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Recent studies have demonstrated that analysis of laboratory-quality voicerecordings can be used to accurately differentiate people diagnosed withParkinson's disease (PD) from healthy controls (HC). These findings could helpfacilitate the development of remote screening and monitoring tools for PD. Inthis study, we analyzed 2759 telephone-quality voice recordings from 1483 PDand 15321 recordings from 8300 HC participants. To account for variations inphonetic backgrounds, we acquired data from seven countries. We developed astatistical framework for analyzing voice, whereby we computed 307 dysphoniameasures that quantify different properties of voice impairment, such as,breathiness, roughness, monopitch, hoarse voice quality, and exaggerated vocaltremor. We used feature selection algorithms to identify robust parsimoniousfeature subsets, which were used in combination with a Random Forests (RF)classifier to accurately distinguish PD from HC. The best 10-foldcross-validation performance was obtained using Gram-Schmidt Orthogonalization(GSO) and RF, leading to mean sensitivity of 64.90% (standard deviation, SD2.90%) and mean specificity of 67.96% (SD 2.90%). This large-scale study is astep forward towards assessing the development of a reliable, cost-effectiveand practical clinical decision support tool for screening the population atlarge for PD using telephone-quality voice.

Developing a large scale population screening tool for the assessment of Parkinson's disease using telephone-quality voice