Assessing progress of Parkinson's disease using acoustic analysis of phonation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F15%3APU115151" target="_blank" >RIV/00216305:26220/15:PU115151 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/7160153?reload=true&arnumber=7160153" target="_blank" >https://ieeexplore.ieee.org/document/7160153?reload=true&arnumber=7160153</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IWOBI.2015.7160153" target="_blank" >10.1109/IWOBI.2015.7160153</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Assessing progress of Parkinson's disease using acoustic analysis of phonation

Popis výsledku v původním jazyce

This paper deals with a complex acoustic analysis of phonation in patients with Parkinson's disease (PD) with a special focus on estimation of disease progress that is described by 7 different clinical scales (e. g. Unified Parkinson's disease rating scale or Beck depression inventory). The analysis is based on parametrization of 5 Czech vowels pronounced by 84 PD patients. Using classification and regression trees we estimated all clinical scores with maximal error lower or equal to 13 %. Best estimation was observed in the case of Mini-mental state examination (MAE = 0.77, estimation error 5.50 %). Finally, we proposed a binary classification based on random forests that is able to identify Parkinson's disease with sensitivity SEN = 92.86% (SPE = 85.71 %). The parametrization process was based on extraction of 107 speech features quantifying different clinical signs of hypokinetic dysarthria present in PD.

Název v anglickém jazyce

Assessing progress of Parkinson's disease using acoustic analysis of phonation

Popis výsledku anglicky

This paper deals with a complex acoustic analysis of phonation in patients with Parkinson's disease (PD) with a special focus on estimation of disease progress that is described by 7 different clinical scales (e. g. Unified Parkinson's disease rating scale or Beck depression inventory). The analysis is based on parametrization of 5 Czech vowels pronounced by 84 PD patients. Using classification and regression trees we estimated all clinical scores with maximal error lower or equal to 13 %. Best estimation was observed in the case of Mini-mental state examination (MAE = 0.77, estimation error 5.50 %). Finally, we proposed a binary classification based on random forests that is able to identify Parkinson's disease with sensitivity SEN = 92.86% (SPE = 85.71 %). The parametrization process was based on extraction of 107 speech features quantifying different clinical signs of hypokinetic dysarthria present in PD.

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

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

2015 4th International Work Conference on Bioinspired Intelligence (IWOBI)

ISBN

978-1-4673-7845-1

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

111-118

Název nakladatele

Neuveden

Místo vydání

Neuveden

Místo konání akce

San Sebastian

Datum konání akce

10. 6. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000380501500017