Machine Learning Approaches for Detecting Parkinson’s Disease from EEG Analysis

A research paper has recently been published in the Applied Sciences journal by Ana M. Maitín López and Álvaro J. García Tejedor, members of the CEIEC research group, and Juan Pablo Romero Muñoz, professor at the UFV and member of the Brain Damage Unit of the Hospital Beata María Ana.

In the article, those publications focused on the diagnosis of Parkinson’s Disease through the analysis of EEG by means of Machine Learning techniques were systematically reviewed. Different parameters were analyzed, such as the age of the patients, the values of the HY and UPDRS scales, the cleaning protocol of the EEG, the features extracted from the EEG, the Machine Learning model used, its architecture, and its performance.

The paper can be found in this link.

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