Entropy-Based Machine Learning Model for Fast Diagnosis and Monitoring of Parkinson’s Disease

This study presents the concept of a computationally efficient machine learning (ML) model for diagnosing and monitoring Parkinson’s disease (PD) using rest-state EEG signals (rs-EEG) from 20 PD subjects and 20 normal control (NC) subjects at a sampling rate of 128 Hz. Based on the comparative analy...

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Detalles Bibliográficos
Autores principales: Belyaev, Maksim, Murugappan, Murugappan, Velichko, Andrei, Korzun, Dmitry
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10610702/
https://www.ncbi.nlm.nih.gov/pubmed/37896703
http://dx.doi.org/10.3390/s23208609

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10610702/
https://www.ncbi.nlm.nih.gov/pubmed/37896703
http://dx.doi.org/10.3390/s23208609

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