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

计量
- 文章访问数: 2855
- HTML全文浏览量: 2658
- PDF下载量: 891
- 被引次数: 0
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

参考文献(144)

[1]	Casson A J, Yates D C, Smith S J M, Duncan J S, Rodriguez-Villegas E. Wearable electroencephalography. IEEE Engineering in Medicine and Biology Magazine, 2010, 29(3): 44-56 doi: 10.1109/MEMB.2010.936545
[2]	Stevens J R, Lonsbury B L, Goel S L. Seizure occurrence and interspike interval: Telemetered electroencephalogram studies. Archives of neurology, 1972, 26(5): 409-419 doi: 10.1001/archneur.1972.00490110043004
[3]	Gastaut H, Magnus O, Caveness W F, Merlis J K, Landolt H, Pond D A, et al. A proposed international classification of epileptic seizures. Epilepsia, 1964, 5(4): 297-306 doi: 10.1111/j.1528-1157.1964.tb03337.x
[4]	Gandhi T, Panigrahi B K, Bhatia M, Anand S. Expert model for detection of epileptic activity in EEG signature. Expert Systems with Applications, 2010, 37(4): 3513-3520 doi: 10.1016/j.eswa.2009.10.036
[5]	Noachtar S, Rémi J. The role of EEG in epilepsy: A critical review. Epilepsy & Behavior, 2009, 15(1): 22-33.
[6]	Gotman J. Automatic recognition of epileptic seizures in the EEG. Electroencephalography and Clinical Neurophysiology, 1982, 54(5): 530-540 doi: 10.1016/0013-4694(82)90038-4
[7]	Geertsema E E, Visser G H, Velis D N, Claus S P, Zijlmans M, Kalitzin S N. Automated seizure onset zone approximation based on nonharmonic high-frequency oscillations in human interictal intracranial EEGs. International Journal of Neural Systems, 2015, 25(5): Article No. 1550015 doi: 10.1142/S012906571550015X
[8]	Khan Y U, Farooq O, Sharma P. Automatic detection of seizure onset in pediatric EEG. International Journal of Embedded Systems and Applications, 2012, 2(3): 81-89 doi: 10.5121/ijesa.2012.2309
[9]	Grewal S, Gotman J. An automatic warning system for epileptic seizures recorded on intracerebral EEGs. Clinical Neurophysiology, 2005, 116(10): 2460-2472 doi: 10.1016/j.clinph.2005.05.020
[10]	Kharbouch A, Shoeb A, Guttag J, Cash S S. An algorithm for seizure onset detection using intracranial EEG. Epilepsy & Behavior, 2011, 22 Suppl 1: S29-S35
[11]	Sorensen T L, Olsen U L, Conradsen I, Henriksen J, Kjaer T W, Thomsen C E, et al. Automatic epileptic seizure onset detection using Matching Pursuit: A case study. In: Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. Buenos Aires, Argentina: IEEE, 2010. 3277−3280
[12]	Worrell G A, Gardner A B, Stead S M, Hu S Q, Goerss S, Cascino G J, et al. High-frequency oscillations in human temporal lobe: Simultaneous microwire and clinical macroelectrode recordings. Brain, 2008, 131(4): 928-937 doi: 10.1093/brain/awn006
[13]	Jacobs J, LeVan P, Chander R, Hall J, Dubeau F, Gotman J. Interictal high-frequency oscillations (80-500Hz) are an indicator of seizure onset areas independent of spikes in the human epileptic brain. Epilepsia, 2008, 49(11): 1893-1907 doi: 10.1111/j.1528-1167.2008.01656.x
[14]	Chaibi S, Sakka Z, Lajnef T, Samet M, Kachouri A. Automated detection and classification of high frequency oscillations (HFOs) in human intracereberal EEG. Biomedical Signal Processing and Control, 2013, 8(6): 927-934 doi: 10.1016/j.bspc.2013.08.009
[15]	Spanedda F, Cendes F, Gotman J. Relations between EEG seizure morphology, interhemispheric spread, and mesial temporal atrophy in bitemporal epilepsy. Epilepsy, 1997, 38(12): 1300-1314 doi: 10.1111/j.1528-1157.1997.tb00068.x
[16]	Zhang J, Zou J Z, Wang M, Chen L L, Wang C M, Wang G S. Automatic detection of interictal epileptiform discharges based on time-series sequence merging method. Neurocomputing, 2013, 110: 35-43 doi: 10.1016/j.neucom.2012.11.017
[17]	Karacor D, Nazlibilek S, Sazli M H, Akarsu E S. Discrete Lissajous figures and applications. IEEE Transactions on Instrumentation and Measurement, 2014, 63(12): 2963-2972 doi: 10.1109/TIM.2014.2318891
[18]	孙玉宝, 吴敏, 韦志辉, 肖亮, 冯灿. 基于稀疏表示的脑电棘波检测算法研究. 电子学报, 2009, 37(9): 1971-1976 doi: 10.3321/j.issn:0372-2112.2009.09.017 Sun Yu-Bao, Wu Min, Wei Zhi-Hui, Xiao Liang, Feng Can. EEG spike detection using sparse representation. Acta Electronica Sinica, 2009, 37(9): 1971-1976 doi: 10.3321/j.issn:0372-2112.2009.09.017
[19]	Casson A J, Rodriguez-Villegas E. Toward online data reduction for portable electroencephalography systems in epilepsy. IEEE Transactions on Biomedical Engineering, 2009, 56(12): 2816-2825 doi: 10.1109/TBME.2009.2027607
[20]	Fürbass F, Hartmann M M, Halford J J, Koren J, Herta J, Gruber A, et al. Automatic detection of rhythmic and periodic patterns in critical care EEG based on American Clinical Neurophysiology Society (ACNS) standardized terminology. Neurophysiologie Clinique/Clinical Neurophysiology, 2015, 45(3): 203-213 doi: 10.1016/j.neucli.2015.08.001
[21]	Hussein A F, Arunkumar N, Gomes C, Alzubaidi A K, Habash Q A, Santamaria-Granados L, et al. Focal and non-focal epilepsy localization: A review. IEEE Access, 2018, 6: 49306-49324 doi: 10.1109/ACCESS.2018.2867078
[22]	柳长源, 张付浩, 韦琦. 基于脑电信号的癫痫疾病智能诊断与研究. 哈尔滨理工大学学报, 2018, 23(3): 91-98 Liu Chang-Yuan, Zhang Fu-Hao, Wei Qi. Intelligent diagnosis and research of epileptic diseases based on EEG signals. Journal of Harbin University of Science and Technology, 2018, 23(3): 91-98
[23]	李莹, 欧阳楷. 自动检测儿童脑电中癫痫波的方法研究. 中国生物医学工程学报, 2005, 24(5): 541-545 doi: 10.3969/j.issn.0258-8021.2005.05.006 Li Ying, Ouyang Kai. Study of automatic detection of epileptiform waves in children EEG. Chinese Journal of Biomedical Engineering, 2005, 24(5): 541-545 doi: 10.3969/j.issn.0258-8021.2005.05.006
[24]	Shoeb A, Guttag J. Application of machine learning to epileptic seizure detection. In: Proceedings of the 27th International Conference on Machine Learning. Haifa, Israel, 2010.
[25]	Oikonomou V P, Tzallas A T, Fotiadis D I. A Kalman filter based methodology for EEG spike enhancement. Computer Methods and Programs in Biomedicine, 2007, 85(2): 101-108 doi: 10.1016/j.cmpb.2006.10.003
[26]	Ille N, Berg P, Scherg M. Artifact correction of the ongoing EEG using spatial filters based on artifact and brain signal topographies. Journal of Clinical Neurophysiology, 2002, 19(2): 113-124 doi: 10.1097/00004691-200203000-00002
[27]	Delorme A, Sejnowski T, Makeig S. Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. NeuroImage, 2007, 34(4): 1443-1449 doi: 10.1016/j.neuroimage.2006.11.004
[28]	Kevric J, Subasi A. The effect of multiscale PCA de-noising in epileptic seizure detection. Journal of Medical Systems, 2014, 38(10): Article No. 131 doi: 10.1007/s10916-014-0131-0
[29]	Chandaka S, Chatterjee A, Munshi S. Cross-correlation aided support vector machine classifier for classification of EEG signals. Expert Systems with Applications, 2009, 36(2): 1329-1336 doi: 10.1016/j.eswa.2007.11.017
[30]	Altunay S, Telatar Z, Erogul O. Epileptic EEG detection using the linear prediction error energy. Expert Systems with Applications, 2010, 37(8): 5661-5665 doi: 10.1016/j.eswa.2010.02.045
[31]	Joshi V, Pachori R B, Vijesh A. Classification of ictal and seizure-free EEG signals using fractional linear prediction. Biomedical Signal Processing and Control, 2014, 9: 1-5 doi: 10.1016/j.bspc.2013.08.006
[32]	Alkan A, Koklukaya E, Subasi A. Automatic seizure detection in EEG using logistic regression and artificial neural network. Journal of Neuroscience Methods, 2005, 148(2): 167-176 doi: 10.1016/j.jneumeth.2005.04.009
[33]	Subasi A, Erçelebi E, Alkan A, Koklukaya E. Comparison of subspace-based methods with AR parametric methods in epileptic seizure detection. Computers in Biology and Medicine, 2006, 36(2): 195-208 doi: 10.1016/j.compbiomed.2004.11.001
[34]	Naghsh-Nilchi A R, Aghashahi M. Epilepsy seizure detection using Eigen-system spectral estimation and Multiple Layer Perceptron neural network. Biomedical Signal Processing and Control, 2010, 5(2): 147-157 doi: 10.1016/j.bspc.2010.01.004
[35]	Kımathymımathk M K, Güler İ, Dizibüyük A, Akımathn M. Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application. Computers in Biology and Medicine, 2005, 35(7): 603-616 doi: 10.1016/j.compbiomed.2004.05.001
[36]	Sadati N, Mohseni H R, Maghsoudi A. Epileptic seizure detection using neural fuzzy networks. In: Proceedings of the 2006 IEEE International Conference on Fuzzy Systems. Vancouver, Canada: IEEE, 2006. 596−600
[37]	Li S F, Zhou W D, Yuan Q, Geng S J, Cai D M. Feature extraction and recognition of ictal EEG using EMD and SVM. Computers in Biology and Medicine, 2013, 43(7): 807-816 doi: 10.1016/j.compbiomed.2013.04.002
[38]	Richman J S, Moorman J R. Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 2000, 278(6): H2039-H2049 doi: 10.1152/ajpheart.2000.278.6.H2039
[39]	Conigliaro D, Manganotti P, Menegaz G. Multiscale sample entropy for time resolved epileptic seizure detection and fingerprinting. In: Proceedings of the 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Florence, Italy: IEEE, 2014. 3582−3585
[40]	Nicolaou N, Georgiou J. Detection of epileptic electroencephalogram based on Permutation Entropy and Support Vector Machines. Expert Systems with Applications, 2012, 39(1): 202-209 doi: 10.1016/j.eswa.2011.07.008
[41]	Hurst H E. Methods of using long-term storage in reservoirs. Hydrological Sciences Journal, 1956, 1(3): 13-27
[42]	Acharya U R, Sree S V, Suri J S. Automatic detection of epileptic EEG signals using higher order cumulant features. International Journal of Neural Systems, 2011, 21(5): 403-414 doi: 10.1142/S0129065711002912
[43]	尧德中. 基于四阶累积量矩阵子空间分解的脑电逆问题算法. 生物医学工程学杂志, 2000, 17(2): 174-178 doi: 10.3321/j.issn:1001-5515.2000.02.015 Yao De-Zhong. Electroencephalography inverse problem by subspace decomposition of the fourth-order cumulant matrix. Journal of Biomedical Engineering, 2000, 17(2): 174-178 doi: 10.3321/j.issn:1001-5515.2000.02.015
[44]	Liang S F, Wang H C, Chang W L. Combination of EEG complexity and spectral analysis for epilepsy diagnosis and seizure detection. EURASIP Journal on Advances in Signal Processing, 2010, 2010(1): Article No. 853434 doi: 10.1155/2010/853434
[45]	Kuhlmann L, Cook M J, Fuller K, Grayden D B, Burkitt A N, Mareels I M Y. Correlation analysis of seizure detection features. In: Proceedings of the 2008 International Conference on Intelligent Sensors, Sensor Networks and Information Processing. Sydney, Australia: IEEE, 2008. 309−314
[46]	Ong P, Zainuddin Z, Lai K H. A novel selection of optimal statistical features in the DWPT domain for discrimination of ictal and seizure-free electroencephalography signals. Pattern Analysis and Applications, 2018, 21(2): 515-527 doi: 10.1007/s10044-017-0642-7
[47]	宦飞, 王志中, 郑崇勋. 基于时频分析检测EEG中癫痫样棘/尖波的方法. 生物物理学报, 2000, 16(3): 539-546 doi: 10.3321/j.issn:1000-6737.2000.03.016 Huan Fei, Wang Zhi-Zhong, Zheng Chong-Xun. Automatic detection of epileptiform spikes in EEG based on time-frequency analysis. Acta Biophysica Sinica, 2000, 16(3): 539-546 doi: 10.3321/j.issn:1000-6737.2000.03.016
[48]	Yadav R, Shah A K, Loeb J A, Swamy M N S, Agarwal R. Morphology-Based automatic seizure detector for intracerebral EEG recordings. IEEE Transactions on Biomedical Engineering, 2012, 59(7): 1871-1881 doi: 10.1109/TBME.2012.2190601
[49]	宦飞, 郑崇勋, 黄远桂. 基于时频分布的EEG中棘波的相关检测. 仪器仪表学报, 1999, 20(5): 446-450 doi: 10.3321/j.issn:0254-3087.1999.05.002 Huan Fei, Zheng Chong-Xun, Huang Yuan-Gui. Correlation detection of spikes in EEG based on time frequency distribution. Chinese Journal of Scientific Instrument, 1999, 20(5): 446-450 doi: 10.3321/j.issn:0254-3087.1999.05.002
[50]	Samiee K, Kovács P Gabbouj M. Epileptic seizure classification of EEG time-series using rational discrete short-time Fourier transform. IEEE Transactions on Biomedical Engineering, 2015, 62(2): 541-552 doi: 10.1109/TBME.2014.2360101
[51]	Qaraqe M, Ismail M, Serpedin E, Zulfi H. Epileptic seizure onset detection based on EEG and ECG data fusion, Epilepsy & Behavior, 2016, 58: 48-60
[52]	Jaiswal A K, Banka H. Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals. Biomedical Signal Processing and Control, 2017, 34: 81-92 doi: 10.1016/j.bspc.2017.01.005
[53]	Boashash B, Ouelha S. Automatic signal abnormality detection using time-frequency features and machine learning: A newborn EEG seizure case study. Knowledge-Based Systems, 2016, 106: 38-50 doi: 10.1016/j.knosys.2016.05.027
[54]	Alam S M S, Bhuiyan M I H. Detection of seizure and epilepsy using higher order statistics in the EMD domain. IEEE Journal of Biomedical and Health Informatics, 2013, 17(2): 312-318 doi: 10.1109/JBHI.2012.2237409
[55]	Wei Z C, Zou J Z, Zhang J, Xu J Q. Automatic epileptic EEG detection using convolutional neural network with improvements in time-domain. Biomedical Signal Processing and Control, 2019, 53: Article No. 101551 doi: 10.1016/j.bspc.2019.04.028
[56]	Acharya U R, Oh S L, Hagiwara Y, Tan J H, Adeli H. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Computers in Biology and Medicine, 2018, 100: 270-278 doi: 10.1016/j.compbiomed.2017.09.017
[57]	Bizopoulos P, Lambrou G I, Koutsouris D. Signal2Image modules in deep neural networks for EEG classification. In: Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Berlin, Germany: IEEE, 2019. 702−705
[58]	Kiral-Kornek I, Roy S, Nurse E, Mashford B, Karoly P, Carroll T, et al. Epileptic seizure prediction using big data and deep learning: Toward a mobile system. EBioMedicine, 2018, 27: 103-111 doi: 10.1016/j.ebiom.2017.11.032
[59]	杨昌健, 邓赵红, 蒋亦樟, 王士同. 基于迁移学习的癫痫EEG信号自适应识别. 计算机科学与探索, 2014, 8(3): 329-337 doi: 10.3778/j.issn.1673-9418.1306005 Yang Chang-Jian, Deng Zhao-Hong, Jiang Yi-Zhang, Wang Shi-Tong. Adaptive recognition of epileptic EEG signals based on transfer learning. Journal of Frontiers of Computer Science and Technology, 2014, 8(3): 329-337 doi: 10.3778/j.issn.1673-9418.1306005
[60]	Zhang B C, Wang W N, Xiao Y T, Xiao S X, Chen S C, Chen S R, et al. Cross-subject seizure detection in EEGs using deep transfer learning. Computational and Mathematical Methods in Medicine, 2020, 2020: Article No. 7902072
[61]	Tian X B, Deng Z H, Ying W H, Choi K S, Wu D R, Qin B, et al. Deep multi-view feature learning for EEG-based epileptic seizure detection. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019, 27(10): 1962-1972 doi: 10.1109/TNSRE.2019.2940485
[62]	Abualsaud K, Mahmuddin M, Saleh M, Mohamed A. Ensemble classifier for epileptic seizure detection for imperfect EEG data. The Scientific World Journal, 2015, 2015: Article No. 945689.
[63]	Chen X H, Ji J L, Ji T X, Li P. Cost-sensitive deep active learning for epileptic seizure detection. In: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. Washington, USA: ACM, 2018. 226−235
[64]	Ihle M, Feldwisch-Drentrup H, Teixeira C A, Witon A, Schelter B, Timmer J, et al. EPILEPSIAE-A European epilepsy database. Computer Methods and Programs in Biomedicine, 2012, 106(3): 127-138 doi: 10.1016/j.cmpb.2010.08.011
[65]	Shoeb A H. Application of Machine Learning to Epileptic Seizure Onset Detection and Treatment [Ph. D. dissertation], Harvard University, USA, 2009
[66]	Andrzejak R G, Lehnertz K, Mormann F, Rieke C, David P, Elger C E. Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E, 2001, 64(1): Article No. 061907
[67]	American epilepsy society seizure prediction challenge [Online], available: https://www.kaggle.com/c/seizure-prediction/, November 17, 2014
[68]	Andrzejak R G, Schindler K, Rummel C. Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. Physical Review E, 2012, 86(4): Article No. 046206
[69]	Katz M J. Fractals and the analysis of waveforms. Computers in Biology and Medicine, 1988, 18(3): 145-156 doi: 10.1016/0010-4825(88)90041-8
[70]	Esteller R, Echauz J, Tcheng T, Litt B, Pless B. Line length: An efficient feature for seizure onset detection. In: Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Istanbul, Turkey: IEEE, 2001. 1707−1710
[71]	D’Alessandro M, Esteller R, Vachtsevanos G, Hinson A, Echauz J, Litt B. Epileptic seizure prediction using hybrid feature selection over multiple intracranial EEG electrode contacts: A report of four patients. IEEE Transactions on Biomedical Engineering, 2003, 50(5): 603-615 doi: 10.1109/TBME.2003.810706
[72]	van Putten M J A M, Kind T, Visser F, Lagerburg V. Detecting temporal lobe seizures from scalp EEG recordings: A comparison of various features. Clinical Neurophysiology, 2005, 116(10): 2480-2489 doi: 10.1016/j.clinph.2005.06.017
[73]	Hjorth B. EEG analysis based on time domain properties. Electroencephalography and Clinical Neurophysiology, 1970, 29(3): 306-310 doi: 10.1016/0013-4694(70)90143-4
[74]	陈龙, 王崴, 瞿珏, 胡波, 蔡睿, 赵敏睿. EEG功率信息显示特征对认知负荷的影响. 中国安全科学学报, 2020, 30(2): 183-189 Chen Long, Wang Wei, Qu Jue, Hu Bo, Cai Rui, Zhao Min-Rui. Influence of display characteristics of EEG power on cognitive load. China Safety Science Journal, 2020, 30(2): 183-189
[75]	Hernández D E, Trujillo L, Z-Flores E, Villanueva O M, Romo-Fewell O. Detecting epilepsy in EEG signals using time, frequency and time-frequency domain features. Computer Science and Engineering-Theory and Applications. Cham: Springer, 2018. 167−182
[76]	Gotman J, Flanagan D, Zhang J, Rosenblatt B. Automatic seizure detection in the newborn: Methods and initial evaluation. Electroencephalography and Clinical Neurophysiology, 1997, 103(3): 356-362 doi: 10.1016/S0013-4694(97)00003-9
[77]	Zibrandtsen I C, Weisdorf S, Ballegaard M, Beniczky S, Kjaer T W. Postictal EEG changes following focal seizures: Interrater agreement and comparison to frequency analysis. Clinical Neurophysiology, 2019, 130(6): 879-885 doi: 10.1016/j.clinph.2019.03.001
[78]	Halliwell M. Doppler ultrasound: Physics, instrumentation and signal processing (Second Edition). Physiological Measurement, 2000, 21(3): 425-426
[79]	Faul S, Boylan G, Connolly S, Marnane L, Lightbody G. An evaluation of automated neonatal seizure detection methods. Clinical Neurophysiology, 2005, 116(7): 1533-1541 doi: 10.1016/j.clinph.2005.03.006
[80]	Jahankhani P, Kodogiannis V, Revett K. EEG signal classification using wavelet feature extraction and neural networks. In: Proceedings of the IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing (JVA＇06). Sofia, Bulgaria: IEEE, 2006. 120−124
[81]	Kumar Y, Dewal M L, Anand R S. Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine. Neurocomputing, 2014, 133(10): 271-279
[82]	Tafreshi A K, Nasrabadi A M, Omidvarnia A H. Epileptic seizure detection using empirical mode decomposition. In: Proceedings of the 2008 IEEE International Symposium on Signal Processing and Information Technology. Sarajevo, Bosnia and Herzegovina: IEEE, 2008. 238−242
[83]	Orosco L, Laciar E, Correa A G, Torres A, Graffigna J P. An epileptic seizures detection algorithm based on the empirical mode decomposition of EEG. In: Proceedings of the 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Minneapolis, USA: IEEE, 2009. 2651−2654
[84]	Pachori R B. Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition. Research Letters in Signal Processing, 2008, 2008: Article No. 293056
[85]	Hu W B, Cao J W, Lai X P, Liu J B. Mean amplitude spectrum based epileptic state classification for seizure prediction using convolutional neural networks. Journal of Ambient Intelligence and Humanized Computing, DOI: 10.1007/s12652-019-01220-6
[86]	Pachori R B, Patidar S. Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions. Computer Methods and Programs in Biomedicine, 2014, 113(2): 494-502 doi: 10.1016/j.cmpb.2013.11.014
[87]	Bajaj V, Pachori R B. EEG signal classification using empirical mode decomposition and support vector machine. Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011), December 20−22, 2011. New Delhi: Springer, 2012. 623−635
[88]	Guarnizo C, Delgado E. EEG single-channel seizure recognition using empirical mode decomposition and normalized mutual information. In: Proceedings of the 10th International Conference on Signal Processing Proceedings. Beijing, China: IEEE, 2010. 1−4
[89]	Fu K, Qu J F, Chai Y, Dong Y. Classification of seizure based on the time-frequency image of EEG signals using HHT and SVM. Biomedical Signal Processing and Control, 2014, 13: 15-22 doi: 10.1016/j.bspc.2014.03.007
[90]	Lehnertz K, Elger C E. Can epileptic seizures be predicted? Evidence from nonlinear time series analysis of brain electrical activity. Physical Review Letters, 1998, 80(22): 5019-5022 doi: 10.1103/PhysRevLett.80.5019
[91]	Mirzaei A, Ayatollahi A, Gifani P, Salehi L. Spectral entropy for epileptic seizures detection. In: Proceedings of the 2nd International Conference on Computational Intelligence, Communication Systems and Networks. Liverpool, UK: IEEE, 2020. 301−307
[92]	Gao J B, Hu J, Tung W W. Entropy measures for biological signal analyses. Nonlinear Dynamics, 2012, 68(3): 431-444 doi: 10.1007/s11071-011-0281-2
[93]	Song Y D, Liò P. A new approach for epileptic seizure detection: Sample entropy based feature extraction and extreme learning machine. Journal of Biomedical Science and Engineering, 2010, 3(6): 556-567 doi: 10.4236/jbise.2010.36078
[94]	Wang S Y, Chaovalitwongse W A, Wong S. Online seizure prediction using an adaptive learning approach. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(12): 2854-2866 doi: 10.1109/TKDE.2013.151
[95]	Aarabi A, He B. A rule-based seizure prediction method for focal neocortical epilepsy. Clinical Neurophysiology, 2012, 123(6): 1111-1122 doi: 10.1016/j.clinph.2012.01.014
[96]	Kalatzis I, Piliouras N, Ventouras E, Papageorgiou C C, Rabavilas A D, Cavouras D. Design and implementation of an SVM-based computer classification system for discriminating depressive patients from healthy controls using the p600 component of ERP signals. Computer Methods and Programs in Biomedicine, 2004, 75(1): 11-22 doi: 10.1016/j.cmpb.2003.09.003
[97]	Park Y, Luo L, Parhi K K, Netoff T. Seizure prediction with spectral power of EEG using cost-sensitive support vector machines. Epilepsia, 2011, 52(10): 1761-1770 doi: 10.1111/j.1528-1167.2011.03138.x
[98]	Al Ghayab H R, Li Y, Siuly S, Abdulla S. A feature extraction technique based on tunable Q-factor wavelet transform for brain signal classification. Journal of Neuroscience Methods, 2019, 312: 43-52 doi: 10.1016/j.jneumeth.2018.11.014
[99]	Oweis R J, Abdulhay E W. Seizure classification in EEG signals utilizing Hilbert-Huang transform. BioMedical Engineering OnLine, 2011, 10(1): Article No. 38 doi: 10.1186/1475-925X-10-38
[100]	Acharya U R, Hagiwara Y, Koh J E W, Oh S L, Tan J H, Adam M, Tan R S. Entropies for automated detection of coronary artery disease using ECG signals: A review. Biocybernetics and Biomedical Engineering, 2018, 38(2): 373-384 doi: 10.1016/j.bbe.2018.03.001
[101]	Tian Y, Zhang H L, Xu W, Zhang H Y, Yang L, Zheng S X, Shi Y P. Spectral entropy can predict changes of working memory performance reduced by short-time training in the delayed-match-to-sample task. Frontiers in Human Neuroscience, 2017, 11: Article No. 437 doi: 10.3389/fnhum.2017.00437
[102]	Azami H, Fernández A, Escudero J. Refined multiscale fuzzy entropy based on standard deviation for biomedical signal analysis. Medical & Biological Engineering & Computing, 2017, 55(11): 2037-2052
[103]	Shayegh F, Sadri S, Amirfattahi R, Ansari-Asl K. A model-based method for computation of correlation dimension, Lyapunov exponents and synchronization from depth-EEG signals. Computer Methods and Programs in Biomedicine, 2014, 113(1): 323-337 doi: 10.1016/j.cmpb.2013.08.014
[104]	Mirowski P, Madhavan D, LeCun Y, Kuzniecky R. Classification of patterns of EEG synchronization for seizure prediction. Clinical Neurophysiology, 2009, 120(11): 1927-1940 doi: 10.1016/j.clinph.2009.09.002
[105]	Faul S, Boylan G, Connolly S, Marnane W, Lightbody G. Chaos theory analysis of the newborn EEG — Is it worth the wait? In: Proceedings of the 2005 IEEE International Workshop on Intelligent Signal Processing. Faro, Portugal: IEEE, 2005. 381−386
[106]	李小兵, 初孟, 邱天爽, 鲍海平. 一种基于时频分析的癫痫脑电棘波检测方法. 中国生物医学工程学报, 2006, 25(6): 678-682 doi: 10.3969/j.issn.0258-8021.2006.06.008 Li Xiao-Bing, Chu Meng, Qiu Tian-Shuang, Bao Hai-Ping. A method of epileptic EEG spike detection based on time-frequency analysis. Chinese Journal of Biomedical Engineering, 2006, 25(6): 678-682 doi: 10.3969/j.issn.0258-8021.2006.06.008
[107]	Yadav R, Agarwal R, Swamy M N S. A novel morphology-based classifier for automatic detection of epileptic seizures. In: Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. Buenos Aires, Argentina: IEEE, 2010. 5545−5548
[108]	Liu Y X, Zhou W D, Yuan Q, Chen S S. Automatic seizure detection using wavelet transform and SVM in long-term intracranial EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2012, 20(6): 749-755 doi: 10.1109/TNSRE.2012.2206054
[109]	Ahmed R, Temko A, Marnane W P, Boylan G, Lightbody G. Exploring temporal information in neonatal seizures using a dynamic time warping based SVM kernel. Computers in Biology and Medicine, 2017, 82: 100-110 doi: 10.1016/j.compbiomed.2017.01.017
[110]	Li M Y, Chen W Z, Zhang T. Automatic epilepsy detection using wavelet-based nonlinear analysis and optimized SVM. Biocybernetics and Biomedical Engineering, 2016, 36(4): 708-718 doi: 10.1016/j.bbe.2016.07.004
[111]	Reddy G R S, Rao R. Automated identification system for seizure EEG signals using tunable-Q wavelet transform. Engineering Science and Technology, An International Journal, 2017, 20(5): 1486-1493 doi: 10.1016/j.jestch.2017.11.003
[112]	Avcu M T, Zhang Z, Chan D W S. Seizure detection using least EEG channels by deep convolutional neural network. In: Proceedings of the 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Brighton, UK: IEEE, 2019. 1120−1124
[113]	Covert I C, Krishnan B, Najm I, Zhan J, Shore M, Hixson J, et al. Temporal graph convolutional networks for automatic seizure detection. In: Proceedings of Machine Learning Research. Ann Arbor, Michigan, USA, 2019.
[114]	Li Y, Liu Y, Cui W G, Guo Y Z, Huang H, Hu Z Y. Epileptic seizure detection in EEG signals using a unified temporal-spectral squeeze-and-excitation network. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020, 28(4): 782-794 doi: 10.1109/TNSRE.2020.2973434
[115]	Thodoroff P, Pineau J, Lim A. Learning robust features using deep learning for automatic seizure detection. In: Proceedings of the 1st Machine Learning for Healthcare Conference. Los Angeles, CA, USA: PMLR, 2016.
[116]	Craley J, Johnson E, Venkataraman A. Integrating convolutional neural networks and probabilistic graphical modeling for epileptic seizure detection in multichannel EEG. In: Proceedings of the 26th International Conference on Information Processing in Medical Imaging. Hong Kong, China: Springer, 2019. 291−303
[117]	Roy S, Kiral-Kornek I, Harrer S. ChronoNet: A deep recurrent neural network for abnormal EEG identification. In: Proceedings of the 17th Conference on Artificial Intelligence in Medicine in Europe. Poznan, Poland: Springer, 2019. 47−56
[118]	Sharathappriyaa V, Gautham S, Lavanya R. Auto-encoder based automated epilepsy diagnosis. In: Proceedings of the 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI). Bangalore, India: IEEE, 2018. 976−982
[119]	Rajaguru H, Prabhakar S K. Multilayer autoencoders and EM-PCA with genetic algorithm for epilepsy classification from EEG. In: Proceedings of the 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA). Coimbatore, India: IEEE, 2018. 353−358
[120]	Pan S J, Yang Q. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345-1359 doi: 10.1109/TKDE.2009.191
[121]	Jiang Y Z, Wu D R, Deng Z H, Qian P J, Wang J, Wang G J, et al. Seizure classification from EEG signals using transfer learning, semi-supervised learning and TSK fuzzy system. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25(12): 2270-2284 doi: 10.1109/TNSRE.2017.2748388
[122]	Zhu Y D, Saqib M, Ham E, Belhareth S, Hoffman R, Wang M D. Mitigating patient-to-patient variation in EEG seizure detection using meta transfer learning. In: Proceedings of the 20th International Conference on Bioinformatics and Bioengineering (BIBE). Cincinnati, USA: IEEE, 2020. 548−555
[123]	Zhao J, Xie X J, Xu X, Sun S L. Multi-view learning overview: Recent progress and new challenges. Information Fusion, 2017, 38: 43-54 doi: 10.1016/j.inffus.2017.02.007
[124]	Yuan Y, Xun G X, Jia K B, Zhang A D. A multi-view deep learning framework for EEG seizure detection. IEEE Journal of Biomedical and Health Informatics, 2019, 23(1): 83-94 doi: 10.1109/JBHI.2018.2871678
[125]	Liu Y, Li Y. A multi-view unified feature learning network for EEG epileptic seizure detection. In: Proceedings of the 2019 IEEE Symposium Series on Computational Intelligence. Xiamen, China: IEEE, 2019. 2608−2612
[126]	Zhou Z H. Ensemble Methods: Foundations and Algorithms. Boca Raton: Chapman & Hall/CRC, 2012.
[127]	Hosseini M P, Hajisami A, Pompili D. Real-time epileptic seizure detection from EEG signals via random subspace ensemble learning. In Proceedings of the 2016 IEEE International Conference on Autonomic Computing (ICAC). Wuerzburg, Germany: IEEE, 2016. 209−218
[128]	Akyol K. Stacking ensemble based deep neural networks modeling for effective epileptic seizure detection. Expert Systems with Applications, 2020, 148: Article No. 113239 doi: 10.1016/j.eswa.2020.113239
[129]	Ramachandran V R K, Alblas H J, Le D V, Meratnia N. Towards an online seizure advisory system—an adaptive seizure prediction framework using active learning heuristics. Sensors, 2018, 18(6): Article No. 1698 doi: 10.3390/s18061698
[130]	Guo L, Rivero D, Pazos A. Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. Journal of Neuroscience Methods, 2010, 193(1): 156-163 doi: 10.1016/j.jneumeth.2010.08.030
[131]	张涛, 陈万忠, 李明阳. 基于频率切片小波变换和支持向量机的癫痫脑电信号自动检测. 物理学报, 2016, 65(3): Article No. 038703 Zhang Tao, Chen Wan-Zhong, Li Ming-Yang. Automatic seizure detection of electroencephalogram signals based on frequency slice wavelet transform and support vector machine. Acta Physica Sinica, 2016, 65(3): Article No. 038703
[132]	Yan B, Wang Y, Li Y H, Gong Y J, Guan L, Yu S. An EEG signal classification method based on sparse auto-encoders and support vector machine. In: Proceedings of the 2016 IEEE/CIC International Conference on Communications in China (ICCC). Chengdu, China: IEEE, 2016. 1−6
[133]	Qiu Y, Zhou W D, Yu N N, Du P D. Denoising sparse autoencoder-based ictal EEG classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26(9): 1717-1726
[134]	Yuan Y, Xun G X, Ma F L, Suo Q L, Xue H F, Jia K B, et al. A novel channel-aware attention framework for multi-channel EEG seizure detection via multi-view deep learning. In: Proceedings of the 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). Las Vegas, USA: IEEE, 2018. 206−209
[135]	Ahmedt-Aristizabal D, Fookes C, Nguyen K, Denman S, Sridharan S, Dionisio S. Deep facial analysis: A new phase I epilepsy evaluation using computer vision. Epilepsy & Behavior, 2018, 82: 17-24
[136]	Hussein R, Palangi H, Wang Z J, Ward R. Robust detection of epileptic seizures using deep neural networks. In: Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Calgary, Canada: IEEE, 2018. 2546−2550
[137]	Roy S, Kiral-Kornek I, Harrer S. Deep learning enabled automatic abnormal EEG identification. In: Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Honolulu, USA: IEEE, 2018. 2756−2759
[138]	Thomas J, Comoretto L, Jin J, Dauwels J, Cash S S, Westover M B. EEG classification via convolutional neural network-based interictal epileptiform event detection. In: Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Honolulu, USA: IEEE, 2018. 3148−3151
[139]	Daoud H G, Abdelhameed A M, Bayoumi M. Automatic epileptic seizure detection based on empirical mode decomposition and deep neural network. In: Proceedings of the 14th International Colloquium on Signal Processing & Its Applications (CSPA). Penang, Malaysia: IEEE, 2018. 182−186
[140]	Jaafar S T, Mohammadi M. Epileptic seizure detection using deep learning approach. UHD Journal of Science and Technology, 2019, 3(2): 41-50 doi: 10.21928/uhdjst.v3n2y2019.pp41-50
[141]	Chen S N, Zhang X, Chen L L, Yang Z X. Automatic diagnosis of epileptic seizure in electroencephalography signals using nonlinear dynamics features. IEEE Access, 2019, 7: 61046-61056 doi: 10.1109/ACCESS.2019.2915610
[142]	Cao J W, Zhu J H, Hu W B, Kummert A. Epileptic signal classification with deep EEG features by stacked CNNs. IEEE Transactions on Cognitive and Developmental Systems, 2020, 12(4): 709-722 doi: 10.1109/TCDS.2019.2936441
[143]	Zhang X, Yao L N, Dong M Q, Liu Z, Zhang Y, Li Y. Adversarial representation learning for robust patient-independent epileptic seizure detection. IEEE Journal of Biomedical and Health Informatics, 2020, 24(10): 2852-2859 doi: 10.1109/JBHI.2020.2971610
[144]	Goldenholz D M, Kuhn A, Austermuehle A, Bachler M, Mayer C, Wassertheurer S, et al. Long-term monitoring of cardiorespiratory patterns in drug-resistant epilepsy. Epilepsia, 2017, 58(1): 77-84 doi: 10.1111/epi.13606