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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| Vydané v: | Service oriented computing and applications Ročník 18; číslo 1; s. 101 - 107 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Springer London
01.03.2024
Springer Nature B.V |
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| ISSN: | 1863-2386, 1863-2394 |
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| 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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| AbstractList | 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. |
| Author | Liu, Yao Feng, Hsuan-Ming Yuan, Linlin |
| Author_xml | – sequence: 1 givenname: Linlin surname: Yuan fullname: Yuan, Linlin organization: College of Physical Education, Huaqiao University – sequence: 2 givenname: Yao surname: Liu fullname: Liu, Yao organization: College of Medicine, Huaqiao University – sequence: 3 givenname: Hsuan-Ming orcidid: 0000-0002-6498-7006 surname: Feng fullname: Feng, Hsuan-Ming email: hmfenghmfeng@gmail.com organization: Department of Computer Science and Information Engineering, National Quemoy University |
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| Cites_doi | 10.1620/tjem.250.271 10.1002/mds.10305 10.1136/jnnp.2007.131045 10.1007/s11761-022-00354-4 10.2217/nmt-2018-0021 10.1016/j.jneumeth.2020.109019 10.1007/s00521-015-2142-2 10.1016/j.bbe.2019.05.005 10.1007/s11761-022-00349-1 10.1016/j.cell.2020.03.022 10.1109/TBME.2008.2005954 10.1007/s11761-022-00352-6 10.1016/j.asoc.2018.10.022 10.1109/JSEN.2022.3177472 10.1016/j.cub.2019.02.034 10.1136/bmjopen-2017-016242 |
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| Title | Parkinson disease prediction using machine learning-based features from speech signal |
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