Reality Bending Lab › Publications › Which Heart Rate Variability (HRV) Indices Should I Use for Psychophysiological Research? A Data-Driven Approach to Identifying Clusters of HRV Indices

Which Heart Rate Variability (HRV) Indices Should I Use for Psychophysiological Research? A Data-Driven Approach to Identifying Clusters of HRV Indices

Pham, T., Johnco, C. J., Lau, Z. J., Makowski, D., & Forbes, M. K.

Psychophysiology, 2025

There are dozens of HRV indices and many of them measure much the same thing. Consensus clustering of 89 indices across 635 resting ECG recordings from two samples produced 21 robust clusters, and from those a recommended set of 13 indices for short resting-state recordings — enough to cover the space without reporting the same quantity six times.

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 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, 1 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 21 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 min): RMSSD, SDNN, RSA (Porges‐Bohrer or Peak‐to‐Trough method), RSA (Gates method), SD1/SD2 or CSI, 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.

• DOI: 10.1111/psyp.70164
• Full text (PDF)

Keywords: Heart Rate Variability · Psychophysiology · Signal Processing · Statistical Methods

Cite

Pham, T., Johnco, C. J., Lau, Z. J., Makowski, D., & Forbes, M. K. (2025). Which Heart Rate Variability (HRV) Indices Should I Use for Psychophysiological Research? A Data-Driven Approach to Identifying Clusters of HRV Indices. Psychophysiology. https://doi.org/10.1111/psyp.70164

See also

• Heart Rate Variability in Psychology: A Review of HRV Indices and an Analysis Tutorial — Pham, T., Lau, Z. J., Chen, S. H. A., & Makowski, D. (2021)
• Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations — Lau, Z. J., Pham, T., Chen, S. H. A., & Makowski, D. (2022)
• The Structure of Chaos: An Empirical Comparison of Fractal Physiology Complexity Indices Using NeuroKit2 — Makowski, D., Te, A. S., Pham, T., Lau, Z. J., & Chen, S. H. A. (2022)