Cepstral Analysis and Hilbert-Huang Transform for Automatic Detection of Parkinson’s Disease

Keywords: Speech articulation, Classification, Hilbert-Huang, Parkinson’s Disease


Most patients with Parkinson’s Disease (PD) develop speech deficits, including reduced sonority, altered articulation, and abnormal prosody. This article presents a methodology to automatically classify patients with PD and Healthy Control (HC) subjects. In this study, the Hilbert-Huang Transform (HHT) and Mel-Frequency Cepstral Coefficients (MFCCs) were considered to model modulated phonations (changing the tone from low to high and vice versa) of the vowels /a/, /i/, and /u/. The HHT was used to extract the first two formants from audio signals with the aim of modeling the stability of the tongue while the speakers were producing modulated vowels. Kruskal-Wallis statistical tests were used to eliminate redundant and non-relevant features in order to improve classification accuracy. PD patients and HC subjects were automatically classified using a Radial Basis Support Vector Machine (RBF-SVM). The results show that the proposed approach allows an automatic discrimination between PD and HC subjects with accuracies of up to 75 % for women and 73 % for men.

Author Biographies

Felipe O. López-Pabón*, Universidad de Antioquia, Colombia

Ingeniero Electrónico, Facultad de Ingeniería, Universidad de Antioquia, Medellín-Colombia, forlando.lopez@udea.edu.co

Tomas Arias-Vergara, Universidad de Antioquia, Colombia

MSc. en Ingeniería, Facultad de Ingeniería, Universidad de Antioquia, Laboratorio de reconocimiento de patrones (LME), Medellín-Colombia,  Universidad de Erlangen-Núremberg, Erlangen-Germany, Universidad de Múnich, Múnich-Germany, tomas.arias@udea.edu.co

Juan R. Orozco-Arroyave, Universidad de Antioquia, Colombia

PhD. en Ciencias de la Computación, Grupo de investigación en Telecomunicaciones aplicadas (GITA), Facultad de Ingeniería, Universidad de Antioquia, Laboratorio de reconocimiento de patrones (LME), Medellín-Colombia, Universidad de Erlangen-Núremberg, Erlangen-Germany, rafael.orozco@udea.edu.co


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How to Cite
F. O. López-Pabón, T. Arias-Vergara, and J. R. Orozco-Arroyave, “Cepstral Analysis and Hilbert-Huang Transform for Automatic Detection of Parkinson’s Disease”, TecnoL., vol. 23, no. 47, pp. 93-108, Jan. 2020.


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