Abstract
Parkinson's disease (PD) is a prevalent neurodegenerative disorder that has prompted the development of telediagnosis and remote monitoring systems. Dysphonia, a common symptom in the early stages of PD, affects approximately 90% of patients. Therefore, testing for persistent pronunciation or dysphonia in continuous speech can aid in the diagnosis of PD. Our study utilized speech signals from 252 subjects as the dataset. In this study, language signal features were used as input to machine learning algorithms, and the resulting classifiers were integrated to improve accuracy in the classification of Parkinson's disease (PD). The experimental results demonstrated a diagnostic accuracy of up to 95% using these machine learning algorithms. Additionally, a method of feature extraction based on clinical experience was presented for analyzing subjects' language signals.
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Acknowledgements
This work was partly supported by the Quanzhou Science and Technology Major Project under Grant No. 2021GZ1; the National Natural Science Foundation of Fujian under Grant No. 2021J011404; and the Quanzhou scientific and technological planning projects under Grant Nos. 2021C037R and 2019C028R.
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Yuan, L., Liu, Y. & Feng, HM. Parkinson disease prediction using machine learning-based features from speech signal. SOCA 18, 101–107 (2024). https://doi.org/10.1007/s11761-023-00372-w
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DOI: https://doi.org/10.1007/s11761-023-00372-w