Anger induction significantly altered multiple ECG and ICG temporal and amplitude features compared to baseline, though the effect sizes were generally small or negligible.
Cross-Sectional (n=202)
This methodological study details the extraction of ECG and ICG features for machine learning-based biometric identification, highlighting that emotional states significantly alter cardiogram features and may challenge model generalization.
The repository contains Supplementary materials for the "Interpretable machine learning for cardiogram-based biometrics" paper. If you find the provided materials useful for your own research and teaching class, please cite the following references: Tanasković, I., Lazarević, L. B., Knežević, G., Milosavljević, N., Dubljević, O., Bjegojević, B., & Miljković, N. (2025). Interpretable machine learning for cardiogram-based biometrics. arXiv preprint. https://doi.org/10.48550/arXiv.2510.19775
Tanasković et al. (2026) conducted a cross-sectional in Healthy (n=202). Anger induction vs. Baseline (relaxation) was evaluated on Differences in cardiogram-based features between baseline and anger states. Anger induction significantly altered multiple ECG and ICG temporal and amplitude features compared to baseline, though the effect sizes were generally small or negligible.