Speech acoustic indices for differential diagnosis between Parkinson's disease, multiple system atrophy and progressive supranuclear palsy
Status PubMed-not-MEDLINE Jazyk angličtina Země Spojené státy americké Médium electronic
Typ dokumentu časopisecké články
Grantová podpora
ANR-16-CE19-0010-01
Agence Nationale de la Recherche (French National Research Agency)
ANR-16-CE19-0010-01
Agence Nationale de la Recherche (French National Research Agency)
NV19-04-00120
Ministerstvo Zdravotnictví Ceské Republiky (Ministry of Health of the Czech Republic)
NV19-04-00120
Ministerstvo Zdravotnictví Ceské Republiky (Ministry of Health of the Czech Republic)
PROGRES Q27/1LF and MH-CZ-DRO-VFN64165
Univerzita Karlova v Praze (Charles University)
PubMed
36302780
PubMed Central
PMC9613976
DOI
10.1038/s41531-022-00389-6
PII: 10.1038/s41531-022-00389-6
Knihovny.cz E-zdroje
- Publikační typ
- časopisecké články MeSH
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.
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Digital speech biomarkers can measure acute effects of levodopa in Parkinson's disease