Research on Diagnosis Algorithm of Parkinson's Disease Based on Speech Sample Multi-edit and Random Forest_Journal of Biomedical Engineering

Authors：

 LIYongming ^1,2 , YANGLiuyang ¹ , LIUYuchuan ¹ , WANGPin ¹ , QIUMingguo ² , XIEWenbin ¹ , ZHANGXiaoheng ³

1. College of Communication Engineering, Chongqing University, Chongqing 400030, China;
2. Department of Medical Image, College of Biomedical Engineering, Third Military Medical University, Chongqing 400038, China;
3. Chongqing Radio & TV University, Chongqing 400052, China;

Corresponding author：

LIYongming, Email: yongmingli@cqu.edu.cn

Keywords：

Parkinson's disease; diagnosis; speech sample selection; ensemble learning; multi-edit nearest neighbour algorithm; random forest

DOI：

10.7507/1001-5515.20160169

Video：

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Abstract Full text Figures/Tables Video References Cited by

Parkinson's disease (PD) diagnosis based on speech data has been proved to be an effective way in recent years. There are still some problems on preprocessing samples, ensemble learning, and so on. The problems can further cause misleading of classifiers, unsatisfactory classification accuracy and stability. This paper proposed a new diagnosis algorithm of PD by combining multi-edit sample selection method and random forest. At the end of it, this paper presents a group of experiments carried out with the newest public datasets. Experimental results showed that this proposed algorithm realized the classification of the samples and the subjects of PD. Furthermore, it achieved average classification accuracy of 100% and obtained improvement of up to 29.44% compared to those provided by the subjects. This paper proposes a new speech diagnosis algorithm for PD based on instance selection; and the method algorithm has a higher and more stable classification accuracy, compared with the other algorithms.

Citation： LIYongming, YANGLiuyang, LIUYuchuan, WANGPin, QIUMingguo, XIEWenbin, ZHANGXiaoheng. Research on Diagnosis Algorithm of Parkinson's Disease Based on Speech Sample Multi-edit and Random Forest. Journal of Biomedical Engineering, 2016, 33(6): 1053-1059. doi: 10.7507/1001-5515.20160169 Copy

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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[J]. Am J Epidemiol, 2003, 157(11): 1015-1022.
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11.	TSANAS A, LITTLE M A, MCSHARRY P E, et al. Accurate telemonitoring of Parkinson's disease progression by noninvasive speech tests[J]. IEEE Trans Biomed Eng, 2010, 57(4): 884-893.
12.	PRASHANTH R, ROY S D, MANDAL P K, et al. Parkinson's disease detection using olfactory loss and REM sleep disorder features[C]//2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Chicago, 2014: 5764-5767.
13.	BENBA A, JILBAB A, HAMMOUCH A, et al. Voiceprints analysis using MFCC and SVM for detecting patients with Parkinson's disease[C]//2015 International Conference on Electrical and Information Technologies (ICEIT). Marrakech, 2015: 300-304.
14.	TSANAS A, LITTLE M A, MCSHARRY P E, et al. Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease[J]. IEEE Trans Biomed Eng, 2012, 59(5): 1264-1271.
15.	SU M, CHUANG K S. Dynamic feature selection for detecting Parkinson's disease through voice signal[C]//2015 IEEE MTT-S 2015 International Microwave Workshop Series on RF and Wireless Technologies for Biomedical and Healthcare Applications (IMWS-BIO). Taipei, 2015: 148-149.
16.	李娟, 王宇平.考虑局部均值和类全局信息的快速近邻原型选择算法[J].自动化学报,2014(6):1116-1125.
17.	LI Leijun, HU Qinghua, WU Xiangqian, et al. Exploration of classification confidence in ensemble learning[J]. Pattern Recognit, 2014, 47(9): 3120-3131.
18.	HATTORI K, TAKAHASHI M. A new nearest-neighbor rule in the pattern classification problem[J]. Pattern Recognit, 1999, 32(3): 425-432.
19.	RICO-JUAN J R, IÑESTA J M. New rank methods for reducing the size of the training set using the nearest neighbor rule[J]. Pattern Recognit Lett, 2012, 33(5): 654-660.

1. DE RIJK M C, LAUNER L J, BERGER K, et al. Prevalence of Parkinson's disease in Europe: A collaborative study of population-based cohorts. Neurologic Diseases in the Elderly Research Group[J]. Neurology, 2000, 54(11 Suppl 5): S21-S23.
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[J]. Am J Epidemiol, 2003, 157(11): 1015-1022.
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[J].生物医学工程学杂志,2015,32(4):874-880.
4. O'SULLIVAN S B, SCHMITZ T J. Parkinson disease[M]//Physical rehabilitation. 5th ed. Philadelphia: F. A. Davis Company, 2007: 856-894.
5. BAGHAI-RAVARY L, BEET S W. Automatic speech signal analysis for clinical diagnosis and assessment of speech disorders [J]. Springer Briefs 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[J]. IEEE Trans Biomed Eng, 2009, 56(4): 1015-1022.
7. 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[J]. IEEE J Biomed Health Inform, 2013, 17(4): 828-834.
8. HARIHARAN M, POLAT K, SINDHU R. A new hybrid intelligent system for accurate detection of Parkinson's disease[J]. Comput Methods Programs Biomed, 2014, 113(3): 904-913.
9. ISLAM M S, PARVEZ I, DENG Hai, et al. Performance comparison of heterogeneous classifiers for detection of Parkinson's disease using voice disorder (dysphonia)[C]//2014 International Conference on Informatics, Electronics & Vision (ICIEV). Dhaka, 2014: 1-7.
10. PÉREZ C J, NARANJO L, MARTIN J, et al. A latent variable-based Bayesian regression to address recording replications in Parkinson's disease[C]//2014 22nd European Signal Processing Conference (EUSIPCO). Lisbon, 2014: 1447-1451.
11. TSANAS A, LITTLE M A, MCSHARRY P E, et al. Accurate telemonitoring of Parkinson's disease progression by noninvasive speech tests[J]. IEEE Trans Biomed Eng, 2010, 57(4): 884-893.
12. PRASHANTH R, ROY S D, MANDAL P K, et al. Parkinson's disease detection using olfactory loss and REM sleep disorder features[C]//2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Chicago, 2014: 5764-5767.
13. BENBA A, JILBAB A, HAMMOUCH A, et al. Voiceprints analysis using MFCC and SVM for detecting patients with Parkinson's disease[C]//2015 International Conference on Electrical and Information Technologies (ICEIT). Marrakech, 2015: 300-304.
14. TSANAS A, LITTLE M A, MCSHARRY P E, et al. Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease[J]. IEEE Trans Biomed Eng, 2012, 59(5): 1264-1271.
15. SU M, CHUANG K S. Dynamic feature selection for detecting Parkinson's disease through voice signal[C]//2015 IEEE MTT-S 2015 International Microwave Workshop Series on RF and Wireless Technologies for Biomedical and Healthcare Applications (IMWS-BIO). Taipei, 2015: 148-149.
16. 李娟, 王宇平.考虑局部均值和类全局信息的快速近邻原型选择算法[J].自动化学报,2014(6):1116-1125.
17. LI Leijun, HU Qinghua, WU Xiangqian, et al. Exploration of classification confidence in ensemble learning[J]. Pattern Recognit, 2014, 47(9): 3120-3131.
18. HATTORI K, TAKAHASHI M. A new nearest-neighbor rule in the pattern classification problem[J]. Pattern Recognit, 1999, 32(3): 425-432.
19. RICO-JUAN J R, IÑESTA J M. New rank methods for reducing the size of the training set using the nearest neighbor rule[J]. Pattern Recognit Lett, 2012, 33(5): 654-660.

Journal of Biomedical Engineering

Research on Diagnosis Algorithm of Parkinson's Disease Based on Speech Sample Multi-edit and Random Forest

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