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).

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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 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 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 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

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

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