Wang Zhongpeng, Song Xiaoxin, Chen Long, Nan Jinxiang, Sun Yulin, Pang Meijun, Zhang Kuo, Liu Xiuyun, Ming Dong
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072 China.
Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, 300392 China.
Cogn Neurodyn. 2024 Oct;18(5):2731-2750. doi: 10.1007/s11571-024-10109-w. Epub 2024 May 7.
At present, at least 30% of refractory epilepsy patients in the world cannot be effectively controlled and treated. The suddenness and unpredictability of seizures greatly affect the physical and mental health and even the life safety of patients, and the realization of early prediction of seizures and the adoption of interventions are of great significance to the improvement of patients' quality of life. In this paper, we firstly introduce the design process of EEG-based seizure prediction methods, introduce several databases commonly used in the research, and summarize the commonly used methods in pre-processing, feature extraction, classification and identification, and post-processing. Then, based on scalp EEG and intracranial EEG respectively, we reviewed the current status of epileptic seizure prediction research from five commonly used feature analysis methods, and make a comprehensive evaluation of both. Finally, this paper describes the reasons why the current algorithms cannot be applied to the clinic, summarizes their limitations, and gives corresponding suggestions, aiming to provide improvement directions for subsequent research. In addition, deep learning algorithms have emerged in recent years, and this paper also compares the advantages and disadvantages of deep learning algorithms with traditional machine learning methods, in the hope of providing researchers with new technologies and new ideas and making significant breakthroughs in the field of epileptic seizure prediction.
目前，全球至少30%的难治性癫痫患者无法得到有效控制和治疗。癫痫发作的突发性和不可预测性极大地影响了患者的身心健康甚至生命安全，实现癫痫发作的早期预测并采取干预措施对提高患者生活质量具有重要意义。本文首先介绍基于脑电图的癫痫发作预测方法的设计过程，介绍研究中常用的几个数据库，并总结预处理、特征提取、分类识别及后处理中常用的方法。然后，分别基于头皮脑电图和颅内脑电图，从五种常用的特征分析方法回顾癫痫发作预测研究的现状，并对两者进行综合评价。最后，本文阐述当前算法无法应用于临床的原因，总结其局限性，并给出相应建议，旨在为后续研究提供改进方向。此外，深度学习算法近年来不断涌现，本文还比较了深度学习算法与传统机器学习方法的优缺点，希望为研究人员提供新技术和新思路，在癫痫发作预测领域取得重大突破。
