Prediction of epilepsy based on common spatial model algorithm and support vector machine double classification_Journal of Biomedical Engineering

Authors：

WANG Yuxiao ,  JIANG Wei , LIU Zhi , BAO Chengxiao

School of Information Science and Engineering, Shandong University, Qingdao, Shandong 266237, P.R.China;

Corresponding author：

JIANG Wei, Email: jwimage@sdu.edu.cn

Keywords：

epilepsy electrical signal; common spatial model; feature combination; support vector machine; double classification

DOI：

10.7507/1001-5515.201911042

Video：

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Abstract Full text Figures/Tables Video References Cited by

At present the prediction method of epilepsy patients is very time-consuming and vulnerable to subjective factors, so this paper presented an automatic recognition method of epilepsy electroencephalogram (EEG) based on common spatial model (CSP) and support vector machine (SVM). In this method, the CSP algorithm for extracting spatial characteristics was applied to the detection of epileptic EEG signals. However, the algorithm did not consider the nonlinear dynamic characteristics of the signals and ignored the time-frequency information, so the complementary characteristics of standard deviation, entropy and wavelet packet energy were selected for the combination in the feature extraction stage. The classification process adopted a new double classification model based on SVM. First, the normal, interictal and ictal periods were divided into normal and paroxysmal periods (including interictal and ictal periods), and then the samples belonging to the paroxysmal periods were classified into interictal and ictal periods. Finally, three categories of recognition were realized. The experimental data came from the epilepsy study at the University of Bonn in Germany. The average recognition rate was 98.73% in the first category and 99.90% in the second category. The experimental results show that the introduction of spatial characteristics and double classification model can effectively solve the problem of low recognition rate between interictal and ictal periods in many literatures, and improve the identification efficiency of each period, so it provides an effective detecting means for the prediction of epilepsy.

Citation： WANG Yuxiao, JIANG Wei, LIU Zhi, BAO Chengxiao. Prediction of epilepsy based on common spatial model algorithm and support vector machine double classification. Journal of Biomedical Engineering, 2021, 38(1): 39-46. doi: 10.7507/1001-5515.201911042 Copy

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3.	唐颖莹, 陆璐, 周东. 中国癫痫诊断治疗现状. 癫痫杂志, 2019, 5(3): 161-164.
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7.	Kıymık M K, Güler İ, Dizibüyük A, et al. Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application. Comput Biol Med, 2005, 35(7): 603-616.
8.	Quintero-Rincón A, Marcelo P, Carlos D G, et al. Fast statistical model-based classification of epileptic EEG signals. Biocyber Biomed Eng, 2018, 38(4): 877-889.
9.	Acharya U R, Yanti R, Zheng J W, et al. Automated diagnosis of epilepsy using CWT, HOS and texture parameters. Int J Neural Syst, 2013, 23(03): 1350009.
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14.	张健钊, 姜威, 元辉, 等. 基于离散S变换和排列熵的癫痫脑电识别. 生物医学工程学杂志, 2017, 34(5): 681-687.
15.	曹春雨, 韩凌, 李春胜. 癫痫发作脑电信号的相位幅度调制研究. 中国生物医学工程学报, 2018, 37(1): 33-39.
16.	Kumar Y, Dewal M L, Anand R S. Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network. Signal Image Video Process, 2014, 8(7): 1323-1334.
17.	余娴, 刘程程, 戴加飞, 等. 基于改进的k-最近邻网络的癫痫脑电信号分析. 生物医学工程学杂志, 2016, 33(6): 1039-1045.
18.	张健钊, 姜威, 贲晛烨. 基于样本熵与小波包能量特征提取和Real AdaBoost算法的正常期、癫痫间歇与发作期的脑电自动检测. 生物医学工程学杂志, 2016, 33(6): 1031-1038.
19.	Andrzejak R G, Lehnertz K, Mormann F, et al. Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Phys Rev E, 2001, 64(6): 061907.
20.	Koles Z J. The quantitative extraction and topographic mapping of the abnormal components in the clinical EEG. Electroencephalogr Clin Neurophysiol, 1991, 79(6): 440-447.
21.	Bandt C, Pompe B. Permutation entropy: A natural complexity measure for time series. Phys Rev Lett, 2002, 88(17): 174102.
22.	Song Yuedong, Crowcroft J, Zhang Jiaxiang. Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine. J Neurosci Meth, 2012, 210(2): 132-146.
23.	Lei Meng, Zhang Li, Li Ming, et al. Near-infrared spectrum of coal origin identification based on SVM algorithm//第37届中国控制会议论文集. 武汉: 中国自动化学会控制理论专业委员会, 2018: 444-448.

1. 杨博文, 陈欣, 孙皓, 等. 癫痫发病危险因素的Meta分析. 中国循证医学杂志, 2014, 14(1): 94-100.
2. 樊丹旸, 苗婕, 侯玉立, 等. 抗癫痫药物对成人骨密度和骨代谢影响的meta分析. 中国循证医学杂志, 2019, 19(2): 189-198.
3. 唐颖莹, 陆璐, 周东. 中国癫痫诊断治疗现状. 癫痫杂志, 2019, 5(3): 161-164.
4. 张瑞, 宋江玲, 胡文凤. 癫痫脑电的特征提取方法综述. 西北大学学报, 2016, 46(6): 782-794.
5. Gotman J. Automatic recognition of epileptic seizures in the EEG. Electroencephalogr Clin Neurophysiol, 1982, 54(5): 530-540.
6. Naghsh-Nilchi A R, Aghashahi M. Epilepsy seizure detection using eigen-system spectral estimation and Multiple Layer Perceptron neural network. Biomed Signal Process Control, 2010, 5(2): 147-157.
7. Kıymık M K, Güler İ, Dizibüyük A, et al. Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application. Comput Biol Med, 2005, 35(7): 603-616.
8. Quintero-Rincón A, Marcelo P, Carlos D G, et al. Fast statistical model-based classification of epileptic EEG signals. Biocyber Biomed Eng, 2018, 38(4): 877-889.
9. Acharya U R, Yanti R, Zheng J W, et al. Automated diagnosis of epilepsy using CWT, HOS and texture parameters. Int J Neural Syst, 2013, 23(03): 1350009.
10. Kumar Y, Dewal M L, Anand R S. Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine. Neurocomputing, 2014, 133: 271-279.
11. Li Shufang, Zhou Weidong, Yuan Qi, et al. Feature extraction and recognition of ictal EEG using EMD and SVM. Comput Biol Med, 2013, 43(7): 807-816.
12. Gao Xiaozeng, Yan Xiaoyan, Gao Ping, et al. Automatic detection of epileptic seizure based on approximate entropy, recurrence quantiﬁcation analysis and convolutional neural networks. Artif Intell Med, 2019, 102: 101711.
13. Richman J S, Randall M J. Physiological time-series analysis, using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol, 2000, 278(6): 2039-2049.
14. 张健钊, 姜威, 元辉, 等. 基于离散S变换和排列熵的癫痫脑电识别. 生物医学工程学杂志, 2017, 34(5): 681-687.
15. 曹春雨, 韩凌, 李春胜. 癫痫发作脑电信号的相位幅度调制研究. 中国生物医学工程学报, 2018, 37(1): 33-39.
16. Kumar Y, Dewal M L, Anand R S. Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network. Signal Image Video Process, 2014, 8(7): 1323-1334.
17. 余娴, 刘程程, 戴加飞, 等. 基于改进的k-最近邻网络的癫痫脑电信号分析. 生物医学工程学杂志, 2016, 33(6): 1039-1045.
18. 张健钊, 姜威, 贲晛烨. 基于样本熵与小波包能量特征提取和Real AdaBoost算法的正常期、癫痫间歇与发作期的脑电自动检测. 生物医学工程学杂志, 2016, 33(6): 1031-1038.
19. Andrzejak R G, Lehnertz K, Mormann F, et al. Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Phys Rev E, 2001, 64(6): 061907.
20. Koles Z J. The quantitative extraction and topographic mapping of the abnormal components in the clinical EEG. Electroencephalogr Clin Neurophysiol, 1991, 79(6): 440-447.
21. Bandt C, Pompe B. Permutation entropy: A natural complexity measure for time series. Phys Rev Lett, 2002, 88(17): 174102.
22. Song Yuedong, Crowcroft J, Zhang Jiaxiang. Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine. J Neurosci Meth, 2012, 210(2): 132-146.
23. Lei Meng, Zhang Li, Li Ming, et al. Near-infrared spectrum of coal origin identification based on SVM algorithm//第37届中国控制会议论文集. 武汉: 中国自动化学会控制理论专业委员会, 2018: 444-448.

Journal of Biomedical Engineering

Prediction of epilepsy based on common spatial model algorithm and support vector machine double classification

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