to ex-tract the time-frequency information in the EEG signals.Then,the adaptive attention mechanism was applied to perform multi-channel weight assignment of the time-frequency information,and obtain spatial features of the EEG signals.Finally,transfer learning was in-corporated to reduce data distribution discrepancies between source and target domains,enhancing the model generalization perform-ance.The model was experimented on the public dataset CHB-MIT.In the cross-validation,the accuracy rate,specificity and false positive rate of the model was 91.88%,96.49%and 0.0369/h,respectively.In patient-specific experiments,the specificity improved from 67.04%to 85.06%,and the false positive rate decreased from 0.4194/h to 0.3485/h after introducing transfer learning.This re-search is of great value in predicting the EEG of epilepsy across subjects.关键词
癫痫预测/深度学习/时频分析/迁移学习/脑电信号Key words
Epilepsy prediction/Deep learning/Time-frequency analysis/Transfer learning/Electroencephalogram signal分类
医药卫生引用本文复制引用
郑凯哲,唐诗诗,刘一聪,练伟,胡珊,许晓伟,周毅..基于迁移学习的癫痫脑电时空预测模型研究[J].生物医学工程研究,2026,45(1):1-6,6.基金项目
国家重点研发计划(2022YFC3601600) （2022YFC3601600）
广东省自然科学基金项目(2024A1515011989). （2024A1515011989）
