Recognition of Low Arousal Level Electroencephalogram in the Vigilance Based on Wavelet Packet Rhythm and Support Vector Machine_Journal of Biomedical Engineering

Authors：

 YANGJianping ^1,3 , ZHANGDeqian ² , LUOWenlang ^1,3 , XIAOXiaopeng ¹

1. School of Electronics and Information Engineering, Jinggangshan University, Ji’an 343009, China;
2. Psychology Department, Jinggangshan University, Ji’an 343009, China;
3. Key Laboratory of Watershed Ecology and Geographical Environment Monitoring， NASG， Ji’an 343009, China;

Corresponding author：

YANGJianping, Email: kaiwu@scut.edu.cn

Keywords：

electroencephalogram; wavelet packet transform; support vector machine; low arousal level

DOI：

10.7507/1001-5515.20160012

Video：

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Poor and monotonous work could easily lead to a decrease of arousal level of the monitoring work personnel. In order to improve the performance of monitoring work, low arousal level needs to be recognized and awakened. We proposed a recognition method of low arousal by the electroencephalogram (EEG) as the object of study to recognize the low arousal level in the vigilance. We used wavelet packet transform to decompose the EEG signal so the EEG rhythms of each component were obtained, and then we calculated the parameters of relative energy and energy ratio of high-low frequency, and constructed the feature vector to monitor low arousal state in the operation. We finally used support vector machine (SVM) to recognize the low arousal state in the simulate operation. The experimental results showed that the method introduced in this article could well distinguish low arousal level from arousal level in the vigilance and it could also get a high recognition rate. Have been compared with other analysis methods, the present method could more effectively recognize low arousal level and provide better technical support for wake-up mechanism of low arousal state.

Citation： YANGJianping, ZHANGDeqian, LUOWenlang, XIAOXiaopeng. Recognition of Low Arousal Level Electroencephalogram in the Vigilance Based on Wavelet Packet Rhythm and Support Vector Machine. Journal of Biomedical Engineering, 2016, 33(1): 61-66. doi: 10.7507/1001-5515.20160012 Copy

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1. 杨英, 盛敬, 杨佳, 等. 基于神经网络的驾驶员觉醒水平双目标监测法[J]. 东北大学学报:自然科学版, 2007, 28(3):418-421.
2. MACHII K, DAIMON T, TERADA Y. Estimation of driver’s attentive state to visual objects outside the vehicle when driver is in low arousal level by using lambda response of Eye-Fixation-Related potentials[C]// 19th ITS World Congress. Vienna: 2012: 256-261.
3. MURATA A, KORIYAMA T, HAYAMI T. A basic study on the prevention of drowsy driving using the change of neck bending angle and the sitting pressure distribution[C]//2012 PROCEEDINGS OF SICE ANNUAL CONFERENCE (SICE). 2012: 274-279.
4. SUBASI A. Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients[J]. Expert Syst Appl, 2005, 28(4): 701-711.
5. 傅佳伟, 石立臣, 吕宝粮. 基于EEG的警觉度分析与估计研究综述[J]. 中国生物医学工程学报, 2009, 28(4):589-596.
6. SIMON M, SCHMIDT E A, KINCSES W E, et al. EEG alpha spindle measures as indicators of driver fatigue under real traffic conditions[J]. Clinical Neurophysiology, 2011, 122(6): 1168-1178.
7. 曾宪伟, 赵卫明, 盛菊琴. 小波包分解树结点与信号子空间频带的对应关系及其应用[J]. 地震学报, 2008, 30(1):90-96.
8. CORTES C, VAPNIK V. Support-vector networks[J]. Mach Learn, 1995, 20(3): 273-297.
9. AN Sen-jian, LIU Wan-quan, VENKATESH S. Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression[J]. Pattern Recognit, 2007, 40(8): 2154-2162.
10. VBEYLI E D. Combined neural network model employing wavelet coefficients for EEG signals classification[J]. Digit Signal Process, 2009, 19(2): 297-308.
11. SONG Y, LIO P. A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine[J]. J Biomed Sci Eng, 2010, 3(6): 556-567.
12. 柳平, 赵岩, 王军. 基于非线性特征提取的EEG信号支持向量分类器[J]. 汕头大学学报:自然科学版, 2009, 24(1):69-74.

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Recognition of Low Arousal Level Electroencephalogram in the Vigilance Based on Wavelet Packet Rhythm and Support Vector Machine

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