Abstract
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.
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References
]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.
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.
Jankovic, J., Parkinson’s disease: clinical features and diagnosis. J. Neurol. Neurosurg. Psychiatry. 79:368–376, 2008. doi:10.1136/jnnp.2007.131045.
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.
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.
Ruggiero, C., Sacile, R., and Giacomini, M., Home telecare. J. Telemed. Telecare. 5:11–17, 1999. doi:10.1258/1357633991932333.
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.
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.
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.
Guyon, I., and Elisseeff, A., An introduction to variable and feature selection. J. Mach. Learn. Res. 3:1157–1182, 2003. doi:10.1162/153244303322753616.
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.
Shannon, C. E., A mathematical theory of communication. Bell System Technical Journal. 27:379–423, 623–656, 1948.
Good, P., Permutation Tests. Springer, New York, p. 270, 1994.
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.
Efron, B., Bootstrap methods: Another look at the jackknife. Ann. Stat. 7:1–26, 1979. doi:10.1214/aos/1176344552.
Reunanen, J., Overfitting in making comparisons between variable selection methods. J. Mach. Learn. Res. 3:1371–1382, 2003. doi:10.1162/153244303322753715.
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.
Liu, R. Y., Bootstrap procedures under some non-i.i.d. models. Ann. Stat. 16:1696–1708, 1988. doi:10.1214/aos/1176351062.
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.
Azuaje, F., Genomic data sampling and its effect on classification performance assessment. BMC Bioinformatics. 4:5, 2003. doi:10.1186/1471-2105-4-5.
Learning Repository, U.C.I.: http://archive.ics.uci.edu/ml/, June 2008.
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.
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.
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 http://www.csie.ntu.edu.tw/~cjlin/libsvm. doi:10.1109/TNN.2002.1000139.
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Sakar, C.O., Kursun, O. Telediagnosis of Parkinson’s Disease Using Measurements of Dysphonia. J Med Syst 34, 591–599 (2010). https://doi.org/10.1007/s10916-009-9272-y
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DOI: https://doi.org/10.1007/s10916-009-9272-y