Research on Passive Assessment of Parkinson's Disease Utilising Speech Biomarkers
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F23%3A00082535" target="_blank" >RIV/00159816:_____/23:00082535 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-031-34586-9_18" target="_blank" >http://dx.doi.org/10.1007/978-3-031-34586-9_18</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-34586-9_18" target="_blank" >10.1007/978-3-031-34586-9_18</a>
Alternative languages
Result language
angličtina
Original language name
Research on Passive Assessment of Parkinson's Disease Utilising Speech Biomarkers
Original language description
Speech disorders, collectively referred to as hypokinetic dysarthria (HD), are early biomarkers of Parkinson's disease (PD). To assess all dimensions of HD, patients could perform several speech tasks using a smartphone outside a clinic. This paper aims to adapt the parametrization process to running speech so that a patient is not required to interact actively with the device, and features can be extracted directly from phone calls. The method utilizes a voice activity detector followed by a voicing detection. The algorithm was tested on a database of 126 recordings (86 patients with PD and 40 healthy controls) of monologue mixed with noise with different signal-to-noise ratios (SNR) to simulate the real environment conditions. Pearson correlation coefficients show a strong linear relationship between speech features and patients' scores assessing HD and other motor/non-motor symptoms - p-value < 0.01 for the normalized amplitude quotient (NAQ) with Test 3F Dysarthric Profile (DX index) and Unified Parkinson's Disease Rating Scale (part III) in 20 dB SNR conditions, p-value < 0.01 for the jitter and shimmer with the Mini Mental State Exam (10 dB SNR). A model based on the Extreme Gradient Boosting algorithm predicts the DX index with a 10.83% estimated error rate (EER) and the Addenbrooke's Cognitive Examination-Revise (ACE-R) score with 13.38% EER. The introduced algorithm can potentially be used in mHealth applications for passive monitoring and assessment of PD patients.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
20601 - Medical engineering
Result continuities
Project
<a href="/en/project/LX22NPO5107" target="_blank" >LX22NPO5107: National institute for Neurological Research</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
PERVASIVE COMPUTING TECHNOLOGIES FOR HEALTHCARE, PERVASIVEHEALTH 2022
ISBN
978-3-031-34585-2
ISSN
1867-8211
e-ISSN
1867-822X
Number of pages
15
Pages from-to
259-273
Publisher name
SPRINGER INTERNATIONAL PUBLISHING AG
Place of publication
CHAM
Event location
Thessaloniki
Event date
Dec 12, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001436746400018