Research on Passive Assessment of Parkinson's Disease Utilising Speech Biomarkers
Identifikátory výsledku
Kód výsledku v 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>
Výsledek na webu
<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>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Research on Passive Assessment of Parkinson's Disease Utilising Speech Biomarkers
Popis výsledku v původním jazyce
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.
Název v anglickém jazyce
Research on Passive Assessment of Parkinson's Disease Utilising Speech Biomarkers
Popis výsledku anglicky
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.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
20601 - Medical engineering
Návaznosti výsledku
Projekt
<a href="/cs/project/LX22NPO5107" target="_blank" >LX22NPO5107: Národní ústav pro neurologický výzkum</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2023
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
PERVASIVE COMPUTING TECHNOLOGIES FOR HEALTHCARE, PERVASIVEHEALTH 2022
ISBN
978-3-031-34585-2
ISSN
1867-8211
e-ISSN
1867-822X
Počet stran výsledku
15
Strana od-do
259-273
Název nakladatele
SPRINGER INTERNATIONAL PUBLISHING AG
Místo vydání
CHAM
Místo konání akce
Thessaloniki
Datum konání akce
12. 12. 2022
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
001436746400018