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dc.contributor.authorNogales Moyano, Alberto 
dc.contributor.authorGarcía Tejedor, Álvaro José 
dc.contributor.authorMaitín, Ana María
dc.contributor.authorPérez Morales, Antonio
dc.contributor.authorDel Castillo, María Dolores
dc.contributor.authorRomero Muñoz, Juan Pablo
dc.description.abstractMedicine is a complex field with highly trained specialists with extensive knowledge that continuously needs updating. Among them all, those who study the brain can perform complex tasks due to the structure of this organ. There are neurological diseases such as degenerative ones whose diagnoses are essential in very early stages. Parkinson’s disease is one of them, usually having a confirmed diagnosis when it is already very developed. Some physicians have proposed using electroencephalograms as a non-invasive method for a prompt diagnosis. The problem with these tests is that data analysis relies on the clinical eye of a very experienced professional, which entails situations that escape human perception. This research proposes the use of deep learning techniques in combination with electroencephalograms to develop a non-invasive method for Parkinson’s disease diagnosis. These models have demonstrated their good performance in managing massive amounts of data. Our main contribution is to apply models from the field of Natural Language Processing, particularly an adaptation of BERT models, for being the last milestone in the area. This model choice is due to the similarity between texts and electroencephalograms that can be processed as data sequences. Results show that the best model uses electroencephalograms of 64 channels from people without resting states and finger-tapping tasks. In terms of metrics, the model has values around
dc.publisherIEEE Accessspa
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.subjectDeep learningspa
dc.subjectParkinson's diseasespa
dc.titleBERT Learns From Electroencephalograms About Parkinson's Disease: Transformer-Based Models for Aid
dc.description.extent1134 KBspa

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Atribución-NoComercial-SinDerivadas 3.0 España
Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 3.0 España