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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.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.
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Key words:
- Epilepsy /
- EEG /
- feature extraction /
- classification
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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
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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 %
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