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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

彭睿旻, 江军, 匡光涛, 杜浩, 伍冬睿, 邵剑波. 基于EEG的癫痫自动检测: 综述与展望. 自动化学报, 2021, 47(x): 1−16 doi: 10.16383/j.aas.c200745
引用本文: 彭睿旻, 江军, 匡光涛, 杜浩, 伍冬睿, 邵剑波. 基于EEG的癫痫自动检测: 综述与展望. 自动化学报, 2021, 47(x): 1−16 doi: 10.16383/j.aas.c200745
Peng Rui-Min, Jiang Jun, Kuang Guang-Tao, Du Hao, Wu Dong-Rui, Shao Jian-Bo. EEG-based automatic epilepsy detection: overview and outlook. Acta Automatica Sinica, 2021, 47(x): 1−16 doi: 10.16383/j.aas.c200745
Citation: Peng Rui-Min, Jiang Jun, Kuang Guang-Tao, Du Hao, Wu Dong-Rui, Shao Jian-Bo. EEG-based automatic epilepsy detection: overview and outlook. Acta Automatica Sinica, 2021, 47(x): 1−16 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
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硕士研究生. 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, 博士生导师, 图像信息处理与智能控制教育部重点实验室副主任. 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, 博士生导师, 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(第六临床学院)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. 本文通信作者. E-mail: shaojb2002@sina.com

EEG-Based Automatic Epilepsy Detection: Overview and Outlook

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 Postgraduate, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China. Research interests: Machine learning, brain-computer interfaces

    JIANG Jun Director, Neuroelectrophysiology Room, Wuhan Children's Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China. Research interests: Neurophysiology, epilepsy, tic disorder

    KUANG Guang-Tao Medical technician, Neuroelectrophysiology Room, Wuhan Children's Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China. Research interests: Brain-computer interfaces, quantitative analysis of electroencephalograms

    DU Hao Director, Neurosurgery Department, Wuhan Children's Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China. Research interests: Surgical treatments of head injury brain tumor, congenital malformations, cerebrovascular disease, epilepsy and cerebral palsy

    WU Dong-Rui Professor, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China. Research interests: 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 (Sixth Clinical College) Affiliated to Tongji Medical College of Huazhong University of Science and 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. Research interests: Pediatric (including fetus) clinical radiology, CT, MRI diagnosis. Corresponding author of this paper

  • 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.
  • 图  1  基于EEG的癫痫自动检测流程.

    Fig.  1  Flowchart of EEG-based automatic epilepsy detection.

    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]).

    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]).

    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]).

    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]2187颅内EEG256708
    CHB-MIT[65]22163头皮EEG256844
    Bonn[66]10100颅内EEG256708
    Kaggle[67]2 (人)48颅内EEG400627
    5 (狗)5 000
    Barcelona[68]53 750颅内EEG51283
    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
    Kiymik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
    Achary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[131]BonnApEnANNAcc: 98.27%
    Liang等[44]BonnApEn、频域特征LDA、SVM、ANNAcc: 97.82-98.51%
    Samiee等[50]Bonn时频域特征NB、LR、SVM、K近邻、ANNAcc: 98.3%
    Zhang等[132]Bonn频率切片小波变换SVMAcc: 98.33%
    Yan等[133]BonnSAESVMAcc: 100.0%
    Ahmed等[109]未公开数据时域、频域、非线性特征SVM、RBF-SVMSen: 82.6%, Spec: 90%
    Acharya等[56]BonnDCNNDCNNAcc: 88.67%
    Qiu等[134]BonnDSAELRAcc: 100.0%
    Yuan等[135]CHB-MITSAEPSVMAcc: 96.61%
    Ahmedt等[136]QUT、MAEUnitCNNSVMAcc: 95.19%
    Hussein等[137]Bonn时域、频域、时频域SoftmaxAcc: 100.0%
    Roy等[138]BonnCNN、RNNLR、MLPAcc: 82.04%
    Thomas等[139]MGHCNNSVMAcc: 83.86%
    Daoud等[140]Bonn非线性特征DCNN、MLPAcc: 98.6%
    Hu等[85]CHB-MITMAS+CNNSVMAcc: 86.25%
    Jaafar等[141]FreiburgLSTMSoftmaxAcc: 97.75%
    Chen等[142]BonnDWT+非线性特征SVMAcc: 99.5%
    Tian等[61]CHB-MIT时域、频域、时频域NB、DT、SVM、K近邻、TSK-FSAcc: 98.33%
    Cao等[143]CHB-MITCNNSVM、KNN、ELM、KELM、RFAcc: 99.33%
    Zhang等[144]TUHCNNRF、KNN、SVMAcc: 97.4%
    下载: 导出CSV
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