Automatic seizure detection of electroencephalogram signals based on frequency slice wavelet transform and SVM

Zhang Tao; Chen Wan-Zhong; Li Ming-Yang

doi:10.7498/aps.65.038703

Abstract

Over 50 million people all over the world are suffering from epilepsy It is of great significance to achieve automatic seizure detection in electroencephalogram (EEG) signal for clinical diagnosis and treatment. In order to achieve automatic diagnosis of epilepsy, a multitude of automated computer aided diagnostic techniques have been proposed. However, only a few of studies lay emphasis on the effects of different rhythm signals. To explore the influence of rhythm signals on classification accuracy, a newly-developed time-frequency analysis method called frequency slice wavelet transform (FSWT), which is able to locate arbitrary time-frequency range with the use of frequency slice function and whose inverse transformation only relies on fast Fourier transform, is employed to extract five different rhythm signals, namely (0.5-4 Hz), (4-8 Hz), (8-13 Hz), (13-30 Hz) and (30-50 Hz) from original EEG signal. Subsequently, for extracting the nonlinear and linear features, the approximate entropy of each rhythm signal and fluctuation index of adjacent rhythm signals are calculated to reflect the variation characteristics of rhythm signals and they are flocked together to form the nine-dimensional feature vectors. Finally, the extracted vectors are fed into a support vector machine (SVM) which is optimized by genetic algorithms (GA) for classification. Specifically, since the parameters of SVM are associated with the final classification accuracy and appropriate parameters could lead to a remarkable result, GA is applied to parameter optimization, half of the obtained vectors are randomly selected as a training set for training, and the remaining vectors constitute a testing set to test the established model. Experimental results of the proposed approach, which is employed in a public epileptic EEG dataset obtained from department of epitology at Bonn University for validation indicate that the proposed method in this study can carry out the task of classifying normal, inter-ictal and epileptic seizure EEG signals with a high classification accuracy (98.33%), a sensitivity of 99%, a specificity of 99%, and a positive predictive value of 99.5%. The presented approach provides an outstanding scheme for the automatic diagnosis of epilepsy, and the directions of our further research may include the application of the proposed method to the diagnosis of other disorders.

Keywords:

Authors and contacts

1.
Department of Communication Engineerings, Jilin University, Changchun 130012, China

Corresponding author: Chen Wan-Zhong, chenwz@jlu.edu.cn

Funds: Project supported by the Natural Science Foundation for Science and Technology Development Plan of Jilin Province, China (Grant No. 20150101191JC), the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20100061110029) and Key Project of Jilin Province Science and Technology Development Plan, China (Grant No. 20090350).

References

[1]	Wang Y, Hou F Z, Dai J F, Liu X F, Li J, Wang J 2014 Acta Phys. Sin. 63 218701 (in Chinese) [王莹, 侯凤贞, 戴加飞, 刘新峰, 李锦, 王俊 2014 物理学报 63 218701]
[2]	Zhang T, Chen W Z, Li M Y 2015 Acta Phys. Sin. 64 128701 (in Chinese) [张涛, 陈万忠, 李明阳 2015 物理学报 64 128701]
[3]	Meng Q F, Chen S S, Chen Y H, Feng Z Q 2014 Acta Phys. Sin. 63 050506 (in Chinese) [孟庆芳, 陈珊珊, 陈月辉, 冯志全 2014 物理学报 63 050506]
[4]	Acharya U R, Sree S V, Swapna G, Martis R J, Suri J S 2013 Knowl-Based Syst. 45 147
[5]	Sasankari K, Thanushkodi K 2014 J. Electr. Eng. Technol. 9 1060
[6]	Kumar Y, Dewal M L, Anand R S 2014 Signal Image Video Process 8 1323
[7]	Li S F, Zhou W D, Yuan Q, Geng S J, Cai D M 2013 Comput. Biol. Med. 43 807
[8]	Khoa T Q D, Huong N T M, Toi V V 2012 Comput. Math. Meth. Med. 1 259
[9]	Geng S J, Zhou W D, Yuan Q, Cai D M, Zeng Y J 2011 Neurol. Res. 33 908
[10]	Ahmadlou M, Adeli H, Adeli A 2010 J. Neural T. 117 1099
[11]	Ahmadlou M, Adeli H, Adeli A 2012 Int. J. Psycp. 85 206
[12]	Yuan Q, Zhou W D, Li S F, Cai D M 2012 Chin. J. Sci. Inst. 33 514 (in Chinses) [袁琦, 周卫东, 李淑芳, 蔡冬梅 2012 仪器仪表学报 33 514]
[13]	Song Y D, Crowcroft J, Zhang J X 2012 J. Neurosci. Meth. 210 132
[14]	Acharya U R, Sree S V, Chattopadhyay S, Yu W W, PENG C A A 2011 Int. J. Neur. Syst. 21 199
[15]	Acharya U R, Yanti R, Zheng J W, Krishnan M M R, Tan J H, Martis R J, Lim C M 2013 Int. J. Neur. Syst. 23 1001
[16]	Yan Z, Miyamoto A, Jiang Z W 2009 Mech. Syst. Signal Pr. 23 1474
[17]	Yan Z H, Miyamoto A, Jiang Z W, Liu X L 2010 Mech. Syst. Signal Pr. 24 491
[18]	Yan Z H, Miyamoto A, Jiang Z W 2011 Comput. Struct. 89 14
[19]	Zhang Y H, Liu M J, Huang N T, Duan W R, Li T Y 2015 High Volt. Eng. 41 2283 (in Chinses) [张宇辉, 刘梦婕, 黄南天, 段伟润, 李天云 2015 高电压技术 41 2283]
[20]	Sun D S 2004 Ph. D. Dissertation (Changsha: Central South University) (in Chinses) [孙德山 2004 博士学位论文 (长沙: 中南大学)]
[21]	Xue N J 2011 Comput. Eng. Design 32 1792 (in Chinses) [薛宁静 2011计算机工程与设计 32 1792]
[22]	Chen S T, Yu P S 2007 J. Hydrol. 347 67
[23]	Andrzejak R G, Lehnertz K, Rieke C, Mormann F, David P, Elger C E 2001 Phys. Rev. E 64 061907
[24]	Li S F, Zhou W D, Yuan Q, Geng S J, Cai D M 2013 Comput. Biol. Med. 43 807
[25]	Pachori R B, Patidar S 2014 Comput. Meth. Prog. Bio. 113 494
[26]	Kumar Y, Dewal M L, Anand R S 2014 Neurocomputing 8 3
[27]	Yuan Q, Zhou W D, Yuan S S, Li X L, Wang J W, Jia G J 2014 Int. J. Neur. Syst. 24 1450015

Cited By

[1]	Wang Y, Hou F Z, Dai J F, Liu X F, Li J, Wang J 2014 Acta Phys. Sin. 63 218701 (in Chinese) [王莹, 侯凤贞, 戴加飞, 刘新峰, 李锦, 王俊 2014 物理学报 63 218701]
[2]	Zhang T, Chen W Z, Li M Y 2015 Acta Phys. Sin. 64 128701 (in Chinese) [张涛, 陈万忠, 李明阳 2015 物理学报 64 128701]
[3]	Meng Q F, Chen S S, Chen Y H, Feng Z Q 2014 Acta Phys. Sin. 63 050506 (in Chinese) [孟庆芳, 陈珊珊, 陈月辉, 冯志全 2014 物理学报 63 050506]
[4]	Acharya U R, Sree S V, Swapna G, Martis R J, Suri J S 2013 Knowl-Based Syst. 45 147
[5]	Sasankari K, Thanushkodi K 2014 J. Electr. Eng. Technol. 9 1060
[6]	Kumar Y, Dewal M L, Anand R S 2014 Signal Image Video Process 8 1323
[7]	Li S F, Zhou W D, Yuan Q, Geng S J, Cai D M 2013 Comput. Biol. Med. 43 807
[8]	Khoa T Q D, Huong N T M, Toi V V 2012 Comput. Math. Meth. Med. 1 259
[9]	Geng S J, Zhou W D, Yuan Q, Cai D M, Zeng Y J 2011 Neurol. Res. 33 908
[10]	Ahmadlou M, Adeli H, Adeli A 2010 J. Neural T. 117 1099
[11]	Ahmadlou M, Adeli H, Adeli A 2012 Int. J. Psycp. 85 206
[12]	Yuan Q, Zhou W D, Li S F, Cai D M 2012 Chin. J. Sci. Inst. 33 514 (in Chinses) [袁琦, 周卫东, 李淑芳, 蔡冬梅 2012 仪器仪表学报 33 514]
[13]	Song Y D, Crowcroft J, Zhang J X 2012 J. Neurosci. Meth. 210 132
[14]	Acharya U R, Sree S V, Chattopadhyay S, Yu W W, PENG C A A 2011 Int. J. Neur. Syst. 21 199
[15]	Acharya U R, Yanti R, Zheng J W, Krishnan M M R, Tan J H, Martis R J, Lim C M 2013 Int. J. Neur. Syst. 23 1001
[16]	Yan Z, Miyamoto A, Jiang Z W 2009 Mech. Syst. Signal Pr. 23 1474
[17]	Yan Z H, Miyamoto A, Jiang Z W, Liu X L 2010 Mech. Syst. Signal Pr. 24 491
[18]	Yan Z H, Miyamoto A, Jiang Z W 2011 Comput. Struct. 89 14
[19]	Zhang Y H, Liu M J, Huang N T, Duan W R, Li T Y 2015 High Volt. Eng. 41 2283 (in Chinses) [张宇辉, 刘梦婕, 黄南天, 段伟润, 李天云 2015 高电压技术 41 2283]
[20]	Sun D S 2004 Ph. D. Dissertation (Changsha: Central South University) (in Chinses) [孙德山 2004 博士学位论文 (长沙: 中南大学)]
[21]	Xue N J 2011 Comput. Eng. Design 32 1792 (in Chinses) [薛宁静 2011计算机工程与设计 32 1792]
[22]	Chen S T, Yu P S 2007 J. Hydrol. 347 67
[23]	Andrzejak R G, Lehnertz K, Rieke C, Mormann F, David P, Elger C E 2001 Phys. Rev. E 64 061907
[24]	Li S F, Zhou W D, Yuan Q, Geng S J, Cai D M 2013 Comput. Biol. Med. 43 807
[25]	Pachori R B, Patidar S 2014 Comput. Meth. Prog. Bio. 113 494
[26]	Kumar Y, Dewal M L, Anand R S 2014 Neurocomputing 8 3
[27]	Yuan Q, Zhou W D, Yuan S S, Li X L, Wang J W, Jia G J 2014 Int. J. Neur. Syst. 24 1450015

[1]	Zhang Yi-Jun, Mu Xiao-Dong, Guo Le-Meng, Zhang Peng, Zhao Dao, Bai Wen-Hua. A support vector machine training scheme based on quantum circuits. Acta Physica Sinica, 2023, 72(7): 070302. doi: 10.7498/aps.72.20222003
[2]	Liang Ke-Da, Liu Teng-Fei, Chang Zhe, Zhang Meng, Li Zhi-Xin, Huang Song-Song, Wang Jing. Inversion models of internal solitary wave propagation speed in ocean based on least squares method and support vector machine. Acta Physica Sinica, 2023, 72(2): 028301. doi: 10.7498/aps.72.20221633
[3]	Song Kun, Gao Tai-Chang, Liu Xi-Chuan, Yin Min, Xue Yang. Method and experiment of rainfall intensity inversion using a microwave link based on support vector machine. Acta Physica Sinica, 2015, 64(24): 244301. doi: 10.7498/aps.64.244301
[4]	Zhao Zhi-Gang, Zhang Chun-Jie, Gou Xiang-Feng, Sang Hu-Tang. Solar cell temperature prediction model of support vector machine optimized by particle swarm optimization algorithm. Acta Physica Sinica, 2015, 64(8): 088801. doi: 10.7498/aps.64.088801
[5]	Zhang Tao, Chen Wan-Zhong, Li Ming-Yang. Recognition of epilepsy electroencephalography based on AdaBoost algorithm. Acta Physica Sinica, 2015, 64(12): 128701. doi: 10.7498/aps.64.128701
[6]	Xing Hong-Yan, Zhu Qing-Qing, Xu Wei. A method of weak target detection based on the sea clutter. Acta Physica Sinica, 2014, 63(10): 100505. doi: 10.7498/aps.63.100505
[7]	Meng Qing-Fang, Chen Shan-Shan, Chen Yue-Hui, Feng Zhi-Quan. Automatic detection of epileptic EEG based on recurrence quantification analysis and SVM. Acta Physica Sinica, 2014, 63(5): 050506. doi: 10.7498/aps.63.050506
[8]	Yu Yang, Hao Zhong-Qi, Li Chang-Mao, Guo Lian-Bo, Li Kuo-Hu, Zeng Qing-Dong, Li Xiang-You, Ren Zhao, Zeng Xiao-Yan. Identification of plastics by laser-induced breakdown spectroscopy combined with support vector machine algorithm. Acta Physica Sinica, 2013, 62(21): 215201. doi: 10.7498/aps.62.215201
[9]	Zhao Yong-Ping, Zhang Li-Yan, Li De-Cai, Wang Li-Feng, Jiang Hong-Zhang. Chaotic time series prediction using filtering window based least squares support vector regression. Acta Physica Sinica, 2013, 62(12): 120511. doi: 10.7498/aps.62.120511
[10]	Wang Fang-Fang, Zhang Ye-Rong. An electromagnetic inverse scattering approach based on support vector machine. Acta Physica Sinica, 2012, 61(8): 084101. doi: 10.7498/aps.61.084101
[11]	Xing HongYan, Qi ZhengDong, Xu Wei. Weak signal estimation in chaotic clutter using selective support vector machine ensemble. Acta Physica Sinica, 2012, 61(24): 240504. doi: 10.7498/aps.61.240504
[12]	Yan Xiao-Mei, Liu Ding. Control of fractional order chaotic system based on least square support vector machines. Acta Physica Sinica, 2010, 59(5): 3043-3048. doi: 10.7498/aps.59.3043
[13]	Wang Ge-Li, Yang Pei-Cai, Mao Yu-Qing. On the application of non-stationary time series prediction based on the SVM method. Acta Physica Sinica, 2008, 57(2): 714-719. doi: 10.7498/aps.57.714
[14]	. Prediction of chaotic time series based on selective support vector machine ensemble. Acta Physica Sinica, 2007, 56(12): 6820-6827. doi: 10.7498/aps.56.6820
[15]	Zhang Jia-Shu, Dang Jian-Liang, Li Heng-Chao. Local support vector machine prediction of spatiotemporal chaotic time series. Acta Physica Sinica, 2007, 56(1): 67-77. doi: 10.7498/aps.56.67
[16]	Ye Mei-Ying. Control of chaotic system based on least squares support vector machine modeling. Acta Physica Sinica, 2005, 54(1): 30-34. doi: 10.7498/aps.54.30
[17]	Ye Mei-Ying, Wang Xiao-Dong, Zhang Hao-Ran. Chaotic time series forecasting using online least squares support vector machine regression. Acta Physica Sinica, 2005, 54(6): 2568-2573. doi: 10.7498/aps.54.2568
[18]	Liu Han, Liu Ding, Ren Hai-Peng. Chaos control based on least square support vector machines. Acta Physica Sinica, 2005, 54(9): 4019-4025. doi: 10.7498/aps.54.4019
[19]	Cui Wan-Zhao, Zhu Chang-Chun, Bao Wen-Xing, Liu Jun-Hua. Prediction of the chaotic time series using support vector machines for fuzzy rule-based modeling. Acta Physica Sinica, 2005, 54(7): 3009-3018. doi: 10.7498/aps.54.3009
[20]	Cui Wan-Zhao, Zhu Chang-Chun, Bao Wen-Xing, Liu Jun-Hua. Prediction of the chaotic time series using support vector machines. Acta Physica Sinica, 2004, 53(10): 3303-3310. doi: 10.7498/aps.53.3303

Catalog

Vol. 65, Issue 3 - 2016-02-05

Metrics

Abstract views: 5891
PDF Downloads: 280
Cited By: 0

姓名
邮箱
手机号码
标题
留言内容
验证码

Search

Article

留言板