生物医学工程学杂志

生物医学工程学杂志

基于小波多尺度分析和极限学习机的癫痫脑电分类算法

查看全文

癫痫脑电的自动分类对于癫痫的诊断和治疗具有重要意义。本文提出了一种基于小波多尺度分析和极限学习机的癫痫脑电分类方法。首先,利用小波多尺度分析对原始脑电信号进行多尺度分解,提取出不同频段的脑电信号。然后采用Hurst指数和样本熵两种非线性方法对原始脑电信号和小波多尺度分解得到的不同频段脑电信号进行特征提取。最后,将得到的特征向量输入到极限学习机中,实现癫痫脑电分类的目的。本文采用的方法在区分癫痫发作期和发作间期时取得了99.5%的分类准确率。结果表明,本方法在癫痫的诊断和治疗中具有很好的应用前景。

The automatic classification of epileptic electroencephalogram (EEG) is significant in the diagnosis and therapy of epilepsy. A classification algorithm for epileptic EEG based on wavelet multiscale analysis and extreme learning machine (ELM) is proposed in this paper. Firstly, wavelet multiscale analysis is applied to the original EEG to extract its sub-bands. Then, two nonlinear methods, i.e. Hurst exponent (Hurst) and sample entropy (SamEn) are used to the feature extraction of EEG and its sub-bands. Finally, ELM algorithm is employed in epileptic EEG classification with the nonlinear features. The proposed method in this paper achieved 99.5% classification accuracy for the discrimination between epileptic ictal and interictal EEG. The result implies that this method has good prospects in the diagnosis and therapy of epilepsy.

关键词: 小波多尺度分析; Hurst指数; 样本熵; 癫痫脑电; 极限学习机

Key words: wavelet multiscale analysis; Hurst exponent; sample entropy; epileptic electroencephalogram; extreme learning machine

引用本文: 崔刚强, 夏良斌, 梁建峰, 涂敏. 基于小波多尺度分析和极限学习机的癫痫脑电分类算法. 生物医学工程学杂志, 2016, 33(6): 1025-1030,1038. doi: 10.7507/1001-5515.20160165 复制

登录后 ,请手动点击刷新查看全文内容。 没有账号,
1. IASEMIDIS L D. Epileptic seizure prediction and control[J]. IEEE Trans Biomed Eng, 2003, 50(5): 549-558.
2. FISHER R S, BOAS W V, BLUME W, et al. Epileptic seizures and epilepsy: Definitions proposed by the International League against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE)[J]. Epilepsia, 2005, 46(4): 470-472.
3. 周昌贵. 脑电图诊断要点[J].现代电生理学杂志,2004,11(3):165-184.
4. LAI Y C, OSORIO I, FREI M G, et al. Computational neuroscience in epilepsy[M]. San Diego: Academic Press, 2008.
5. YUAN Qi, ZHOU Weidong, LI Shufang, et al. Epileptic EEG classification based on extreme learning machine and nonlinear features[J]. Epilepsy Res, 2011, 96(1/2): 29-38.
6. GUO Ling, RIVERO D, PAZOS A. Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks[J]. J Neurosci Methods, 2010, 193(1): 156-163.
7. YUAN Qi, ZHOU Weidong, LIU Yinxia, et al. Epileptic seizure detection with linear and nonlinear features[J]. Epilepsy Behav, 2012, 24(4): 415-421.
8. JOUNY C C, BERGEY G K. Characterization of early partial seizure onset: frequency, complexity and entropy[J]. Clin Neurophysiol, 2012, 123(4): 658-669.
9. KUMAR S P, SRIRAAM N, BENAKOP P G, et al. Entropies based detection of epileptic seizures with artificial neural network classifiers[J]. Expert Syst Appl, 2010, 37(4): 3284-3291.
10. VIDYASAGAR K E C, MOGHAVVEMI M, PRABHAT T S S T. Performance evaluation of contemporary classifiers for automatic detection of epileptic EEG[C]//IEEE International Conference on Industrial Instrumentation and Control (ICIC). Pune: IEEE, 2015: 372-377.
11. SOOD M, BHOOSHAN S V. Automatic processing of EEG signals for seizure detection using soft computing techniques[C]//IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014). Jaipur: IEEE, 2014: 1-6.
12. KOVACS P, SAMIEE K, GABBOUJ M. On application of rational discrete short time Fourier transform in epileptic seizure classification[C]//IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP-2014). Florence: IEEE, 2014: 5839-5843.
13. CHISCI L, MAVINO A, PERFERI G, et al. Real-time epileptic seizure prediction using AR models and support vector machines[J]. IEEE Trans Biomed Eng, 2010, 57(5): 1124-1132.
14. KUMAR Y, DEWAL M L, ANAND R S. Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine[J]. Neurocomputing, 2014, 133(8): 271-279.
15. 朱天桥, 黄力宇.单导癫痫脑电模糊特征提取的支持向量机发作预测[J].仪器仪表学报,2010,31(11):2434-2439.
16. HUANG G B, ZHU Q Y, SIEW C K. Extreme learning machine: Theory and applications[J]. Neurocomputing, 2006, 70(1/3): 489-501.
17. ABRY P, CHAINAIS P, COUTIN L, et al. Multifractal random walks as fractional wiener integrals[J]. IEEE Transactions on Information Theory, 2009, 55(8): 3825-3846.
18. RICHMAN J S, MOORMAN J R. Physiological time-series analysis using approximate entropy and sample entropy[J]. Am J Physiol Heart Circ Physiol, 2000, 278(6): H2039-H2049.
19. 蔡冬梅, 周卫东,刘凯,等.基于Hurst指数和SVM的癫痫脑电检测方法[J].中国生物医学工程学报,2010,29(6):836-840.
20. 徐永红, 李杏杏,赵勇.基于小波包和多元多尺度熵的癫痫脑电信号分类方法[J].生物医学工程学杂志,2013,30(5):1073-1078.
21. 袁琦, 周卫东,李淑芳,等.基于ELM和近似熵的脑电信号检测方法[J].仪器仪表学报,2012,33(3):514-519.