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Updated: Oct 11, 2025

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Relationship between electrocardiogram-based features and personality traits: Machine learning approach.

Tanja Boljanić1,2, Nadica Miljković1, Ljiljana B Lazarevic3

  • 1School of Electrical Engineering, University of Belgrade, Belgrade, Serbia.

Annals of Noninvasive Electrocardiology : the Official Journal of the International Society for Holter and Noninvasive Electrocardiology, Inc
|November 27, 2021
PubMed
Summary
This summary is machine-generated.

Machine learning accurately predicts personality traits like Disintegration and Honesty/Humility using electrocardiogram (ECG) features. This research explores the physiological basis of personality through advanced signal processing and artificial intelligence.

Keywords:
ECGHEXACOdisintegrationmachine learningpersonality traitsrandom forest

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Area of Science:

  • Psychophysiology
  • Computational Psychology
  • Biomedical Engineering

Background:

  • The connection between human emotions and electrocardiogram (ECG) signals is established.
  • This study investigates the link between ECG features during relaxation and seven personality traits.

Purpose of the Study:

  • To explore the relationship between standard surface ECG features and personality traits using machine learning.
  • To predict personality traits based on ECG-derived parameters.

Main Methods:

  • Utilized standard surface ECG recordings from 71 healthy university students.
  • Extracted and analyzed 62 ECG-based parameters, including heart rate variability and temporal/amplitude features.
  • Employed a random forest machine learning algorithm for personality trait classification.

Main Results:

  • High classification accuracy was achieved for Disintegration (81.3%) and Honesty/Humility (75.0%) using clinically relevant ECG features.
  • Moderate to high accuracies were observed for Openness (73.3%) and Conscientiousness (70%).
  • Lower accuracies were noted for Agreeableness, eXtraversion, and Emotionality, with some improvement for eXtraversion when all features were used.

Conclusions:

  • Clinically relevant ECG features show potential for predicting personality traits.
  • Further research is needed to explore the physiological underpinnings of these observed relationships.