Journal
APPLIED SCIENCES-BASEL
Volume 12, Issue 14, Pages -Publisher
MDPI
DOI: 10.3390/app12146967
Keywords
machine learning; deep learning; artificial neural networks; electroencephalogram; Parkinson's disease; review
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This article reviewed studies using machine learning techniques to study the EEG of patients with PD, including 59 studies. The results showed that Support Vector Machine and Artificial Neural Networks were the predominant models, with 31 articles diagnosing PD with an accuracy of 97.35%. There was significant heterogeneity in EEG characteristics, although spectral features predominated.
Background: Parkinson's disease (PD) affects 7-10 million people worldwide. Its diagnosis is clinical and can be supported by image-based tests, which are expensive and not always accessible. Electroencephalograms (EEG) are non-invasive, widely accessible, low-cost tests. However, the signals obtained are difficult to analyze visually, so advanced techniques, such as Machine Learning (ML), need to be used. In this article, we review those studies that consider ML techniques to study the EEG of patients with PD. Methods: The review process was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, which are used to provide quality standards for the objective evaluation of various studies. All publications before February 2022 were included, and their main characteristics and results were evaluated and documented through three key points associated with the development of ML techniques: dataset quality, data preprocessing, and model evaluation. Results: 59 studies were included. The predominating models were Support Vector Machine (SVM) and Artificial Neural Networks (ANNs). In total, 31 articles diagnosed PD with a mean accuracy of 97.35 +/- 3.46%. There was no standard cleaning protocol for EEG and a great heterogeneity in EEG characteristics was shown, although spectral features predominated by 88.37%. Conclusions: Neither the cleaning protocol nor the number of EEG channels influenced the classification results. A baseline value was provided for the PD diagnostic problem, although recent studies focus on the identification of cognitive impairment.
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