Research on Passive Assessment of Parkinson’s Disease Utilising Speech Biomarkers

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU148280" target="_blank" >RIV/00216305:26220/23:PU148280 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-34586-9_18" target="_blank" >https://link.springer.com/chapter/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>

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Pervasive Computing Technologies for Healthcare

ISBN

978-3-031-34586-9

ISSN

—

e-ISSN

—

Počet stran výsledku

15

Strana od-do

259-273

Název nakladatele

Springer Nature

Místo vydání

Switzerland

Místo konání akce

Soluň

Datum konání akce

12. 12. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—