TY  - 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 . .