An Interpretable Machine Learning Method for the Detection of Schizophrenia Using EEG Signals

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dc.contributor.author Vázquez López, Manuel Alberto
dc.contributor.author Maghsoudi, Arash
dc.contributor.author Pérez Mariño, Inés
dc.date.accessioned 2022-03-01T10:56:14Z
dc.date.available 2022-03-01T10:56:14Z
dc.date.issued 2021-05-28
dc.identifier.bibliographicCitation Vázquez, M. A., Maghsoudi, A., & Mariño, I. P. (2021). An Interpretable Machine Learning Method for the Detection of Schizophrenia Using EEG Signals. Frontiers in Systems Neuroscience, 15.
dc.identifier.uri http://hdl.handle.net/10016/34269
dc.description.abstract In this work we propose a machine learning (ML) method to aid in the diagnosis of schizophrenia using electroencephalograms (EEGs) as input data. The computational algorithm not only yields a proposal of diagnostic but, even more importantly, it provides additional information that admits clinical interpretation. It is based on an ML model called random forest that operates on connectivity metrics extracted from the EEG signals. Specifically, we use measures of generalized partial directed coherence (GPDC) and direct directed transfer function (dDTF) to construct the input features to the ML model. The latter allows the identification of the most performance-wise relevant features which, in turn, provide some insights about EEG signals and frequency bands that are associated with schizophrenia. Our preliminary results on real data show that signals associated with the occipital region seem to play a significant role in the diagnosis of the disease. Moreover, although every frequency band might yield useful information for the diagnosis, the beta and theta (frequency) bands provide features that are ultimately more relevant for the ML classifier that we have implemented.
dc.description.sponsorship We acknowledge support by the Agencia Estatal de Investigación of Spain (CAIMAN, reference TEC2017-86921-C2-1-R and CLARA, reference RTI2018-099655-B-I00) and by the grant of the Ministry of Education and Science of the Russian Federation Agreement No. 074-02-2018-330.
dc.format.extent 11
dc.language.iso eng
dc.publisher Frontiers Media
dc.rights © 2021 Vázquez, Maghsoudi and Mariño.
dc.rights Atribución 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by/3.0/es/
dc.subject.other Connectivity
dc.subject.other Direct directed transfer function
dc.subject.other Electroencephalography
dc.subject.other Generalized partial directed coherence
dc.subject.other Machine learning
dc.subject.other Random forest
dc.subject.other Schizophrenia
dc.title An Interpretable Machine Learning Method for the Detection of Schizophrenia Using EEG Signals
dc.type article
dc.subject.eciencia Biología y Biomedicina
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.3389/fnsys.2021.652662
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. RTI2018-099655-B-I00
dc.relation.projectID Gobierno de España. TEC2017-86921-C2-1-R
dc.type.version publishedVersion
dc.identifier.publicationfirstpage 1
dc.identifier.publicationlastpage 11
dc.identifier.publicationtitle Frontiers in Systems Neuroscience
dc.identifier.publicationvolume 15
dc.identifier.uxxi AR/0000028654
dc.contributor.funder Agencia Estatal de Investigación (España)
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