文献类型：会议论文

英文题名：MTL-SSU: A Multi-Task Self-Supervised Learning Framework for Epileptic Seizure Prediction

作者：Lou, Jionghao[1];Zhang, Jian[2];Li, Zhongmei[1];Feng, Enbo[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai, Peoples R China;[2]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai, Peoples R China

会议论文集：2024 International Conference on Bioinformatics and Biomedicine

会议日期：DEC 03-06, 2024

会议地点：Lisbon, PORTUGAL

语种：英文

外文关键词：EEG; epilepsy; seizure prediction; multi-task learning; self-supervised learning

摘要：Predicting epileptic seizures is crucial for reducing patient suffering and informing clinical treatments. However, training supervised models for seizure prediction requires extensive labeled data, which is labor-intensive and costly. We propose a novel Multi-Task Self-Supervised Learning framework with U-Net architecture (MTL-SSU) for EEG-based seizure prediction. Unlike conventional self-supervised methods, MTLSSU incorporates domain-specific knowledge for epileptic seizure analysis. Leveraging U-Net, the approach performs two key tasks: mimic segmentation and channel discrimination. These tasks enable the model to capture effective feature representations from unlabeled EEG signals. After pretraining, the U-Net encoder and a linear classifier are fine-tuned for seizure prediction. Employing a rigorous k-fold cross-validation strategy on the CHB-MIT database, MTL-SSU achieves 93.55% accuracy and 0.9775 AUC in patient-specific epilepsy prediction. Notably, even in semisupervised learning scenarios with limited labeled data, MTLSSU exhibits exceptional performance, highlighting its significant potential for clinical application.