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-  2017 

混合语音段特征双边式优选算法用于帕金森病分类研究

DOI: doi:10.7507/1001-5515.201704061

Keywords: 帕金森病, 分类, 双边式混合语音特征选择, 协同效应, 多核学习

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Abstract:

近年来,已有研究证明基于语音数据可实现帕金森病(PD)的诊断,但是目前相关研究主要集中在特征提取及分类器设计等方面,对于样本优选方面考虑不足。本课题组前期研究结果表明,样本优选可有效改进分类准确性,但是样本和语音的相关关系至今还未能深入研究。因此,本文提出了基于相关特征加权和多核学习算法,同时对语音段和特征进行优选,用于发现语音段和特征的协同效应,从而达到提升 PD 分类准确性的目的。实验结果表明,本文算法针对受试者的分类准确率达到了 82.5%,较已有文献算法提高了 30.5%。此外,本文算法还挖掘出了语音段和特征的协同效应,对语音标记物提取有一定参考价值

References

[1]  1. Lang A E, Lozano A M. Parkinson's disease. New England Journal of Medicine, 1998, 37(3): 198.
[2]  2. van Den Eeden S K, Tanner C M, Bernstein A L, et al. Incidence of parkinson's disease: variation by age, gender, and race/ethnicity. Am J Epidemiol, 2003, 157(11): 1015-1022.
[3]  3. 王宗宝, 黄永志, 张新静, 等. 帕金森病患者局部场电位信号多频耦合特征分析. 生物医学工程学杂志, 2015, 32(4): 874-880.
[4]  4. O’sullivan S B, Schmitz T J. Improving functional outcomes in physical rehabilitation. 5th ed. USA: F. A. Davis Company, 2010: 856-894.
[5]  5. Baghai-Ravary L, Beet S W. Automatic speech signal analysis for clinical diagnosis and assessment of speech disorders. SpringerBriefs in Electrical and Computer Engineering, 2012, 115(2): 31-36.
[6]  6. Little M A, Mcsharry P E, Hunter E J, et al. Suitability of dysphonia measurements for telemonitoring of Parkinson's disease. IEEE Trans Biomed Eng, 2009, 56(4): 1015.
[7]  7. Tsanas A, Little M A, Mcsharry P E, et al. Novel speech signal processing algorithms for high-accuracy classification of Parkinson’s disease. IEEE Trans Biomed Eng, 2012, 59(5): 1264-1271.
[8]  8. Sakar B E, Isenkul M E, Sakar C O, et al. Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE J Biomed Health Inform, 2013, 17(4): 828-834.
[9]  9. Yair E, Gath I. High resolution Pole-Zero analysis of parkinsonian speech. IEEE Trans Biomed Eng, 1991, 38: 161-167.
[10]  10. Perez C J, Naranjo L, Martin J, et al. A latent variable-based Bayesian regression to address recording replications in Parkinson’s disease. European Signal Processing Conference, 2014: 1447-1451.
[11]  11. Hariharan M, Polat K, Sindhu R. A new hybrid intelligent system for accurate detection of Parkinson's disease. Comput Methods Programs Biomed, 2014, 113(3): 904-913.
[12]  12. Yang Shanshan, Zheng Fang, Luo Xin, et al. Effective dysphonia detection using feature dimension reduction and kernel density estimation for patients with parkinson's disease. PLoS One, 2014, 9(2): 1-10.
[13]  13. Shahbakhti M, Taherifar D, Sorouri A. Linear and Non-linear Speech Features for Detection of Parkinson's disease//The 2013 Biomedical Engineering International Conference, 2013.
[14]  14. Avci D, Dogantekin A. An expert diagnosis system for parkinson disease based on genetic Algorithm-Wavelet Kernel-Extreme learning machine. Parkinsons Dis, 2016: 1-9.
[15]  15. Galaz Z, Mekyska J, Mzourek Z, et al. Department prosodic analysis of neutral, stress-modified andrhymed speech in patients with parkinson's disease. Comput Methods Programs Biomed, 2016, 127: 301-317.
[16]  16. Hirschauer T J, Adeli H, Buford J A. Computer-Aided diagnosis of parkinson's disease using enhanced probabilistic neural network. Journal of Medical System, 2015, 39: 179.
[17]  17. Kazumune H, Shuichi A, Dimos V. Dimarogonas Self-Triggered model predictive control for nonlinear Input-Affine dynamical systems via adaptive control Samples Selection. IEEE Trans Automat Contr, 2017, 62(1): 177-189.
[18]  18. 李勇明, 杨刘洋, 刘玉川, 等. 基于语音样本重复剪辑和随机森林的帕金森病诊断算法研究. 生物医学工程学杂志, 2016, 33(6): 1053-1059.
[19]  19. Kira K, Rendell L. The feature selection problem: Traditional methods and a new algorithm//Proceedings of the Ninth National conference on Artificial Intelligence, New Orleans: AAAI press, 1992: 129-134.
[20]  20. Gonen M, Alpaydin E. Multiple kernel learning algorithms. Journal of Machine Learning Research, 2011, 12: 2211-2268.

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