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Voice assessments for detecting patients with Parkinson’s diseases using PCA and NPCA

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Abstract

In this study, we wanted to discriminate between two groups of people. The database used in this study contains 20 patients with Parkinson’s disease and 20 healthy people. Three types of sustained vowels (/a/, /o/ and /u/) were recorded from each participant and then the analyses were done on these voice samples. Firstly, an initial feature vector extracted from time, frequency and cepstral domains. Then we used linear and nonlinear feature extraction techniques, principal component analysis (PCA), and nonlinear PCA. These techniques reduce the number of parameters and choose the most effective acoustic features used for classification. Support vector machine with its different kernel was used for classification. We obtained an accuracy up to 87.50 % for discrimination between PD patients and healthy people.

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Authors and Affiliations

  1. Laboratoire de Recherche en Génie Electrique, Ecole Normale Supérieure de l’Enseignement Technique, Mohammed V University, Rabat, Morocco

    Achraf Benba, Abdelilah Jilbab & Ahmed Hammouch

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  1. Achraf Benba
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  2. Abdelilah Jilbab
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  3. Ahmed Hammouch
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Correspondence to Achraf Benba.

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Achraf Benba declares that he has no conflict of interest. And he doesn’t have any financial relationship with the organization. Abdelilah Jilbab declares that he has no conflict of interest. And he doesn’t have any financial relationship with the organization. Ahmed Hammouch declares that he has no conflict of interest. And he doesn’t have any financial relationship with the organization. All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5).

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Benba, A., Jilbab, A. & Hammouch, A. Voice assessments for detecting patients with Parkinson’s diseases using PCA and NPCA. Int J Speech Technol 19, 743–754 (2016). https://doi.org/10.1007/s10772-016-9367-z

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  • DOI: https://doi.org/10.1007/s10772-016-9367-z

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