A partition bagging ensemble learning algorithm for Parkinson’s speech data mining_Journal of Biomedical Engineering

Authors：

 LI Yongming ^1,2 , ZHANG Cheng ¹ , WANG Pin ¹ , XIE Tingjie ¹ , ZENG Xiaoping ¹ , ZHANG Yanling ³ , CHENG Oumei ⁴ , YAN Fang ¹

1. School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, P.R.China;
2. Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing 400044, 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 400016, P.R.China;

Corresponding author：

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

Keywords：

Parkinson’s disease; classification; speech data; partition bagging boosting mechanism; ensemble learning

DOI：

10.7507/1001-5515.201803061

Video：

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Abstract Full text Figures/Tables Video References Cited by

Methods for achieving diagnosis of Parkinson’s disease (PD) based on speech data mining have been proven effective in recent years. However, due to factors such as the degree of disease of the data collection subjects and the collection equipment and environment, there are different categories of sample aliasing in the sample space of the acquired data set. Samples in the aliased area are difficult to be identified effectively, which seriously affects the classification accuracy of the algorithm. In order to solve this problem, a partition bagging ensemble learning is proposed in this article, which measures the aliasing degree of the sample by designing the the ratio of sample centroid distance metrics and divides the training set into multiple subsets. And then the method of transfer training of misclassified samples is used to adjust the results of subset partitioning. Finally, the optimized weights of each sub-classifier are used to integrate the test results. The experimental results show that the classification accuracy of the proposed method is significantly improved on two public datasets and the increasement of mean accuracy is up to 25.44%. This method not only effectively improves the classification accuracy of PD speech dataset, but also increases the sample utilization rate, providing a new idea for the diagnosis of PD.

Citation： LI Yongming, ZHANG Cheng, WANG Pin, XIE Tingjie, ZENG Xiaoping, ZHANG Yanling, CHENG Oumei, YAN Fang. A partition bagging ensemble learning algorithm for Parkinson’s speech data mining. Journal of Biomedical Engineering, 2019, 36(4): 548-556. doi: 10.7507/1001-5515.201803061 Copy

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2.	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.
3.	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.
4.	王刚, 郑汭, 谭玉燕, 等. 帕金森病疾病经济负担及相关因素的调查研究. 中华神经科杂志, 2006, 39(5): 336-337.
5.	姚芳传, 喻东山. 帕金森病的临床特点. 新医学, 2002, 33(5): 267.
6.	中华医学会神经病学分会运动障碍及帕金森病学组. 帕金森病的诊断. 中华神经科杂志, 2006, 39(6): 408-409.
7.	Ho A K, Iansek R, Marigliani C, et al. Speech impairment in a large sample of patients with Parkinson’s disease. Behav Neurol, 1998, 11(3): 131-137.
8.	Trail M, Fox C, Ramig L O, et al. Speech treatment for Parkinson’s disease. NeuroRehabilitation, 2005, 20(3): 205-221.
9.	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): 1015-1022.
10.	Smith A E, Nugent C D, McClean S I. Performance evaluation of artificial intelligence classifiers for the medical domain. Stud Health Technol Inform, 2002, 90: 553-556.
11.	Goldschmidt D, Decaestecker C, Berthe J V, et al. The contribution of image cytometry and artificial intelligence-related methods of numerical data analysis for adipose tumor histopathologic classification. Lab Invest, 1996, 75(3): 295-306.
12.	Buciński A, Marszałł M P, Krysiński J A, et al. Contribution of artificial intelligence to the knowledge of prognostic factors in Hodgkin’s lymphoma. Eur J Cancer Prev, 2010, 19(4): 308-312.
13.	Bayestehtashk A, Asgari M, Shafran I, et al. Fully automated assessment of the severity of Parkinson’s disease from dpeech. Comput Speech Lang, 2015, 29(1): 172-185.
14.	Khan T, Westin J, Dougherty M. Classification of speech intelligibility in Parkinson’s disease. Biocybern Biomed Eng, 2014, 34(1): 35-45.
15.	El Moudden I, Ouzir M, Elbernoussi S. Feature selection and extraction for class prediction in dysphonia measures analysis: A case study on Parkinson’s disease speech rehabilitation. Technol Health Care, 2017, 25(4): 693-708.
16.	Gürüler H. A novel diagnosis system for Parkinson’s disease using complex-valued artificial neural network with k-means clustering feature weighting method. Neural Comput Appl, 2017, 28(7): 1657-1666.
17.	Tsanas A, Little M A, McSharry P E. Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson’s disease symptom severity. J R Soc Interface, 2011, 8(59): 842-855.
18.	高春丽, 周梁, 王坚, 等. 帕金森病患者的发音和言语障碍及治疗学进展. 中华老年医学杂志, 2006, 25(9): 712-715.
19.	张玉海, 杜怀栋, 陈惠军, 等. 帕金森病的嗓音特征. 听力学及言语疾病杂志, 2001, 9(2): 84-86.
20.	Solomon N P, Hixon T J. Speech breathing in Parkinson’s disease. J Speech Hear Res, 1993, 36(2): 294-310.
21.	Martínez-Sánchez F, Meilán J J G, Carro J, et al. Speech rate in Parkinson’s disease: A controlled study. Neurología (English Edition), 2016, 31(7): 466-472.
22.	Adler C H, Beach T G, Hentz J G, et al. Low clinical diagnostic accuracy of early vs advanced Parkinson disease clinicopathologic study. Neurology, 2014, 83(5): 406-412.
23.	Li Yongming, Yang Liuyang, Wang Pin, et al. Classification of Parkinson’s disease by decision tree based instance selection and ensemble learning algorithms. J Med Imag Health In, 2017, 7(2): 444-452.
24.	Zhang Shichao, Li Xuelong, Zong Ming, et al. Learning k for kNN classification. ACM Transactions on Intelligent Systems and Technology, 2017, 8(3, SI): 1-19.
25.	Hartigan J A, Wong M A. Algorithm AS 136: A K-means clustering algorithm. Journal of the Royal Statistical Society: Series C (Applied Statistics), 1979, 28(1): 100-108.
26.	Zhang Hehua, Yang Liuyang, Liu Yuchuan, et al. Classification of Parkinson’s disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples. Biomed Eng Online, 2016, 15(1): 122.
27.	Torlay L, Perrone-Bertolotti M, Thomas E, et al. Machine learning-XGBoost analysis of language networks to classify patients with epilepsy. Brain informatics, 2017, 4(3): 159-169.

1. Dauer W, Przedborski S. Parkinson’s disease: mechanisms and models. Neuron, 2003, 39(6): 889-909.
2. 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.
3. 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.
4. 王刚, 郑汭, 谭玉燕, 等. 帕金森病疾病经济负担及相关因素的调查研究. 中华神经科杂志, 2006, 39(5): 336-337.
5. 姚芳传, 喻东山. 帕金森病的临床特点. 新医学, 2002, 33(5): 267.
6. 中华医学会神经病学分会运动障碍及帕金森病学组. 帕金森病的诊断. 中华神经科杂志, 2006, 39(6): 408-409.
7. Ho A K, Iansek R, Marigliani C, et al. Speech impairment in a large sample of patients with Parkinson’s disease. Behav Neurol, 1998, 11(3): 131-137.
8. Trail M, Fox C, Ramig L O, et al. Speech treatment for Parkinson’s disease. NeuroRehabilitation, 2005, 20(3): 205-221.
9. 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): 1015-1022.
10. Smith A E, Nugent C D, McClean S I. Performance evaluation of artificial intelligence classifiers for the medical domain. Stud Health Technol Inform, 2002, 90: 553-556.
11. Goldschmidt D, Decaestecker C, Berthe J V, et al. The contribution of image cytometry and artificial intelligence-related methods of numerical data analysis for adipose tumor histopathologic classification. Lab Invest, 1996, 75(3): 295-306.
12. Buciński A, Marszałł M P, Krysiński J A, et al. Contribution of artificial intelligence to the knowledge of prognostic factors in Hodgkin’s lymphoma. Eur J Cancer Prev, 2010, 19(4): 308-312.
13. Bayestehtashk A, Asgari M, Shafran I, et al. Fully automated assessment of the severity of Parkinson’s disease from dpeech. Comput Speech Lang, 2015, 29(1): 172-185.
14. Khan T, Westin J, Dougherty M. Classification of speech intelligibility in Parkinson’s disease. Biocybern Biomed Eng, 2014, 34(1): 35-45.
15. El Moudden I, Ouzir M, Elbernoussi S. Feature selection and extraction for class prediction in dysphonia measures analysis: A case study on Parkinson’s disease speech rehabilitation. Technol Health Care, 2017, 25(4): 693-708.
16. Gürüler H. A novel diagnosis system for Parkinson’s disease using complex-valued artificial neural network with k-means clustering feature weighting method. Neural Comput Appl, 2017, 28(7): 1657-1666.
17. Tsanas A, Little M A, McSharry P E. Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson’s disease symptom severity. J R Soc Interface, 2011, 8(59): 842-855.
18. 高春丽, 周梁, 王坚, 等. 帕金森病患者的发音和言语障碍及治疗学进展. 中华老年医学杂志, 2006, 25(9): 712-715.
19. 张玉海, 杜怀栋, 陈惠军, 等. 帕金森病的嗓音特征. 听力学及言语疾病杂志, 2001, 9(2): 84-86.
20. Solomon N P, Hixon T J. Speech breathing in Parkinson’s disease. J Speech Hear Res, 1993, 36(2): 294-310.
21. Martínez-Sánchez F, Meilán J J G, Carro J, et al. Speech rate in Parkinson’s disease: A controlled study. Neurología (English Edition), 2016, 31(7): 466-472.
22. Adler C H, Beach T G, Hentz J G, et al. Low clinical diagnostic accuracy of early vs advanced Parkinson disease clinicopathologic study. Neurology, 2014, 83(5): 406-412.
23. Li Yongming, Yang Liuyang, Wang Pin, et al. Classification of Parkinson’s disease by decision tree based instance selection and ensemble learning algorithms. J Med Imag Health In, 2017, 7(2): 444-452.
24. Zhang Shichao, Li Xuelong, Zong Ming, et al. Learning k for kNN classification. ACM Transactions on Intelligent Systems and Technology, 2017, 8(3, SI): 1-19.
25. Hartigan J A, Wong M A. Algorithm AS 136: A K-means clustering algorithm. Journal of the Royal Statistical Society: Series C (Applied Statistics), 1979, 28(1): 100-108.
26. Zhang Hehua, Yang Liuyang, Liu Yuchuan, et al. Classification of Parkinson’s disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples. Biomed Eng Online, 2016, 15(1): 122.
27. Torlay L, Perrone-Bertolotti M, Thomas E, et al. Machine learning-XGBoost analysis of language networks to classify patients with epilepsy. Brain informatics, 2017, 4(3): 159-169.

Journal of Biomedical Engineering

A partition bagging ensemble learning algorithm for Parkinson’s speech data mining

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