skip to main content
10.1145/3605098.3636135acmconferencesArticle/Chapter ViewAbstractPublication PagessacConference Proceedingsconference-collections
poster

Analysis of voice recordings features for Classification of Parkinson's Disease

Published: 21 May 2024 Publication History

Abstract

Parkinson's disease (PD) is a chronic neurodegenerative disease. Motor symptoms are very mild in the early stages, making diagnosis difficult. Recent studies have shown that the use of patient voice recordings can aid in early diagnosis. Although the analysis of such recordings is costly from a clinical perspective, machine learning techniques are making their processing increasingly accurate and efficient. Voice recordings contain many features, but it is unknown which ones are relevant to the diagnosis of this disease. This paper proposes the use of machine learning models combined with feature selection methods for the classification of PD patients. The results show that this approach is appropriate since it drastically reduces the number of features maintining high classification performance.

References

[1]
H. Gunduz. 2019. Deep learning-based Parkinson's disease classification using vocal feature sets. IEEE Access 7 (2019), 115540--115551.
[2]
M. Hoq, M. N. Uddin, and S.-B. Park. 2021. Vocal feature extraction-based artificial intelligent model for Parkinson's disease detection. Diagnostics 11, 6 (2021), 1076.
[3]
C. O. Sakar, G. Serbes, A. Gunduz, H. C. Tunc, H. Nizam, B. E. Sakar, M. Tutuncu, T. Aydin, M. E. Isenkul, and H. Apaydin. 2019. A comparative analysis of speech signal processing algorithms for Parkinson's disease classification and the use of the tunable Q-factor wavelet transform. Applied Soft Computing 74 (2019), 255--263.
[4]
G. Solana-Lavalle and R. Rosas-Romero. 2021. Analysis of voice as an assisting tool for detection of Parkinson's disease and its subsequent clinical interpretation. Biomedical Signal Processing and Control 66 (2021), 102415.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
SAC '24: Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing
April 2024
1898 pages
ISBN:9798400702433
DOI:10.1145/3605098
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 May 2024

Check for updates

Author Tags

  1. parkinson disease
  2. classification
  3. feature selection
  4. artificial neural networks
  5. support vector machines
  6. voice recordings

Qualifiers

  • Poster

Funding Sources

  • National Plan for Scientific and Technical Research and Innovation of the Spanish Government
  • Xunta de Galicia
  • Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia

Conference

SAC '24
Sponsor:

Acceptance Rates

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

Upcoming Conference

SAC '25
The 40th ACM/SIGAPP Symposium on Applied Computing
March 31 - April 4, 2025
Catania , Italy

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 40
    Total Downloads
  • Downloads (Last 12 months)40
  • Downloads (Last 6 weeks)4
Reflects downloads up to 20 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media