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dc.contributor.authorBoljanić, Tanja
dc.contributor.authorMiljković, Nadica
dc.contributor.authorLazarevic, Ljiljana B.
dc.contributor.authorKnezevic, Goran
dc.contributor.authorMilašinović, Goran
dc.date.accessioned2021-12-06T15:53:26Z
dc.date.available2021-12-06T15:53:26Z
dc.date.issued2022
dc.identifier.citationBoljanić, Tanja, Nadica Miljković, Ljiljana B. Lazarevic, Goran Knezevic, and Goran Milašinović. “Relationship Between Electrocardiogram‐based Features and Personality Traits: Machine Learning Approach.” Annals of Noninvasive Electrocardiology 27, no. 1 (November 27, 2021). doi:10.1111/anec.12919.
dc.identifier.issn1082-720Xen
dc.identifier.urihttp://hdl.handle.net/11556/1239
dc.description.abstractBackground: Based on the known relationship between the human emotion and standard surface electrocardiogram (ECG), we explored the relationship between features extracted from standard ECG recorded during relaxation and seven personality traits (Honesty/humility, Emotionality, eXtraversion, Agreeableness, Conscientiousness, Openness, and Disintegration) by using the machine learning (ML) approach which learns from the ECG-based features and predicts the appropriate personality trait by adopting an automated software algorithm. Methods: A total of 71 healthy university students participated in the study. For quantification of 62 ECG-based parameters (heart rate variability, as well as temporal and amplitude-based parameters) for each ECG record, we used computation procedures together with publicly available data and code. Among 62 parameters, 34 were segregated into separate features according to their diagnostic relevance in clinical practice. To examine the feature influence on personality trait classification and to perform classification, we used random forest ML algorithm. Results: Classification accuracy when clinically relevant ECG features were employed was high for Disintegration (81.3%) and Honesty/humility (75.0%) and moderate to high for Openness (73.3%) and Conscientiousness (70%), while it was low for Agreeableness (56.3%), eXtraversion (47.1%), and Emotionality (43.8%). When all calculated features were used, the classification accuracies were the same or lower, except for the eXtraversion (52.9%). Correlation analysis for selected features is presented. Conclusions: Results indicate that clinically relevant features might be applicable for personality traits prediction, although no remarkable differences were found among selected groups of parameters. Physiological associations of established relationships should be further explored.en
dc.description.sponsorshipMinistry of Education, Science, and Technological Development, Republic of Serbia, Grant/Award Number: 179018 and TR33020; Abbott Laboratoriesen
dc.language.isoengen
dc.publisherJohn Wiley and Sons Inc.en
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleRelationship between electrocardiogram‐based features and personality traits: Machine learning approachen
dc.typearticleen
dc.identifier.doi10.1111/anec.12919en
dc.rights.accessRightsopenAccessen
dc.subject.keywordsDisintegrationen
dc.subject.keywordsECGen
dc.subject.keywordsHEXACOen
dc.subject.keywordsMachine learningen
dc.subject.keywordsPersonality traitsen
dc.subject.keywordsRandom foresten
dc.identifier.essn1542-474Xen
dc.issue.number1
dc.journal.titleAnnals of Noninvasive Electrocardiologyen
dc.page.initiale12919
dc.volume.number27


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