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Voice assessments for detecting patients with neurological diseases using PCA and NPCA

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Abstract

In this study, we wanted to discriminate between 30 patients who suffer from Parkinson’s disease (PD) and 20 patients with other neurological diseases (ND). All participants were asked to pronounce sustained vowel /a/ hold as long as possible at comfortable level. The analyses were done on these voice samples. Firstly, an initial feature vector extracted from time, frequency and cepstral domains. Then we used principal component analysis (PCA) and nonlinear PCA (NPCA). These techniques reduce the number of parameters and select the most effective ones to be used for classification. Support vector machine and k-nearest neighbor with different kernels was used for classification. We obtained accuracy up to 88% for discrimination between PD patients ND patients using KNN with k equal to three and five.

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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

Authors
  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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Conflict of interest

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.

Ethical approval

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 neurological diseases using PCA and NPCA. Int J Speech Technol 20, 673–683 (2017). https://doi.org/10.1007/s10772-017-9438-9

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  • DOI: https://doi.org/10.1007/s10772-017-9438-9

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