Speech features-based Parkinson’s disease classification using combined SMOTE-ENN and binary machine learning
Author:
Publisher
Springer Science and Business Media LLC
Subject
Biomedical Engineering,Applied Microbiology and Biotechnology,Bioengineering,Biotechnology
Link
https://link.springer.com/content/pdf/10.1007/s12553-023-00810-x.pdf
Reference55 articles.
1. Khoshnevis SA, Sankar R. Diagnosis of Parkinson's disease using higher order statistical analysis of alpha and beta rhythms. Biomed Signal Process Control. 2022;77. https://doi.org/10.1016/j.bspc.2022.103743.
2. Junaid M, Ali S, Eid F, El-Sappagh S, Abuhmed T. Explainable machine learning models based on multimodal time-series data for the early detection of Parkinson’s disease. Comput Methods Programs Biomed. 2023;234:107495. https://doi.org/10.1016/j.cmpb.2023.107495.
3. Di Lazzaro G, Ricci M, Al-Wardat M, Schirinzi T, Scalise S, Giannini F, Mercuri NB, Saggio G, Pisani A. Technology-Based Objective Measures Detect Subclinical Axial Signs in Untreated, de novo Parkinson’s Disease. J Parkinsons Dis. 2020;10(1):113–22. https://doi.org/10.3233/JPD-191758.
4. Ghaderyan P, Ghoreshi Beyrami SM. Neurodegenerative diseases detection using distance metrics and sparse coding: A new perspective on gait symmetric features. Comput Biol Med. 2020;120. https://doi.org/10.1016/j.compbiomed.2020.103736.
5. Farashi S. Distinguishing between Parkinson's disease patients and healthy individuals using a comprehensive set of time, frequency and time-frequency features extracted from vertical ground reaction force data. Biomed Signal Process Control 2020;62.
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