Relationship between electrocardiogram-based features and personality traits: Machine learning approach
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Background: 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 influen...ce 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.
Ključne reči:
disintegration / ECG / HEXACO / machine learning / personality traits / random forestIzvor:
Annals of Noninvasive Electrocardiology, 2021, 27, 1, e12919-Izdavač:
- Wiley
Finansiranje / projekti:
- Povećanje energetske efikasnosti HE i TE EPS-a razvojem tehnologije i uređaja energetske elektronike za regulaciju i automatizaciju (RS-MESTD-Technological Development (TD or TR)-33020)
- Identifikacija, merenje i razvoj kognitivnih i emocionalnih kompetencija važnih društvu orijentisanom na evropske integracije (RS-MESTD-Basic Research (BR or ON)-179018)
DOI: 10.1111/anec.12919
ISSN: 1082-720X
WoS: 000722943100001
Scopus: 2-s2.0-85119978513
Institucija/grupa
Psihologija / PsychologyTY - JOUR AU - Boljanić, Tanja AU - Miljković, Nadica AU - Lazarević, Ljiljana AU - Knežević, Goran AU - Milašinović, Goran PY - 2021 UR - http://reff.f.bg.ac.rs/handle/123456789/3506 AB - Background: 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. PB - Wiley T2 - Annals of Noninvasive Electrocardiology T1 - Relationship between electrocardiogram-based features and personality traits: Machine learning approach IS - 1 SP - e12919 VL - 27 DO - 10.1111/anec.12919 ER -
@article{ author = "Boljanić, Tanja and Miljković, Nadica and Lazarević, Ljiljana and Knežević, Goran and Milašinović, Goran", year = "2021", abstract = "Background: 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.", publisher = "Wiley", journal = "Annals of Noninvasive Electrocardiology", title = "Relationship between electrocardiogram-based features and personality traits: Machine learning approach", number = "1", pages = "e12919", volume = "27", doi = "10.1111/anec.12919" }
Boljanić, T., Miljković, N., Lazarević, L., Knežević, G.,& Milašinović, G.. (2021). Relationship between electrocardiogram-based features and personality traits: Machine learning approach. in Annals of Noninvasive Electrocardiology Wiley., 27(1), e12919. https://doi.org/10.1111/anec.12919
Boljanić T, Miljković N, Lazarević L, Knežević G, Milašinović G. Relationship between electrocardiogram-based features and personality traits: Machine learning approach. in Annals of Noninvasive Electrocardiology. 2021;27(1):e12919. doi:10.1111/anec.12919 .
Boljanić, Tanja, Miljković, Nadica, Lazarević, Ljiljana, Knežević, Goran, Milašinović, Goran, "Relationship between electrocardiogram-based features and personality traits: Machine learning approach" in Annals of Noninvasive Electrocardiology, 27, no. 1 (2021):e12919, https://doi.org/10.1111/anec.12919 . .