A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform

• Published in: Applied soft computing Vol. 74; pp. 255 - 263
• Main Authors: Sakar, C. Okan, Serbes, Gorkem, Gunduz, Aysegul, Tunc, Hunkar C., Nizam, Hatice, Sakar, Betul Erdogdu, Tutuncu, Melih, Aydin, Tarkan, Isenkul, M. Erdem, Apaydin, Hulya
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2019
• Subjects: Decision support system; Ensemble learning; Mel-frequency cepstral coefficients; Parkinson’s disease telemonitoring; Tunable Q-factor wavelet transform; Mel-frequency cepstral coefficients; Decision support system; Parkinson’s disease telemonitoring; Ensemble learning; Tunable Q-factor wavelet transform
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2018.10.022

In recent years, there has been increasing interest in the development of telediagnosis and telemonitoring systems for Parkinson’s disease (PD) based on measuring the motor system disorders caused by the disease. As approximately 90% percent of PD patients exhibit some form of vocal disorders in the...