Abstract
Different characterization approaches, including nonlinear dynamics (NLD), have been addressed for the automatic detection of PD; however, the obtained discrimination capability when only NLD features are considered has not been evaluated yet.
This paper evaluates the discrimination capability of a set with ten different NLD features in the task of automatic classification of speech signals from people with Parkinson’s disease (PPD) and a control set (CS). The experiments presented in this paper are performed considering the five Spanish vowels uttered by 20 PPD and 20 people from the CS.
According the results, it is possible to achieve accuracy rates of up to 76,81% considering only utterances from the vowel /i/. When features calculated from the five Spanish vowels are combined, the performance of the system is not improved, indicating that the inclusion of more NLD features to the system does not guarantee better performance.
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References
Ramig, L.O., Fox, C., Shimon, S.: Speech treatment for parkinson’s disease. Expert Review Neurotherapeutics 8(2), 297–309 (2008)
Perez, K.S., Ramig, L.O., Smith, M.E., Dromery, C.: The parkinson larynx: tremor and videostroboscopic findings. Journal of Voice 10(4), 353–361 (1996)
Kantz, H., Schreiber, T.: Nonlinear time series analysis, 2nd edn. Cambridge University Press, Cambridge (2006)
Giovanni, A., Ouaknine, M., Guelfucci, R., Yu, T., Zanaret, M., Triglia, J.M.: Nonlinear behavior of vocal fold vibration: the role of coupling between the vocal folds. Journal of Voice 13(4), 456–476 (1999)
Little, M.A., McSharry, P.E., Hunter, E.J., Spielman, J., Ramig, L.O.: Suitability of dysphonia measurements for telemonitoring of parkinson’s disease. IEEE Transactions on Bio-Medical Engineering 56(4), 1015–1022 (2009)
Tsanas, A., Little, M., McSharry, P., Ramig, L.: Accurate telemonitoring of parkinson’s disease progression by noninvasive speech tests. IEEE Transactions on Biomedical Engineering 57(4), 884–893 (2010)
Kostek, B., Kaszuba, K., Zwan, P., Robowski, P., Slawek, J.: Automatic assessment of the motor state of the parkinson’s disease patient-a case study. Diagnostic Pathology 7(1), 1–8 (2012)
Orozco-Arroyave, J., Arias-Londoño, J.D., Bonilla, J.V., Nöth, E.: Automatic detection of hypernasal speech signals using nonlinear and entropy measurements. In: Proceedings of the INTERSPEECH (2012)
Arias-Londoño, J., Godino-Llorente, J., Sáenz-Lechón, N., Osma-Ruiz, V., Castellanos-Domínguez, G.: Automatic detection of pathological voices using complexity measures, noise parameters, and mel-cepstral coefficients. IEEE Transactions on Bio-medical Engineering 58(2), 370–379 (2011)
Takens, F.: Detecting strange attractors in turbulence. Dynamical Systems and Turbulence: Lecture Notes in Mathematics, vol. 898, pp. 366–381 (1981)
Kaspar, F., Shuster, H.G.: Easily calculable measure for complexity of spatiotemporal patterns. Physical Review A 36(2), 842–848 (1987)
Costa, M., Goldberger, A., Peng, C.: Multiscale entropy analysis of biological signals. Physical Review E 71, 1–18 (2005)
Xu, L.S., Wang, K.Q., Wang, L.: Gaussian kernel approximate entropy algorithm for analyzing irregularity of time series. In: Proceedings of the International Conference on Machine Learning and Cybernetics, pp. 5605–5608 (2005)
Little, M.A., McSharry, P.E., Roberts, S.J., Costello, D.E., Moroz, I.M.: Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. Biomedical Engineering Online 6(23), 1–19 (2007)
Novovicova, J., Pudil, J.K.P.: Floating search methods in feature selection. Pattern Recognition Letters 15(11), 1119–1125 (1994)
Scholköpf, B., Smola, A.: Learning with Kernel. The MIT press (2002)
Sáenz-Lechón, N., Godino-Llorente, J., Osma-Ruiz, V., Gómez-Vilda, P.: Methodological issues in the development of automatic systems for voice pathology detection. Biomedical Signal Processing and Control 1, 120–128 (2006)
Phonetics, D.: Dissection of the speech production mechanism. Working Papers in Phonetics, UCLA (102), 1–89 (2002)
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Orozco-Arroyave, J.R., Arias-Londoño, J.D., Vargas-Bonilla, J.F., Nöth, E. (2013). Analysis of Speech from People with Parkinson’s Disease through Nonlinear Dynamics. In: Drugman, T., Dutoit, T. (eds) Advances in Nonlinear Speech Processing. NOLISP 2013. Lecture Notes in Computer Science(), vol 7911. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38847-7_15
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DOI: https://doi.org/10.1007/978-3-642-38847-7_15
Publisher Name: Springer, Berlin, Heidelberg
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