Speech acoustic indices for differential diagnosis between Parkinson's disease, multiple system atrophy and progressive supranuclear palsy

Abstract

While speech disorder represents an early and prominent clinical feature of atypical parkinsonian syndromes such as multiple system atrophy (MSA) and progressive supranuclear palsy (PSP), little is known about the sensitivity of speech assessment as a potential diagnostic tool. Speech samples were acquired from 215 subjects, including 25 MSA, 20 PSP, 20 Parkinson's disease participants, and 150 healthy controls. The accurate differential diagnosis of dysarthria subtypes was based on the quantitative acoustic analysis of 26 speech dimensions related to phonation, articulation, prosody, and timing. A semi-supervised weighting-based approach was then applied to find the best feature combinations for separation between PSP and MSA. Dysarthria was perceptible in all PSP and MSA patients and consisted of a combination of hypokinetic, spastic, and ataxic components. Speech features related to respiratory dysfunction, imprecise consonants, monopitch, slow speaking rate, and subharmonics contributed to worse performance in PSP than MSA, whereas phonatory instability, timing abnormalities, and articulatory decay were more distinctive for MSA compared to PSP. The combination of distinct speech patterns via objective acoustic evaluation was able to discriminate between PSP and MSA with very high accuracy of up to 89% as well as between PSP/MSA and PD with up to 87%. Dysarthria severity in MSA/PSP was related to overall disease severity. Speech disorders reflect the differing underlying pathophysiology of tauopathy in PSP and α-synucleinopathy in MSA. Vocal assessment may provide a low-cost alternative screening method to existing subjective clinical assessment and imaging diagnostic approaches.

Fig. 1. Scheme chart depicting the methodology and major findings.

PC hypothesized perceptual correlates, AF acoustic features, WF weighting factor, RLR relative loudness of respiration, PIR pause intervals per respiration, RSR rate of speech respiration, DUS duration of stop consonants, stdF0 standard deviation of fundamental frequency, RFA resonant frequency attenuation, stdPSD standard deviation of the power spectral density, GVI gaping in-between voiced intervals, stdF0a pitch fluctuations, EST entropy of speech timing, RST rate of speech timing, AST acceleration of speech timing, VD vowel duration, DDKI diadochokinetic instability, NSR net speech rate, DDKR diadochokinetic rate, PSI proportion of sub-harmonic intervals, PSP progressive supranuclear palsy, MSA multiple system atrophy.

Fig. 2. Boxpolts of the distribution across groups of subsystem features.

a F_resp = respiration feature; b F_phon = phonation feature; c F_art = articulation feature; d F_pros = prosodic feature; e F_time = timing feature. HC healthy controls, PD Parkinson’s disease, MSA multiple system atrophy, PSP progressive supranuclear palsy. Statistically significant differences between groups: *p < 0.05, **p < 0.01, ***p < 0.001.

Fig. 3. Boxpolts of the distribution across groups of subsystem and dysarthria type indices.

a SSI₁ = first subsystem index; b SSI₂ = second subsystem index; c DTI₁ = first dysarthria type index; d DTI₂ = second dysarthria type index. HC healthy controls, PD Parkinson’s disease, MSA multiple system atrophy, PSP progressive supranuclear palsy. Statistically significant differences between groups: *p < 0.05, **p < 0.01, ***p < 0.001.

Fig. 4. Two-dimensional projection of all subjects over the indices.

a Using the subsystem indices SSI₁ and SSI₂, b using the dysarthria type indices DTI₁ and DTI₂. The black line is the logistic regression boundary for the classification between PSP and MSA using all data.