Epileptic seizure prediction model based on multichannel spatiotemporal feature extraction(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2025年第2期
- Page:
- 213-219
- Research Field:
- 医学信号处理与医学仪器
- Publishing date:
Info
- Title:
- Epileptic seizure prediction model based on multichannel spatiotemporal feature extraction
- Author(s):
- E Jina1; YU Wenjie1; FEI Lingxia2; ZHUANG Jun2; LIANG Guohua1; YANG Feng1
- 1. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China 2. Department of Epilepsy, Guangdong Sanjiu Brain Hospital, Guangzhou 510520, China
- Keywords:
- Keywords: epileptic seizure prediction multichannel scalp EEG signal Stockwell transform adaptive feature extraction bidirectional neighborhood long short-term memory network
- PACS:
- R318；TP391
- DOI:
- DOI:10.3969/j.issn.1005-202X.2025.02.011
- Abstract:
- 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.
Keywords: epileptic seizure prediction multichannel scalp EEG signal Stockwell transform adaptive feature extraction bidirectional neighborhood long short-term memory network
Last Update: 2025-01-22