Publication detail

Degree of Parkinson’s Disease Severity Estimation Based on Speech Signal Processing

GALÁŽ, Z. MEKYSKA, J. MŽOUREK, Z. KISKA, T. SMÉKAL, Z. REKTOROVÁ, I.

Original Title

Degree of Parkinson’s Disease Severity Estimation Based on Speech Signal Processing

English Title

Degree of Parkinson’s Disease Severity Estimation Based on Speech Signal Processing

Type

conference paper

Language

en

Original Abstract

This paper deals with Parkinson’s disease (PD) severity estimation according to the Unified Parkinson’s Disease Rating Scale: motor subscale (UPDRS III), which quantifies the hallmark symptoms of PD, using an acoustic analysis of speech signals. Experimental dataset comprised 42 speech tasks acquired from 50 PD patients (UPDRS III ranged from 6 to 92). It was divided into subsets: words, sentences, reading text, monologue and diadochokinetic tasks. We performed a parametrization of the whole corpus and these groups separately using a wide range of conventional and novel speech features. We used guided regularized random forest algorithm to select features with maximum clinical information and performed random forests regression to estimate PD severity. According to significant correlations between true UPDRS III scores and scores predicted by the proposed methodology it was shown information extracted through variety of speech tasks can be used to estimate PD severity. of PD severity.

English abstract

This paper deals with Parkinson’s disease (PD) severity estimation according to the Unified Parkinson’s Disease Rating Scale: motor subscale (UPDRS III), which quantifies the hallmark symptoms of PD, using an acoustic analysis of speech signals. Experimental dataset comprised 42 speech tasks acquired from 50 PD patients (UPDRS III ranged from 6 to 92). It was divided into subsets: words, sentences, reading text, monologue and diadochokinetic tasks. We performed a parametrization of the whole corpus and these groups separately using a wide range of conventional and novel speech features. We used guided regularized random forest algorithm to select features with maximum clinical information and performed random forests regression to estimate PD severity. According to significant correlations between true UPDRS III scores and scores predicted by the proposed methodology it was shown information extracted through variety of speech tasks can be used to estimate PD severity. of PD severity.

Keywords

hypokinetic dysarthria; Parkinson’s disease; regression; severity estimation; speech processing

Released

29.06.2016

ISBN

978-1-5090-1287-9

Book

Proceedings of the 39th International Conference on Telecommunication and Signal Processing, TSP 2016

Edition

1

Edition number

1

Pages from

503

Pages to

507

Pages count

4

BibTex


@inproceedings{BUT126644,
  author="Zoltán {Galáž} and Jiří {Mekyska} and Zdeněk {Mžourek} and Tomáš {Kiska} and Zdeněk {Smékal} and Irena {Rektorová}",
  title="Degree of Parkinson’s Disease Severity Estimation Based on Speech Signal Processing",
  annote="This paper deals with Parkinson’s disease (PD) severity estimation according to the Unified Parkinson’s Disease
Rating Scale: motor subscale (UPDRS III), which quantifies the hallmark symptoms of PD, using an acoustic analysis of speech  signals. Experimental dataset comprised 42 speech tasks acquired from 50 PD patients (UPDRS III ranged from 6 to 92). It was divided into subsets: words, sentences, reading text, monologue and diadochokinetic tasks. We performed a parametrization of the whole corpus and these groups separately using a wide range of conventional and novel speech features. We used guided regularized random forest algorithm to select features with maximum clinical information and performed random forests regression to estimate PD severity. According to significant correlations between true UPDRS III scores and scores predicted by the proposed methodology it was shown information extracted through variety of speech tasks can be used to estimate PD severity.
of PD severity.",
  booktitle="Proceedings of the 39th International Conference on Telecommunication and Signal Processing, TSP 2016",
  chapter="126644",
  edition="1",
  howpublished="online",
  year="2016",
  month="june",
  pages="503--507",
  type="conference paper"
}