Perceptual Features as Markers of Parkinson's Disease: The Issue of Clinical Interpretability
This work addresses the need for better diagnostic markers for Parkinson's disease patients, though it is incremental as it builds on existing feature analysis methods.
The paper tackled the problem of quantifying hypokinetic dysarthria in Parkinson's disease by comparing perceptual features to conventional clinical features, finding that perceptual features outperformed conventional ones with a classification accuracy of 92% and strong correlations with clinical scores.
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.