Publication detail

IDENTIFICATION OF PARKINSON’S DISEASE USING ACOUSTIC ANALYSIS OF POEM RECITATION

MUCHA, J.

Original Title

IDENTIFICATION OF PARKINSON’S DISEASE USING ACOUSTIC ANALYSIS OF POEM RECITATION

English Title

IDENTIFICATION OF PARKINSON’S DISEASE USING ACOUSTIC ANALYSIS OF POEM RECITATION

Type

conference paper

Language

en

Original Abstract

Parkinson’s disease (PD) is the second most frequent neurodegenerative disorder. It is estimated that 60–90% of PD patients suffer from speech disorder called hypokinetic dysarthria (HD). The goal of this work is to reveal influence of poem recitation on acoustic analysis of speech and propose concept of Parkinson’s disease identification based on this analysis. Classification methods used in this work are Random Forests and Support Vector Machine. The best achieved accuracy of disease identification is 70.66% with 59.25% sensitivity for Random Forests classifier fed mainly with articulation features. These results demonstrate a high potential of research in this area.

English abstract

Parkinson’s disease (PD) is the second most frequent neurodegenerative disorder. It is estimated that 60–90% of PD patients suffer from speech disorder called hypokinetic dysarthria (HD). The goal of this work is to reveal influence of poem recitation on acoustic analysis of speech and propose concept of Parkinson’s disease identification based on this analysis. Classification methods used in this work are Random Forests and Support Vector Machine. The best achieved accuracy of disease identification is 70.66% with 59.25% sensitivity for Random Forests classifier fed mainly with articulation features. These results demonstrate a high potential of research in this area.

Keywords

poem recitation, acoustic analysis, binary classification, Parkinson’s disease, hypokinetic dysarthria

Released

27.04.2017

Location

Brno

ISBN

978-80-214-5496-5

Book

Proceedings of the 23nd Conference STUDENT EEICT 2017

Pages from

619

Pages to

623

Pages count

5

BibTex


@inproceedings{BUT135620,
  author="Ján {Mucha}",
  title="IDENTIFICATION OF PARKINSON’S DISEASE USING ACOUSTIC ANALYSIS OF POEM RECITATION",
  annote="Parkinson’s disease (PD) is the second most frequent neurodegenerative disorder. It is estimated
that 60–90% of PD patients suffer from speech disorder called hypokinetic dysarthria (HD).
The goal of this work is to reveal influence of poem recitation on acoustic analysis of speech and propose
concept of Parkinson’s disease identification based on this analysis. Classification methods
used in this work are Random Forests and Support Vector Machine. The best achieved accuracy of
disease identification is 70.66% with 59.25% sensitivity for Random Forests classifier fed mainly
with articulation features. These results demonstrate a high potential of research in this area.",
  booktitle="Proceedings of the 23nd Conference STUDENT EEICT 2017",
  chapter="135620",
  howpublished="electronic, physical medium",
  year="2017",
  month="april",
  pages="619--623",
  type="conference paper"
}