Abstract
Background: Diagnosis of Parkinson’s disease (PD) is mainly based on motor symptoms and can be supported by imaging techniques such as the single photon emission computed tomography (SPECT) or M-iodobenzyl-guanidine cardiac scintiscan (MIBG), which are expensive and not always available. In this review, we analyzed studies that used machine learning (ML) techniques to diagnose PD through resting state or motor activation electroencephalography (EEG) tests. Methods: The review process was performed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. All publications previous to May 2020 were included, and their main characteristics and results were assessed and documented. Results: Nine studies were included. Seven used resting state EEG and two motor activation EEG. Subsymbolic models were used in 83.3% of studies. The accuracy for PD classification was 62–99.62%. There was no standard cleaning protocol for the EEG and a great heterogeneity in the characteristics that were extracted from the EEG. However, spectral characteristics predominated. Conclusions: Both the features introduced into the model and its architecture were essential for a good performance in predicting the classification. On the contrary, the cleaning protocol of the EEG, is highly heterogeneous among the different studies and did not influence the results. The use of ML techniques in EEG for neurodegenerative disorders classification is a recent and growing field.
Highlights
Parkinson’s disease (PD) is the second most common neurological disease after Alzheimer’s disease, affecting 2–3% of the population older than 65 years of age [1]
Both the features introduced into the model and its architecture were essential for a good performance in predicting the classification
As a result of this phase, 9 articles were excluded for not using machine learning (ML) techniques, 24 articles did not focus their study on PD, 27 articles did not use EEG recordings, 3 articles considered animal studies, 1 article had pharmacological interventions, 24 articles were reviews with a different purpose, 3 articles performed studies on sleep EEG recordings, 6 articles were based on EEG changes evoked by exogenous stimuli and 2 articles had incomplete descriptions of the methodology used
Summary
Parkinson’s disease (PD) is the second most common neurological disease after Alzheimer’s disease, affecting 2–3% of the population older than 65 years of age [1] It is characterized by the loss of dopaminergic neurons in the substantia nigra [2]. Used techniques include image-based tests (single photon emission computed tomography (SPECT), M-iodobenzyl-guanidine cardiac scintiscan (MIBG)), these are costly and are not always accessible. Diagnosis of Parkinson’s disease (PD) is mainly based on motor symptoms and can be supported by imaging techniques such as the single photon emission computed tomography (SPECT) or M-iodobenzyl-guanidine cardiac scintiscan (MIBG), which are expensive and not always available. We analyzed studies that used machine learning (ML) techniques to diagnose PD through resting state or motor activation electroencephalography (EEG) tests. Review the current practice and prediction accuracy of any existing models
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