Computerised Analysis of hypomimia and hypokinetic dysarthria for improved diagnosis of Parkinson's disease

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU149222" target="_blank" >RIV/00216305:26220/23:PU149222 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S2405844023083834" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2405844023083834</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.heliyon.2023.e21175" target="_blank" >10.1016/j.heliyon.2023.e21175</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Computerised Analysis of hypomimia and hypokinetic dysarthria for improved diagnosis of Parkinson's disease

Popis výsledku v původním jazyce

Background and Objective: An ageing society requires easy-to-use approaches for diagnosis and monitoring of neurodegenerative disorders, such as Parkinson’s disease (PD), so that clinicians can effectively adjust a treatment policy and improve patients’ quality of life. Current methods of PD diagnosis and monitoring usually require the patients to come to a hospital, where they undergo several neurological and neuropsychological examinations. These examinations are usually time-consuming, expensive, and performed just a few times per year. Hence, this study explores the possibility of fusing computerised analysis of hypomimia and hypokinetic dysarthria (two motor symptoms manifested in the majority of PD patients) with the goal of proposing a new methodology of PD diagnosis that could be easily integrated into mHealth systems. Methods: We enrolled 73 PD patients and 46 age- and gender-matched healthy controls, who performed several speech/voice tasks while recorded by a microphone and a camera. Acoustic signals were parametrised in the fields of phonation, articulation and prosody. Video recordings of a face were analysed in terms of facial landmarks movement. Both modalities were consequently modelled by the XGBoost algorithm. Results: The acoustic analysis enabled diagnosis of PD with 77 % balanced accuracy, while in the case of the facial analysis, we observed 81 % balanced accuracy. The fusion of both modalities increased the balanced accuracy to 83 % (88 % sensitivity and 78 % specificity). The most informative speech exercise in the multimodality system turned out to be a tongue twister. Additionally, we identified muscle movements that are characteristic of hypomimia. Conclusions: The introduced methodology, which is based on the myriad of speech exercises likewise audio and video modality, allows for the detection of PD with an accuracy of up to 83 %. The speech exercise - tongue twisters occurred to be the most valuable from the clinical point of view. Addi

Název v anglickém jazyce

Computerised Analysis of hypomimia and hypokinetic dysarthria for improved diagnosis of Parkinson's disease

Popis výsledku anglicky

Background and Objective: An ageing society requires easy-to-use approaches for diagnosis and monitoring of neurodegenerative disorders, such as Parkinson’s disease (PD), so that clinicians can effectively adjust a treatment policy and improve patients’ quality of life. Current methods of PD diagnosis and monitoring usually require the patients to come to a hospital, where they undergo several neurological and neuropsychological examinations. These examinations are usually time-consuming, expensive, and performed just a few times per year. Hence, this study explores the possibility of fusing computerised analysis of hypomimia and hypokinetic dysarthria (two motor symptoms manifested in the majority of PD patients) with the goal of proposing a new methodology of PD diagnosis that could be easily integrated into mHealth systems. Methods: We enrolled 73 PD patients and 46 age- and gender-matched healthy controls, who performed several speech/voice tasks while recorded by a microphone and a camera. Acoustic signals were parametrised in the fields of phonation, articulation and prosody. Video recordings of a face were analysed in terms of facial landmarks movement. Both modalities were consequently modelled by the XGBoost algorithm. Results: The acoustic analysis enabled diagnosis of PD with 77 % balanced accuracy, while in the case of the facial analysis, we observed 81 % balanced accuracy. The fusion of both modalities increased the balanced accuracy to 83 % (88 % sensitivity and 78 % specificity). The most informative speech exercise in the multimodality system turned out to be a tongue twister. Additionally, we identified muscle movements that are characteristic of hypomimia. Conclusions: The introduced methodology, which is based on the myriad of speech exercises likewise audio and video modality, allows for the detection of PD with an accuracy of up to 83 %. The speech exercise - tongue twisters occurred to be the most valuable from the clinical point of view. Addi

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

—

Návaznosti

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 periodika

Heliyon

ISSN

2405-8440

e-ISSN

—

Svazek periodika

9

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

26

Strana od-do

„“-„“

Kód UT WoS článku

001111636000001

EID výsledku v databázi Scopus

2-s2.0-85174607876