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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
引用本文: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[J].物理学报,2016,65(3):38703-038703.
作者姓名:张涛  陈万忠  李明阳
作者单位:吉林大学通信工程学院, 长春 130012
基金项目:吉林省科技发展计划自然基金项目(批准号: 20150101191JC)、高等学校博士学科点专项科研基金(批准号: 20100061110029)和吉林省科技发展计划重点项目(批准号: 20090350)资助的课题.
摘    要:实现癫痫脑电信号的自动检测对癫痫的临床诊断和治疗具有重要意义.本文提出先使用频率切片小波变换分离出5个不同频段的节律信号,再分别计算每个节律信号的近似熵和相邻节律的波动指数,最后使用遗传算法优化的支持向量机进行分类.实验结果表明,所提出的方法能够对正常、癫痫发作间期和癫痫发作期三种脑电信号进行准确分类,分类准确率为98.33%.

关 键 词:癫痫脑电信号  频率切片小波变换  支持向量机
收稿时间:2015-10-19

Automatic seizure detection of electroencephalogram signals based on frequency slice wavelet transform and SVM
Zhang Tao,Chen Wan-Zhong,Li Ming-Yang.Automatic seizure detection of electroencephalogram signals based on frequency slice wavelet transform and SVM[J].Acta Physica Sinica,2016,65(3):38703-038703.
Authors:Zhang Tao  Chen Wan-Zhong  Li Ming-Yang
Institution:Department of Communication Engineerings, Jilin University, Changchun 130012, China
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 δ (0.5-4 Hz), θ(4-8 Hz), α (8-13 Hz), β (13-30 Hz) and γ (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:electroencephalogram signal  frequency slice wavelet transform  support vector machine
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