文献类型：期刊文献

英文题名：A review of epilepsy detection and prediction methods based on EEG signal processing and deep learning

作者：Zhang, Xizhen[1,2];Zhang, Xiaoli[1,2];Huang, Qiong[1];Chen, Fuming[1]

第一作者：Zhang, Xizhen

通信作者：Chen, FM[1]

机构：[1]940 Hosp Joint Logist Support Force Chinese People, Med Support Ctr, Lanzhou, Peoples R China;[2]Gansu Univ Tradit Chinese Med, Lanzhou, Peoples R China

第一机构：940 Hosp Joint Logist Support Force Chinese People, Med Support Ctr, Lanzhou, Peoples R China

通信机构：[1]corresponding author), 940 Hosp Joint Logist Support Force Chinese People, Med Support Ctr, Lanzhou, Peoples R China.

年份：2024

卷号：18

外文期刊名：FRONTIERS IN NEUROSCIENCE

收录：;Scopus(收录号：2-s2.0-85210595158);WOS:【SCI-EXPANDED(收录号:WOS:001366014100001)】；

基金：The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was supported by the Natural Science Foundation of Gansu Province [Grant no. 22JR5RA002].

语种：英文

外文关键词：preprocessing; feature extraction; epilepsy detection; epilepsy prediction; deep learning

摘要：Epilepsy is a chronic neurological disorder that poses significant challenges to patients and their families. Effective detection and prediction of epilepsy can facilitate patient recovery, reduce family burden, and streamline healthcare processes. Therefore, it is essential to propose a deep learning method for efficient detection and prediction of epileptic electroencephalography (EEG) signals. This paper reviews several key aspects of epileptic EEG signal processing, focusing on epilepsy detection and prediction. It covers publicly available epileptic EEG datasets, preprocessing techniques, feature extraction methods, and deep learning-based networks used in these tasks. The literature is categorized based on patient independence, distinguishing between patient-independent and non-patient-independent studies. Additionally, the evaluation methods are classified into general classification indicators and specific epilepsy prediction criteria, with findings organized according to the prediction cycles reported in various studies. The review reveals several important insights. Despite the availability of public datasets, they often lack diversity in epilepsy types and are collected under controlled conditions that may not reflect real-world scenarios. As a result, signal preprocessing methods tend to be limited and may not fully represent practical conditions. Feature extraction and network designs frequently emphasize fusion mechanisms, with recent advances in Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) showing promising results, suggesting that new network models warrant further exploration. Studies using patient-independent data generally produce better results than those relying on non-patient-independent data. Metrics based on general classification methods typically perform better than those using specific epilepsy prediction criteria, though future research should focus on the latter for more accurate evaluation. Epilepsy prediction cycles are typically kept under 1 h, with most studies concentrating on intervals of 30 min or less.