Parkinson disease prediction using machine learning-based features from speech signal

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 d...

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Veröffentlicht in:Service oriented computing and applications Jg. 18; H. 1; S. 101 - 107
Hauptverfasser: Yuan, Linlin, Liu, Yao, Feng, Hsuan-Ming
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer London 01.03.2024
Springer Nature B.V
Schlagworte:
Acoustics
Algorithms
Archives & records
Computer Appl. in Administrative Data Processing
Computer Science
Computer Systems Organization and Communication Networks
Datasets
Disease
Dopamine
e-Commerce/e-business
Feature extraction
IT in Business
Machine learning
Management of Computing and Information Systems
Neural networks
Pandemics
Parkinson's disease
Patients
Remote monitoring
Software Engineering/Programming and Operating Systems
Special Issue Paper
Speech
Telemedicine
Tremor (Muscular contraction)
Wavelet transforms
Parkinson disease
Speech signal
Machine learning
ISSN:1863-2386, 1863-2394
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1863-2386
1863-2394
DOI:10.1007/s11761-023-00372-w