Perceptual Features as Markers of Parkinson's Disease: The Issue of Clinical Interpretability

TL;DR

This study demonstrates that perceptual voice features can more effectively distinguish Parkinson's disease patients from healthy individuals and correlate with clinical scores than traditional acoustic features, despite being less interpretable.

Contribution

It provides large-scale evidence that perceptual features outperform conventional acoustic features in PD voice analysis, highlighting their potential for clinical applications.

Findings

Perceptual features achieve 92% classification accuracy.

Perceptual features show strong correlation with clinical scores.

Perceptual features outperform traditional features in discrimination power.

Abstract

Up to 90% of patients with Parkinson's disease (PD) suffer from hypokinetic dysathria (HD) which is also manifested in the field of phonation. Clinical signs of HD like monoloudness, monopitch or hoarse voice are usually quantified by conventional clinical interpretable features (jitter, shimmer, harmonic-to-noise ratio, etc.). This paper provides large and robust insight into perceptual analysis of 5 Czech vowels of 84 PD patients and proves that despite the clinical inexplicability the perceptual features outperform the conventional ones, especially in terms of discrimination power (classification accuracy ACC = 92 %, sensitivity SEN = 93 %, specificity SPE = 92 %) and partial correlation with clinical scores like UPDRS (Unified Parkinson's disease rating scale), MMSE (Mini-mental state examination) or FOG (Freezing of gait questionnaire), where p < 0.0001.