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Machine learning techniques with ECG and EEG data: an exploratory study

2020Journal article Open access
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dc.contributor.authorPonciano, Vasco Rafael Gaspar
dc.contributor.authorPires, Ivan M.
dc.contributor.authorRibeiro, Fernando Reinaldo
dc.contributor.authorGarcia, Nuno M.
dc.contributor.authorVillasana, María Vanessa
dc.contributor.authorLameski, Petre
dc.contributor.authorZdravevski, Eftim
dc.date.accessioned2020-07-07T08:59:24Z
dc.date.available2020-07-07T08:59:24Z
dc.date.issued2020
dc.description.abstractElectrocardiography (ECG) and electroencephalography (EEG) are powerful tools in medicine for the analysis of various diseases. The emergence of affordable ECG and EEG sensors and ubiquitous mobile devices provides an opportunity to make such analysis accessible to everyone. In this paper, we propose the implementation of a neural network-based method for the automatic identification of the relationship between the previously known conditions of older adults and the different features calculated from the various signals. The data were collected using a smartphone and low-cost ECG and EEG sensors during the performance of the timed-up and go test. Different patterns related to the features extracted, such as heart rate, heart rate variability, average QRS amplitude, average R-R interval, and average R-S interval from ECG data, and the frequency and variability from the EEG data were identified. A combination of these parameters allowed us to identify the presence of certain diseases accurately. The analysis revealed that the different institutions and ages were mainly identified. Still, the various diseases and groups of diseases were difficult to recognize, because the frequency of the different diseases was rare in the considered population. Therefore, the test should be performed with more people to achieve better results.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationPONCIANO, Vasco [et al.] (2020) - Machine learning techniques with ECG and EEG data: an exploratory study. Computers. ISSN 2073-431X . Vol. 9, nº. 3, p. 55. DOI: https://doi.org/10.3390/computers9030055pt_PT
dc.identifier.doihttps://doi.org/10.3390/computers9030055pt_PT
dc.identifier.issn2073-431X
dc.identifier.urihttp://hdl.handle.net/10400.11/7179
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationUIDB/EEA/50008/2020.pt_PT
dc.relation.publisherversionhttps://www.mdpi.com/2073-431X/9/3/55pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectArtificial intelligencept_PT
dc.subjectElectrocardiographypt_PT
dc.subjectElectroencephalographypt_PT
dc.subjectFeature extractionpt_PT
dc.subjectRecognition of diseasespt_PT
dc.titleMachine learning techniques with ECG and EEG data: an exploratory studypt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue3pt_PT
oaire.citation.titleComputerspt_PT
oaire.citation.volume9pt_PT
person.familyNameSerrano Pires
person.familyNameReinaldo Silva Garcia Ribeiro
person.familyNameGarcia dos Santos
person.givenNameIvan Miguel
person.givenNameFernando
person.givenNameNuno Manuel
person.identifier.ciencia-id211D-8B3D-0131
person.identifier.ciencia-id7B1C-D761-291D
person.identifier.ciencia-idE719-0DEC-9751
person.identifier.orcid0000-0002-3394-6762
person.identifier.orcid0000-0002-1225-3844
person.identifier.orcid0000-0002-3195-3168
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication53c92dfa-27be-4305-9b32-f3cf2e36a4f1
relation.isAuthorOfPublication165761b1-f958-4c13-b53f-ef0a4dde1d97
relation.isAuthorOfPublicationc3c2a619-5f2a-42ae-8ee2-f21b9c42a33a
relation.isAuthorOfPublication.latestForDiscovery53c92dfa-27be-4305-9b32-f3cf2e36a4f1

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