Combining speech sample and feature bilateral selection algorithm for classification of Parkinson’s disease_Journal of Biomedical Engineering

Authors：

ZHANG Xiaoheng ¹ , WANG Lirui ² , CAO Yao ² , WANG Pin ² , ZHANG Cheng ² , YANG Liuyang ² ,  LI Yongming ² , ZHANG Yanling ³ , CHENG Oumei ⁴

1. Chongqing Radio & TV University, Chongqing 400052, P.R.China;
2. College of Communication Engineering, Chongqing University, Chongqing 400030,P.R.China;
3. Department of Neurology, Southwest hospital, Third military medical university, Chongqing 400038, P.R.China;
4. Department of Neurology, The first affiliated hospital, Chongqing Medical University, Chongqing 400038, P.R.China;

Corresponding?author：

LI Yongming, Email: yongmingli@cqu.edu.cn

Keywords：

Parkinson’s disease; classification; bilateral hybrid speech feature selection; synergy effects; multiple kernel learning

DOI：

10.7507/1001-5515.201704061

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Diagnosis of Parkinson’s disease (PD) based on speech data has been proved to be an effective way in recent years. However, current researches just care about the feature extraction and classifier design, and do not consider the instance selection. Former research by authors showed that the instance selection can lead to improvement on classification accuracy. However, no attention is paid on the relationship between speech sample and feature until now. Therefore, a new diagnosis algorithm of PD is proposed in this paper by simultaneously selecting speech sample and feature based on relevant feature weighting algorithm and multiple kernel method, so as to find their synergy effects, thereby improving classification accuracy. Experimental results showed that this proposed algorithm obtained apparent improvement on classification accuracy. It can obtain mean classification accuracy of 82.5%, which was 30.5% higher than the relevant algorithm. Besides, the proposed algorithm detected the synergy effects of speech sample and feature, which is valuable for speech marker extraction.

Citation： ZHANG Xiaoheng, WANG Lirui, CAO Yao, WANG Pin, ZHANG Cheng, YANG Liuyang, LI Yongming, ZHANG Yanling, CHENG Oumei. Combining speech sample and feature bilateral selection algorithm for classification of Parkinson’s disease. Journal of Biomedical Engineering, 2017, 34(6): 942-948. doi: 10.7507/1001-5515.201704061 Copy

1.	Lang A E, Lozano A M. Parkinson's disease. New England Journal of Medicine, 1998, 37(3): 198.
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.	王宗寶, 黃永志, 張新靜, 等. 帕金森病患者局部場電位信號多頻耦合特征分析. 生物醫學工程學雜志, 2015, 32(4): 874-880.
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.	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.	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.	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.	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.	Yair E, Gath I. High resolution Pole-Zero analysis of parkinsonian speech. IEEE Trans Biomed Eng, 1991, 38: 161-167.
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.	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.	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.	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.	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.	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.	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.	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.	李勇明, 楊劉洋, 劉玉川, 等. 基于語音樣本重復剪輯和隨機森林的帕金森病診斷算法研究. 生物醫學工程學雜志, 2016, 33(6): 1053-1059.
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.	Gonen M, Alpaydin E. Multiple kernel learning algorithms. Journal of Machine Learning Research, 2011, 12: 2211-2268.

1. Lang A E, Lozano A M. Parkinson's disease. New England Journal of Medicine, 1998, 37(3): 198.
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. 王宗寶, 黃永志, 張新靜, 等. 帕金森病患者局部場電位信號多頻耦合特征分析. 生物醫學工程學雜志, 2015, 32(4): 874-880.
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. 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. 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. 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. 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. Yair E, Gath I. High resolution Pole-Zero analysis of parkinsonian speech. IEEE Trans Biomed Eng, 1991, 38: 161-167.
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. 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. 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. 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. 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. 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. 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. 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. 李勇明, 楊劉洋, 劉玉川, 等. 基于語音樣本重復剪輯和隨機森林的帕金森病診斷算法研究. 生物醫學工程學雜志, 2016, 33(6): 1053-1059.
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. Gonen M, Alpaydin E. Multiple kernel learning algorithms. Journal of Machine Learning Research, 2011, 12: 2211-2268.

Previous Article
Study of neuronal spike-frequency adaptation with transcranial magneto-acoustical stimulation
Next Article
Design and implementation of real-time continuous glucose monitoring instrument

Journal of Biomedical Engineering

Combining speech sample and feature bilateral selection algorithm for classification of Parkinson’s disease

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content