Epileptic seizure prediction model based on multichannel spatiotemporal feature extraction
10.3969/j.issn.1005-202X.2025.02.011
- VernacularTitle:多通道时空特征提取的癫痫发作预测模型
- Author:
Ji'na E
1
;
Wenjie YU
;
Lingxia FEI
;
Jun ZHUANG
;
Guohua LIANG
;
Feng YANG
Author Information
1. 南方医科大学生物医学工程学院,广东 广州 510515
- Publication Type:Journal Article
- Keywords:
epileptic seizure prediction;
multichannel scalp EEG signal;
Stockwell transform;
adaptive feature extraction;
bidirectional neighborhood long short-term memory network
- From:
Chinese Journal of Medical Physics
2025;42(2):213-219
- CountryChina
- Language:Chinese
-
Abstract:
A novel epileptic seizure prediction prediction model based on multichannel temporal and spatial feature extractions is presented.The model applies Stockwell transform to the original multichannel electroencephalogram(EEG)signals for extracting time-frequency components.To address the issue of insignificant difference between preseizure and interseizure time-frequency features,an adaptive feature module composing of ConvNeXt,SENet and pyramid pooling module is designed to enhance the ability of capturing key time-frequency features in each EEG channel.Meanwhile,a prediction model based on Bi-NLSTM is constructed to improve the spatiotemporal dependence between the time-frequency features of multichannel high-order EEG for further promoting the epilepsy classification performance.On the CHB-MIT dataset,the model has an accuracy,sensitivity,specificity and AUC of 96.0%,97.8%,96.8%and 0.987,respectively,and the false positive rate per hour decreased to 0.038,outperforming the existing mainstream methods.In addition,the effect of each component on the model performance is verified by ablation study.The proposed method improves the overall performance for seizure prediction effectively by optimizing local time-frequency feature extraction and enhancing multichannel spatiotemporal dependence.
