Analysis of voice as an assisting tool for detection of Parkinson's disease and its subsequent clinical interpretation

Abstract

In this work, a voice-based analysis is conducted with the contribution of providing physicians with a decision tool along with framing information to help them see functional differences and understand why the detection method suspects PD. The voice-based detection method consists in applying feature subset selection and four different classifiers to voice recordings from five datasets (gender-based, balanced and unbalanced) derived from the largest public dataset for voice-based PD detection. One of the contributions is an improvement over previous works on voice-based PD detection over the same dataset, in terms of performance and complexity. The detection performance is characterized by 95.9% of accuracy, 98.35% of sensitivity, 91.06% of specificity, and 95.6% of precision in women; and 94.36% of accuracy, 100% of sensitivity, 97.1% of specificity, and 96.83% of precision in men. The number of features, fed to classifiers, ranges from 6 to 20. This work shows that different factors are associated with PD detection according to gender: high-frequency voice content is the most significant functional information to assist PD detection in women, while low-frequency content assists PD detection in men better. It is shown that a comparison of the variability of the most important features between patients with PD and controls can be used as contextual information by a physician to have a better interpretation of the classification.

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