Advances in a Multimodal Approach for Dysphagia Analysis Based on Automatic Voice Analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F16%3APU119421" target="_blank" >RIV/00216305:26220/16:PU119421 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-319-33747-0_20" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-319-33747-0_20</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-33747-0_20" target="_blank" >10.1007/978-3-319-33747-0_20</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Advances in a Multimodal Approach for Dysphagia Analysis Based on Automatic Voice Analysis

Popis výsledku v původním jazyce

Parkinson’s disease (PD) is the second most frequent neurodegenerative disease with prevalence among general population reaching 0.1–1 %, and an annual incidence between 1.3–2.0/10,000 inhabitants. The most obvious symptoms are movement-related such as tremor, rigidity, slowness of movement and walking difficulties and frequently these are the symptoms that lead to the PD diagnoses but also they could have dysphagia. In this sense voice analysis is a safe, non-invasive, and reliable screening procedure for PD patients with dysphagia, which could detect patients at high risk of clinically significant aspiration. In this paper we will present a part of an ongoing project that will evaluate automatic speech analysis based on linear and non-linear features. These can be reliable predictors/indicators of swallowing and balance impairments in PD. An important advantage of voice analysis is its low intrusiveness and easy implementation in clinical practice. Thus, if a significant correlation between these simple analyses and the gold standard video-fluoroscopic analysis will imply simpler and less stressing diagnostic test for the patients as well as the use of cheaper analysis systems.

Název v anglickém jazyce

Advances in a Multimodal Approach for Dysphagia Analysis Based on Automatic Voice Analysis

Popis výsledku anglicky

Parkinson’s disease (PD) is the second most frequent neurodegenerative disease with prevalence among general population reaching 0.1–1 %, and an annual incidence between 1.3–2.0/10,000 inhabitants. The most obvious symptoms are movement-related such as tremor, rigidity, slowness of movement and walking difficulties and frequently these are the symptoms that lead to the PD diagnoses but also they could have dysphagia. In this sense voice analysis is a safe, non-invasive, and reliable screening procedure for PD patients with dysphagia, which could detect patients at high risk of clinically significant aspiration. In this paper we will present a part of an ongoing project that will evaluate automatic speech analysis based on linear and non-linear features. These can be reliable predictors/indicators of swallowing and balance impairments in PD. An important advantage of voice analysis is its low intrusiveness and easy implementation in clinical practice. Thus, if a significant correlation between these simple analyses and the gold standard video-fluoroscopic analysis will imply simpler and less stressing diagnostic test for the patients as well as the use of cheaper analysis systems.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

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

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in Neural Networks

ISBN

978-3-319-33746-3

ISSN

—

e-ISSN

—

Počet stran výsledku

11

Strana od-do

201-211

Název nakladatele

Springer International Publishing

Místo vydání

Switzerland

Místo konání akce

Vietri sul Mare

Datum konání akce

20. 5. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000426341600020