Journal of Medical Systems

, Volume 34, Issue 4, pp 591–599 | Cite as

Telediagnosis of Parkinson’s Disease Using Measurements of Dysphonia

  • C. Okan Sakar
  • Olcay Kursun
Original Paper


Parkinson’s disease (PD) is a neurological illness which impairs motor skills, speech, and other functions such as mood, behavior, thinking, and sensation. It causes vocal impairment for approximately 90% of the patients. As the symptoms of PD occur gradually and mostly targeting the elderly people for whom physical visits to the clinic are inconvenient and costly, telemonitoring of the disease using measurements of dysphonia (vocal features) has a vital role in its early diagnosis. Such dysphonia features extracted from the voice come in variety and most of them are interrelated. The purpose of this study is twofold: (1) to select a minimal subset of features with maximal joint relevance to the PD-score, a binary score indicating whether or not the sample belongs to a person with PD; and (2) to build a predictive model with minimal bias (i.e. to maximize the generalization of the predictions so as to perform well with unseen test examples). For these tasks, we apply the mutual information measure with the permutation test for assessing the relevance and the statistical significance of the relations between the features and the PD-score, rank the features according to the maximum-relevance-minimum-redundancy (mRMR) criterion, use a Support Vector Machine (SVM) for building a classification model and test it with a more suitable cross-validation scheme that we called leave-one-individual-out that fits with the dataset in hand better than the conventional bootstrapping or leave-one-out validation methods.


Acoustic measurements for telemedicine Mutual information Permutation test Maximum relevance minimum redundancy (mRMRCross-validation 


  1. 1.
    ]Little, M. A., McSharry, P. E., Hunter, E. J., Ramig, L. O., Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease. IEEE Trans. Biomed. Eng. 2009. doi: 10.1109/TBME.2008.2005954.
  2. 2.
    Ishihara, L., and Brayne, C., A systematic review of depression and mental illness preceding Parkinson’s disease. Acta Neurol. Scand. 113 (4)211–220, 2006. doi: 10.1111/j.1600-0404.2006.00579.x.CrossRefGoogle Scholar
  3. 3.
    Jankovic, J., Parkinson’s disease: clinical features and diagnosis. J. Neurol. Neurosurg. Psychiatry. 79:368–376, 2008. doi: 10.1136/jnnp.2007.131045.CrossRefGoogle Scholar
  4. 4.
    Huse, D. M., Schulman, K., Orsini, L., Castelli-Haley, J., Kennedy, S., and Lenhart, G., Burden of illness in Parkinson’s disease. Mov. Disord. 20:1449–1454, 2005. doi: 10.1002/mds.20609.CrossRefGoogle Scholar
  5. 5.
    Ho, A. K., Iansek, R., Marigliani, C., and Bradshaw, J. L., Gates, S., Speech impairment in a large sample of patients with Parkinson’s disease. Behav. Neurol. 11:131–137, 1998.Google Scholar
  6. 6.
    Ruggiero, C., Sacile, R., and Giacomini, M., Home telecare. J. Telemed. Telecare. 5:11–17, 1999. doi: 10.1258/1357633991932333.CrossRefGoogle Scholar
  7. 7.
    Little, M. A., McSharry, P. E., Roberts, S. J., Costello, D. A., and Moroz, I. M., Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. Biomed. Eng. Online. 6:23, 2007. doi: 10.1186/1475-925X-6-23.CrossRefGoogle Scholar
  8. 8.
    Godino-Llorente, J. I., and Gomez-Vilda, P., Automatic detection of voice impairments by means of short-term cepstral parameters and neural network based detectors. IEEE Trans. Biomed. Eng. 51:380–384, 2004. doi: 10.1109/TBME.2003.820386.CrossRefGoogle Scholar
  9. 9.
    Rahn, D. A., Chou, M., Jiang, J. J., and Zhang, Y., Phonatory impairment in Parkinson’s disease: Evidence from nonlinear dynamic analysis and perturbation analysis. J. Voice. 21:64–71, 2007. doi: 10.1016/j.jvoice.2005.08.011.CrossRefGoogle Scholar
  10. 10.
    Guyon, I., and Elisseeff, A., An introduction to variable and feature selection. J. Mach. Learn. Res. 3:1157–1182, 2003. doi: 10.1162/153244303322753616.zbMATHCrossRefGoogle Scholar
  11. 11.
    Peng, H., Long, F., and Ding, C., Feature selection based on mutual information: criteria of max-dependency, max-relevance and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27 (8)1226–1238, 2005. doi: 10.1109/TPAMI.2005.159.CrossRefGoogle Scholar
  12. 12.
    Shannon, C. E., A mathematical theory of communication. Bell System Technical Journal. 27:379–423, 623–656, 1948.MathSciNetGoogle Scholar
  13. 13.
    Good, P., Permutation Tests. Springer, New York, p. 270, 1994.zbMATHGoogle Scholar
  14. 14.
    Hsu, C. W., and Lin, C. J., A comparison of methods for multi-class support vector machines. IEEE Trans. Neural Netw. 13:415–425, 2002. doi: 10.1109/TNN.2002.1000139.CrossRefGoogle Scholar
  15. 15.
    Efron, B., Bootstrap methods: Another look at the jackknife. Ann. Stat. 7:1–26, 1979. doi: 10.1214/aos/1176344552.zbMATHCrossRefMathSciNetGoogle Scholar
  16. 16.
    Reunanen, J., Overfitting in making comparisons between variable selection methods. J. Mach. Learn. Res. 3:1371–1382, 2003. doi: 10.1162/153244303322753715.zbMATHCrossRefGoogle Scholar
  17. 17.
    Molinaro, A., Simon, R., and Pfeiffer, R., Prediction error estimation: A comparison of resampling methods. Bioinformatics. 21:3301–3307, 2005. doi: 10.1093/bioinformatics/bti499.CrossRefGoogle Scholar
  18. 18.
    Liu, R. Y., Bootstrap procedures under some non-i.i.d. models. Ann. Stat. 16:1696–1708, 1988. doi: 10.1214/aos/1176351062.zbMATHCrossRefGoogle Scholar
  19. 19.
    Wu, C. F. J., Jackknife, bootstrap, and other resampling methods in regression analysis (with discussion). Ann. Stat. 14:1261–1295, 1986. doi: 10.1214/aos/1176350142.zbMATHCrossRefGoogle Scholar
  20. 20.
    Azuaje, F., Genomic data sampling and its effect on classification performance assessment. BMC Bioinformatics. 4:5, 2003. doi: 10.1186/1471-2105-4-5.CrossRefGoogle Scholar
  21. 21.
    Learning Repository, U.C.I.:, June 2008.
  22. 22.
    Ding, C., and Peng, H., Minimum redundancy feature selection from microarray gene expression data. J. Bioinform. Comput. Biol. 3 (2)185–205, 2005. doi: 10.1142/S0219720005001004.CrossRefMathSciNetGoogle Scholar
  23. 23.
    Kwak, N., and Choi, C. H., Input feature selection by mutual information based on Parzen Window. IEEE Trans. Pattern Anal. Mach. Intell. 24 (12)1667–1671, 2002. doi: 10.1109/TPAMI.2002.1114861.CrossRefGoogle Scholar
  24. 24.
    Hsu, C. W., Lin, C. J., A Comparison of methods for multi-class support vector machines. IEEE Trans. Neural Netw. 13:415–425, 2002. LIBSVM software available for download at doi: 10.1109/TNN.2002.1000139.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of Computer EngineeringBahcesehir UniversityIstanbulTurkey

Personalised recommendations