Diagnosing Parkinson’s disease using the classification of speech signals

• Autorzy: Froelich W. , Wróbel K. , Porwik P.
• Języki publikacji: EN

Abstrakty

EN

This paper addressees the problem of an early diagnosis of Parkinson’s disease by the classification of characteristic features of person’s voice. A new, two-step classification approach is proposed. In the first step, the voice samples are classified using standard state-of-the-art classifiers. In the second step, the classified samples are assigned to patients and the final classification process based on majority criterion is performed. The advantage of using our new approach is the resulting, reliable patientoriented medical diagnose. The proposed two-step method of classification allows also to deal with the variable number of voice samples gathered for every patient. Preliminary experiments revealed quite satisfactory classification accuracy obtained during the performed leave-one-out cross validation.

Słowa kluczowe

EN

Parkinson's disease; medical diagnosis; data classification

PL

choroba Parkinsona; diagnostyka medyczna; klasyfikacja danych

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2014
• Tom: Vol. 23
• Strony: 187--193
• Opis fizyczny: Bibliogr. 15 poz., rys., tab.

Twórcy

autor

Froelich W.

• Institute of Computer Science, University of Silesia, ul. Bedzinska 39, Sosnowiec

autor

Wróbel K.

• Institute of Computer Science, University of Silesia, ul. Bedzinska 39, Sosnowiec

autor

Porwik P.

• Institute of Computer Science, University of Silesia, ul. Bedzinska 39, Sosnowiec

Bibliografia

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