Journal article
T. Pham, C. Johnco, Z. J. Lau, D. Makowski, M. K. Forbes
Preprint on OSF, 2025
Preprint on OSF, 2025
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APA
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Pham, T., Johnco, C., Lau, Z. J., Makowski, D., & Forbes, M. K. (2025). Which Heart Rate Variability (HRV) indices should I use? A data-driven approach to identifying clusters of HRV indices. Preprint on OSF. https://doi.org/10.31234/osf.io/jz6yq
Chicago/Turabian
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Pham, T., C. Johnco, Z. J. Lau, D. Makowski, and M. K. Forbes. “Which Heart Rate Variability (HRV) Indices Should I Use? A Data-Driven Approach to Identifying Clusters of HRV Indices.” Preprint on OSF (2025).
MLA
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Pham, T., et al. “Which Heart Rate Variability (HRV) Indices Should I Use? A Data-Driven Approach to Identifying Clusters of HRV Indices.” Preprint on OSF, 2025, doi:10.31234/osf.io/jz6yq.
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@article{pham2025a,
title = {Which Heart Rate Variability (HRV) indices should I use? A data-driven approach to identifying clusters of HRV indices},
year = {2025},
journal = {Preprint on OSF},
doi = {10.31234/osf.io/jz6yq},
author = {Pham, T. and Johnco, C. and Lau, Z. J. and Makowski, D. and Forbes, M. K.}
}
Abstract
Heart Rate Variability (HRV) can be quantified using a myriad of mathematical indices, but the lack of systematic and empirical comparison between these indices complicates the evaluation and interpretation of HRV data. This study assessed the reliability, consistency, and generalizability of the structural relationships among 89 HRV indices using a consensus- clustering approach. We analyzed 635 short-term resting-state electrocardiogram (ECG) recordings from two samples of college students with differing psychological profiles. Results from a sample with elevated internalizing symptoms (N=233)—collected across two sessions, one week apart—were compared to evaluate the test-retest reliability of the HRV clusters. To further assess the stability and generalizability of these HRV clusters beyond individuals with elevated internalizing symptoms, these results were compared with a second sample not selected based on psychological symptoms (N=203). We identified 19 clusters of 70 HRV indices with cross-method, test-retest, and cross-sample robustness. Based on the robust empirical convergence and the relative popularity of some HRV indices in the extant literature, we recommend 13 HRV indices for short-term recordings of resting-state HRV (under 10 minutes): RMSSD, SDNN, RSA (Porges-Bohrer or Peak-to-Trough method), RSA (Gates method), SD1/SD2 or CSI, ApEn or SampEn, HF or LnHF, DFA α1, DFA α2, one of the MDFA α1 features, one of the MDFA α2 features, one of the heart rate asymmetry indices, and one of the heart rate fragmentation indices. This approach mitigates the biases that can arise from redundant or highly correlated indices, facilitates clearer interpretation, and enhances the validity of conclusions drawn from HRV analyses.
Heart Rate Variability (HRV) can be quantified using a myriad of mathematical indices, but the lack of systematic and empirical comparison between these indices complicates the evaluation and interpretation of HRV data. This study assessed the reliability, consistency, and generalizability of the structural relationships among 89 HRV indices using a consensus- clustering approach. We analyzed 635 short-term resting-state electrocardiogram (ECG) recordings from two samples of college students with differing psychological profiles. Results from a sample with elevated internalizing symptoms (N=233)—collected across two sessions, one week apart—were compared to evaluate the test-retest reliability of the HRV clusters. To further assess the stability and generalizability of these HRV clusters beyond individuals with elevated internalizing symptoms, these results were compared with a second sample not selected based on psychological symptoms (N=203). We identified 19 clusters of 70 HRV indices with cross-method, test-retest, and cross-sample robustness. Based on the robust empirical convergence and the relative popularity of some HRV indices in the extant literature, we recommend 13 HRV indices for short-term recordings of resting-state HRV (under 10 minutes): RMSSD, SDNN, RSA (Porges-Bohrer or Peak-to-Trough method), RSA (Gates method), SD1/SD2 or CSI, ApEn or SampEn, HF or LnHF, DFA α1, DFA α2, one of the MDFA α1 features, one of the MDFA α2 features, one of the heart rate asymmetry indices, and one of the heart rate fragmentation indices. This approach mitigates the biases that can arise from redundant or highly correlated indices, facilitates clearer interpretation, and enhances the validity of conclusions drawn from HRV analyses.