A. Tsanas, M. A. Little, P. E. Mcsharry, J. Spielman, and L. O. Ramig, Novel speech signal processing Algorithms for high-accuracy classification of Parkinson"s disease, IEEE Transactions on Biomedical Engineering, vol.59, issue.5, pp.1264-1271, 2012.

R. K. Sharma and A. K. Gupta, Voice analysis for Telediagnosis of Parkinson disease using artificial neural networks and support vector machines, International Journal of Intelligent Systems and Applications, vol.7, issue.6, pp.41-47, 2015.

J. R. Williamson, Segment-dependent dynamics in predicting Parkinson"s disease, 2015.

P. Drotar, J. Mekyska, Z. Smekal, I. Rektorova, L. Masarova et al., Contribution of different handwriting modalities to differential diagnosis of Parkinson's Disease, 2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings, 2015.

A. A. Spadoto, R. C. Guido, F. L. Carnevali, A. F. Pagnin, A. X. Falcao et al., Improving Parkinson's disease identification through evolutionary-based feature selection, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011.

P. Drotar, J. Mekyska, I. Rektorova, L. Masarova, Z. Smekal et al., Decision support framework for Parkinson"s disease based on novel handwriting markers, IEEE Transactions on Neural Systems and Rehabilitation Engineering, pp.1-1, 2015.

E. J. Smits, Standardized handwriting to assess Bradykinesia, Micrographia and tremor in Parkinson"s disease, PLoS ONE, vol.9, issue.5, p.97614, 2014.

D. Garcia, Robust smoothing of gridded data in one and higher dimensions with missing values, Computational Statistics & Data Analysis, vol.54, issue.4, pp.1167-1178, 2010.
URL : https://hal.archives-ouvertes.fr/hal-01826117

D. N. Kaslovsky and F. G. Meyer, Noise Corruption Of Empirical Mode Decomposition And Its Effect On Instantaneous Frequency, Advances in Adaptive Data Analysis, vol.02, issue.03, pp.373-396, 2010.

P. Drotar, J. Mekyska, I. Rektorova, L. Masarova, Z. Smekal et al., A new modality for quantitative evaluation of Parkinson's disease: In-air movement, 13th IEEE International Conference on BioInformatics and BioEngineering, 2013.

C. Hsu, C. Chang, and C. Lin, A Practical Guide to Support Vector Classification, 2003.

S. Fahn, Unified Parkinson"s disease rating scale, 2006.

L. Kurlowicz and M. Wallace, the mini mental state examination (MMSE), 1999.

R. Bhidayasiri and D. Tarsy, Parkinson"S Disease: Hoehn And Yahr Scale, Movement Disorders: A Video Atlas, pp.4-5, 2012.

P. Breheny, kernel density estimation, 2012.

S. S. Shapiro and R. S. Francia, An Approximate Analysis of Variance Test for Normality, Journal of the American Statistical Association, vol.67, issue.337, pp.215-216, 1972.

W. Trochim, The T-Test, 2006.

R. Shier, Statistics: 2.3 The Mann-Whitney U Test, 2004.

M. Paret, Alphas, P-Values, and Confidence Intervals, Oh My! | Minitab, 1970.

D. Doermann, E. Zotkina, and H. Li, GEDI-A Groundtruthing environment for document images, 2010.

A. Rakotomamonjy, Support Vector Machines and Area Under ROC curve, 2004.

P. Drotár, J. Mekyska, I. Rektorová, L. Masarová, Z. Smékal et al., Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson"s disease, Artificial Intelligence in Medicine, vol.67, issue.23, pp.39-46, 2016.

, The shapiro-wilk and related tests for normality, 2015.

A. Slabi, ROC Analysis with Matlab