Feature Selection Improves Speech Based Parkinson's Disease Detection Performance

Ayşe Tekindor, Eda Aydın

2024

Abstract

Parkinson’s disease (PD) is a neurodegenerative disorder that is caused by decrease in dopamine levels in the brain. There is currently no cure for PD; however, the progression of the disease can be brought under control by diagnosis made in early stages. Studies have shown that speech impairments are early symptoms of PD. In this study, an approach for the early diagnosis of patients with PD using speech based features was proposed. In order to detect the PD, four feature groups such as Bark Spectrum coefficients, Mel Frequency Cepstral Coefficients (MFCCs), Gammatone Cepstral Coefficients (GTCCs), and Spectral-Temporal Features were created. Minimum Redundancy Maximum Relevance (mRMR) based feature selection was applied to each feature group. Three classifiers including decision tree, Naive Bayes, and support vector machine were employed to evaluate the performance of the feature sets. The proposed method was validated on the Italian speech dataset. Feature selection improved the PD diagnosing performance, especially for the Naive Bayes model which obtained 96.01% accuracy by overall feature selection and 96.17% by group-based feature selection.

Paper Citation

in Harvard Style

Tekindor A. and Aydın E. (2024). Feature Selection Improves Speech Based Parkinson's Disease Detection Performance. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS; ISBN 978-989-758-688-0, SciTePress, pages 726-732. DOI: 10.5220/0012347300003657

in Bibtex Style

@conference{biosignals24,
author={Ayşe Tekindor and Eda Aydın},
title={Feature Selection Improves Speech Based Parkinson's Disease Detection Performance},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS},
year={2024},
pages={726-732},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012347300003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS
TI - Feature Selection Improves Speech Based Parkinson's Disease Detection Performance
SN - 978-989-758-688-0
AU - Tekindor A.
AU - Aydın E.
PY - 2024
SP - 726
EP - 732
DO - 10.5220/0012347300003657
PB - SciTePress