基于EEG的癫痫自动检测: 综述与展望

彭睿旻; 江军; 匡光涛; 杜浩; 伍冬睿; 邵剑波

doi:10.16383/j.aas.c200745

基于EEG的癫痫自动检测: 综述与展望

doi: 10.16383/j.aas.c200745

彭睿旻^1,,
江军^2,,
匡光涛^2,,
杜浩^2,,
伍冬睿^1,,
邵剑波^2,

1.
华中科技大学人工智能与自动化学院图像信息处理与智能控制教育部重点实验室武汉 430074
2.
华中科技大学同济医学院附属武汉儿童医院 (武汉市妇幼保健院) 武汉 430000

基金项目: 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

详细信息

作者简介:
彭睿旻：华中科技大学人工智能与自动化学院博士研究生. 主要研究方向为机器学习, 脑机接口. E-mail: rmpeng2019@hust.edu.cn

江军：华中科技大学同济医学院附属武汉儿童医院 (武汉市妇幼保健院)神经电生理室主任. 主要研究方向为神经电生理, 癫痫, 抽动障碍. E-mail: jiangjunzm@163.com

匡光涛：华中科技大学同济医学院附属武汉儿童医院 (武汉市妇幼保健院)神经电生理室技师. 主要研究方向为脑机接口, 脑电图定量分析. E-mail: jacksondear@163.com

杜浩：华中科技大学同济医学院附属武汉儿童医院 (武汉市妇幼保健院)神经外科主任. 主要研究方向为颅脑损伤脑肿瘤, 先天性畸形, 脑血管病, 癫痫及脑瘫等方面外科治疗. E-mail: duhaodt@163.com

伍冬睿：华中科技大学人工智能与自动化学院教授. 主要研究方向为机器学习, 脑机接口, 计算智能, 情感计算. 本文通信作者. E-mail: drwu@hust.edu.cn

邵剑波：教授, 二级主任医师, 医学博士, 国务院特殊津贴专家. 现任华中科技大学同济医学院附属武汉儿童医院 (武汉市妇幼保健院)院长, 江汉大学儿科临床学院院长, 医学影像中心主任. 中华医学会放射学分会儿科学组副组长, 中国医师协会放射学分会儿科组副组长. 湖北省放射学分会副主任委员, 武汉市放射学分会副主任委员. 主要研究方向小儿(含胎儿)临床放射, CT, MRI诊断. E-mail: shaojb2002@sina.com

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出版历程
- 收稿日期: 2020-09-10
- 录用日期: 2021-04-16
- 网络出版日期: 2021-05-27
- 刊出日期: 2022-02-18

EEG-based Automatic Epilepsy Detection: Review and Outlook

1.
Ministry of Education Key Laboratory on Image Information Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074
2.
Wuhan Children＇s Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000

Funds: Supported by Wuhan Science and Technology Bureau (2020020601012240), Technology Innovation Project of Hubei Province of China (2019AEA171)

More Information

Author Bio:
PENG Rui-Min　Ph. D. candidate at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. Her research interest covers machine learning and brain-computer interfaces

JIANG Jun　Director at the Neuroelectrophysiology Room, Wuhan Children＇s Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science & Technology. Her research interest covers neurophysiology, epilepsy, tic disorder

KUANG Guang-Tao　Medical technician at the Neuroelectrophysiology Room, Wuhan Children＇s Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science & Technology. His research interest covers brain-computer interfaces, quantitative analysis of electroencephalograms

DU Hao　Director at the Neurosurgery Department, Wuhan Children＇s Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science & Technology. His research interest covers surgical treatments of head injury brain tumor, congenital malformations, cerebrovascular disease, epilepsy and cerebral palsy

WU Dong-Rui　Professor at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. His research interest covers machine learning, brain-computer interfaces, computational intelligence, affective computing. Corresponding author of this paper

SHAO Jian-Bo　Professor, level-2 chief physician, doctor of medicine, doctoral supervisor, special allowance expert of the State Council. Currently dean of Wuhan Children＇s Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science & Technology, dean of Pediatric Clinic School of Jianghan University, Medical Imaging Center Director. Deputy leader of the Pediatrics Group of the Radiology Branch of the Chinese Medical Association, Deputy Leader of the Pediatrics Group of the Radiology Branch of the Chinese Medical Doctor Association. Deputy chairman of the Hubei Radiology Branch, deputy chairman of the Wuhan Radiology Branch. His research interest covers pediatric (including fetus) clinical radiology, CT, MRI diagnosis

摘要

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.
- 癫痫 /
- 头皮脑电 /
- 特征提取 /
- 分类
Abstract: 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 EEG-based automatic epilepsy detection, and then describes typical feature extraction and classification approaches in detail. Finally, future research directions are pointed out.
- Epilepsy /
- EEG /
- feature extraction /
- classification

HTML全文

图 1 基于EEG的癫痫自动检测流程

Fig. 1 Flowchart of EEG-based automatic epilepsy detection

下载: 全尺寸图片幻灯片

图 2 癫痫自动检测中脑电功率谱密度示例(Bonn数据集^[66])

Fig. 2 EEG PSD for automatic seizure detection (Bonn dataset^[66])

下载: 全尺寸图片幻灯片

图 3 脑电图经小波变换后各分量示例(Bonn数据集^[66])

Fig. 3 Wavelet transforms for automatic seizure detection (Bonn dataset^[66])

下载: 全尺寸图片幻灯片

图 4 癫痫自动检测中的脑电样本熵示例(Bonn数据集^[66])

Fig. 4 Sample entropy for automatic seizure detection (Bonn dataset^[66])

下载: 全尺寸图片幻灯片

表 1 常见癫痫数据集

Table 1 Popular epilepsy datasets

数据集名称	受试数量	总发作次数	信号类型	采样频率 (Hz)	总时长 (小时)
Freiburg^[64]	21	87	颅内 EEG	256	708
CHB-MIT^[65]	22	163	头皮 EEG	256	844
Bonn^[66]	10	100	颅内 EEG	256	708
Kaggle^[67]	2 (人)	48	颅内 EEG	400	627
Kaggle^[67]	5 (狗)	48	颅内 EEG	5 000	627
Barcelona^[68]	5	3 750	颅内 EEG	512	83

下载: 导出CSV

表 2 癫痫自动检测特征总结

Table 2 Summary of features used in automatic seizure detection

文献	特征	类型
Gotman^[6]	振幅、均值、变异系数等	时域特征
Hjorth^[73]	Hjorth 参数
Chandaka 等^[29]	互相关图的图心、等效宽度等
Kalatzis 等^[96]	平均绝对信号斜率、峰间值、峰间斜率
Putten 等^[72]	极值次数、过零率
Park 等^[97]	$\alpha$, $\beta$, $\theta$, $\gamma$, $\delta$波的功率谱并求其均值、方差、标准差等特征	频域特征
Alkan 等^[32]	功率谱
Gotman 等^[76]	峰值频率、主频峰值带宽
Naghsh-Nilchi和Aghashahi^[34]	频谱边缘频率
Kıymık 等^[35]	短时傅里叶变换	时频域特征
Hernandez 等^[75]	离散小波变换, 并提取均值、方差、标准差、最值等特征
Pachori和Patidar^[86]	经验模态分解获得本征模态函数
Ghayab 等^[98]	使用可调 Q 因子小波变换进行时频变换并提取均值、方差、标准差、偏态、峰度、中值等特征
Oweis和Abdulhay^[99]	希尔伯特–黄变换
Acharya 等^[100]	香浓熵、对数能量、近似熵、排列熵、Renyi 熵、模糊熵	非线性特征
Tian 等^[101]	谱熵
Nicolaou和Georgiou^[40]	排列熵
Azami 等^[102]	多尺度模糊熵、样本熵
Shayegh 等^[103]	最大 Lyapunov 指数分量
Mirowski 等^[104]	最大互相关指数
Wang 等^[94]	Hurst 参数
Faul 等^[105]	奇异值分解熵、Kolmogorov 复杂度、条件熵、排列熵、奇异谱的 Fisher 信息量、最大 Lyapunov 指数分量

下载: 导出CSV

表 3 癫痫自动检测机器学习方法总结

Table 3 Summary of automatic seizure detection methods

作者	数据集	特征	分类器	结果
Guo 等^[130]	Bonn	ApEn	ANN	Acc: 98.27 %
Liang 等^[44]	Bonn	ApEn、频域特征	LDA、SVM、ANN	Acc: 97.82 % ~ 98.51 %
Samiee 等^[50]	Bonn	时频域特征	NB、LR、SVM、K 近邻、ANN	Acc: 98.3 %
张涛等^[131]	Bonn	频率切片小波变换	SVM	Acc: 98.33 %
Yan 等^[132]	Bonn	SAE	SVM	Acc: 100.0 %
Ahmed 等^[109]	非公开数据	时域、频域、非线性特征	SVM、RBF-SVM	Sen: 82.6 %, Spec: 90 %
Acharya 等^[56]	Bonn	DCNN	DCNN	Acc: 88.67 %
Qiu 等^[133]	Bonn	DSAE	LR	Acc: 100.0 %
Yuan 等^[134]	CHB-MIT	SAE	PSVM	Acc: 96.61 %
Ahmedt-Aristizabal 等^[135]	QUT、MAEUnit	CNN	SVM	Acc: 95.19 %
Hussein 等^[136]	Bonn	时域、频域、时频域	Softmax	Acc: 100.0 %
Roy 等^[137]	Bonn	CNN、RNN	LR、MLP	Acc: 82.04 %
Thomas 等^[138]	MGH	CNN	SVM	Acc: 83.86 %
Daoud 等^[139]	Bonn	非线性特征	DCNN、MLP	Acc: 98.6 %
Hu 等^[85]	CHB-MIT	MAS+CNN	SVM	Acc: 86.25 %
Jaafar 等^[140]	Freiburg	LSTM	Softmax	Acc: 97.75 %
Chen 等^[141]	Bonn	DWT+非线性特征	SVM	Acc: 99.5 %
Tian 等^[61]	CHB-MIT	时域、频域、时频域	NB、DT、SVM、K 近邻、TSK-FS	Acc: 98.33 %
Cao 等^[142]	CHB-MIT	CNN	SVM、KNN、ELM、KELM、RF	Acc: 99.33 %
Zhang 等^[143]	TUH	CNN	RF、KNN、SVM	Acc: 97.4 %

下载: 导出CSV

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