CLEP: Contrastive Learning for Epileptic Seizure Prediction Using a Spatio-Temporal-Spectral Network

作     者：Guo, Lianghui Yu, Tao Zhao, Shijie Li, Xiaoli Liao, Xiaofeng Li, Yang 

作者机构：Department of Automation Sciences and Electrical Engineering Beihang University Beijing100083 China The Beijing Institute of Functional Neurosurgery Xuanwu Hospital Capital Medical University Beijing100053 China The Northwestern Polytechnical University Xi’an710072 China The Beijing Normal University Beijing100875 China The College of Computer Science Chongqing University Chongqing400715 China 

出 版 物：《TechRxiv》 (TechRxiv)

年 卷 期：2022年

核心收录：

主　　题：Convolution 

摘      要：Seizure prediction of epileptic preictal period through electroencephalogram (EEG) signals is important for clinical epilepsy diagnosis. However, recent deep learning-based methods commonly employ intra-subject training strategy and need sufficient data, which are laborious and time-consuming for a practical system and pose a great challenge for seizure predicting. Besides, multi-domain characterizations, including spatio-temporal-spectral dependencies in an epileptic brain are generally neglected or not considered simultaneously in current approaches, and this insufficiency commonly leads to suboptimal seizure prediction performance. To tackle the above issues, in this paper, we propose Contrastive Learning for Epileptic seizure Prediction (CLEP) using a Spatio-Temporal-Spectral Network (STS-Net). Specifically, the CLEP learns intrinsic epileptic EEG patterns across subjects by contrastive learning. The STS-Net extracts multi-scale temporal and spectral representations under different rhythms from raw EEG signals. Then, a novel triple attention layer (TAL) is employed to construct inter-dimensional interaction among multi-domain features. Moreover, a spatio dynamic graph convolution network (sdGCN) is proposed to dynamically model the spatial relationships between electrodes and aggregate spatial information. The proposed CLEP-STS-Net achieved a sensitivity of 96.7% and a false prediction rate of 0.072/h on the CHB-MIT scalp EEG database. We also validate the proposed method on clinical intracranial EEG (iEEG) database from the Xuanwu Hospital of Capital Medical University, and the predicting system yielded a sensitivity of 95%, a false prediction rate of 0.087/h. The experimental results outperform the state-of-the-art studies which validate the efficacy of our method. Our code is available at https://***/LianghuiGuo/CLEP-STS-Net. © 2022, CC BY.