摘要
癫痫是一种由脑部神经元阵发性异常超同步电活动导致的慢性非传染性疾病,也是全球最常见的神经系统疾病之一.基于EEG的癫痫自动检测是指通过机器学习、分布检验、相关性分析和时频分析等数据分析方法,对癫痫发作阶段的EEG信号进行自动识别的研究问题,能够为癫痫诊疗与评估提供客观参考依据,从而减轻医生工作负担并提高治疗效率,因此具有十分重要的理论意义与实际应用价值.本文详细介绍基于EEG的癫痫自动识别整体框架,以及对应于各个步骤所涉及的典型方法.针对核心模块,即特征提取与分类器选择,进行方法总结与理论解释.最后,对癫痫自动检测研究领域的未来研究方向进行展望.
Epilepsy is a chronic non-communicable disease caused by the abnormal supersynchronous electrical activity of brain neurons.It is also one of the most common neurological diseases in the world.EEG-based automatic epilepsy detection,referring to the research problem of automatic identification of seizure stage in EEG signals through data analysis methods such as machine learning,distribution testing,correlation analysis,and time-frequency analysis,can provide an objective reference for epilepsy diagnosis and treatment to relieve the burden of medical professions,and may also improve the detection accuracy.This paper first introduces the flowchart of EEGbased automatic epilepsy detection,and then describes typical feature extraction and classification approaches in detail.Finally,future research directions are pointed out.
作者
彭睿旻
江军
匡光涛
杜浩
伍冬睿
邵剑波
PENG Rui-Min;JIANG Jun;KUANG Guang-Tao;DU Hao;WU Dong-Rui;SHAO Jian-Bo(Ministry of Education Key Laboratory on Image Informa-tion Processing and Intelligent Control,School of Artificial Intel-ligence and Automation,Huazhong University of Science and Technology,Wuhan 430074;Wuhan Children's Hospital(Wuhan Maternal and Child Healthcare Hospital),Tongji Medic-al College,Huazhong University of Science and Technology,Wuhan 430000)
出处
《自动化学报》
EI
CAS
CSCD
北大核心
2022年第2期335-350,共16页
Acta Automatica Sinica
基金
武汉市应用基础前沿项目(2020020601012240)
湖北省技术创新专项资助项目(2019AEA171)资助。
关键词
癫痫
头皮脑电
特征提取
分类
Epilepsy
EEG
feature extraction
classification
