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dc.contributor.authorMaitín, Ana María
dc.contributor.authorGarcía Tejedor, Álvaro José 
dc.contributor.authorRomero Muñoz, Juan Pablo 
dc.date.accessioned2021-01-12T12:15:51Z
dc.date.available2021-01-12T12:15:51Z
dc.date.issued2020
dc.identifier.issn2076-3417spa
dc.identifier.urihttp://hdl.handle.net/10641/2147
dc.description.abstractBackground: 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.spa
dc.language.isoengspa
dc.publisherApplied Sciencesspa
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectParkinson’s diseasespa
dc.subjectElectroencephalographyspa
dc.subjectMachine learningspa
dc.titleMachine learning approaches for detecting parkinson’s disease from EEG analysis: a systematic review.spa
dc.typejournal articlespa
dc.type.hasVersionAMspa
dc.rights.accessRightsopen accessspa
dc.description.extent1,30 MBspa
dc.identifier.doi10.3390/app10238662spa
dc.relation.publisherversionhttps://www.mdpi.com/2076-3417/10/23/8662spa


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