BERT Learns From Electroencephalograms About Parkinson's Disease: Transformer-Based Models for Aid Diagnosis

Medicine 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...

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Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.101672-101682
Main Authors: Nogales, Alberto, Garcia-Tejedor, Alvaro J., Maitin, Ana M., Perez-Morales, Antonio, Castillo, Maria Dolores Del, Romero, Juan Pablo
Format: Article
Language:English
Subjects:
BERT
Bit error rate
Brain modeling
Data analysis
Deep learning
Diagnosis
Diseases
electro-encephalography
Electroencephalography
Natural language processing
Neurological diseases
neurology
Parkinson's disease
Support vector machines
Task complexity
transformers
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Summary:Medicine 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 86%.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3201843