文献类型：期刊文献

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 University of Science and Technology, Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, Shanghai, China; [2] East China University of Science and Technology, School of Information Science and Engineering, Shanghai, China

年份：2024

起止页码：3565

外文期刊名：Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

收录：EI(收录号：20250717853750)

语种：英文

外文关键词：Contrastive Learning - Electroencephalography - Multi-task learning - Prediction models - Semi-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, MTL-SSU 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 semi-supervised learning scenarios with limited labeled data, MTL-SSU exhibits exceptional performance, highlighting its significant potential for clinical application. ? 2024 IEEE.