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International Journal of Speech Technology
Erschienen in: International Journal of Speech Technology 1/2018

21.12.2017

Multiclass classification of Parkinson’s disease using cepstral analysis

verfasst von: Elmehdi Benmalek, Jamal Elmhamdi, Abdelilah Jilbab

Erschienen in: International Journal of Speech Technology | Ausgabe 1/2018

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Abstract

This paper addressees the problem of an early diagnosis of PD (Parkinson’s disease) by the classification of characteristic features of person’s voice knowing that 90% of the people with PD suffer from speech disorders. We collected 375 voice samples from healthy and people suffer from PD. We extracted from each voice sample features using the MFCC and PLP Cepstral techniques. All the features are analyzed and selected by feature selection algorithms to classify the subjects in 4 classes according to UPDRS (unified Parkinson’s disease Rating Scale) score. The advantage of our approach is the resulting and the simplicity of the technique used, so it could also extended for other voice pathologies. We used as classifier the discriminant analysis for the results obtained in previous multiclass classification works. We obtained accuracy up to 87.6% for discrimination between PD patients in 3 different stages and healthy control using MFCC along with the LLBFS algorithm.
Vorheriger Artikel Arabic isolated word recognition system using hybrid feature extraction techniques and neural network
Nächster Artikel Speaker discrimination based on fuzzy fusion and feature reduction techniques

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Metadaten
Titel
Multiclass classification of Parkinson’s disease using cepstral analysis
verfasst von
Elmehdi Benmalek
Jamal Elmhamdi
Abdelilah Jilbab
Publikationsdatum
21.12.2017
Verlag
Springer US
Erschienen in
International Journal of Speech Technology / Ausgabe 1/2018
Print ISSN: 1381-2416
Elektronische ISSN: 1572-8110
DOI
https://doi.org/10.1007/s10772-017-9485-2

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