Detail publikace

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

Originální název

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

Anglický název

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

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

Dokumenty

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"
}