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HRV Studio is an open-source platform designed to enhance the reproducibility of heart rate variability (HRV) analysis by integrating transparent methodologies with automated quality-control diagnostics. Validation against established tools like NeuroKit2 and Kubios demonstrated that HRV Studio achieves near-identical agreement on key time-domain indices and maintains high consistency across various frequency-domain measures, despite some sensitivity in very low frequency (VLF) outputs. The platform's robust performance under synthetic perturbations and arrhythmia-focused stress tests underscores its reliability for HRV research, although clinical validation remains to be established.
HRV Studio achieves near-perfect agreement with leading HRV analysis tools, revolutionizing reproducibility in cardiovascular research.
Reproducibility of heart rate variability (HRV) analysis is limited by differences in preprocessing and computational conventions across software platforms. We developed HRV Studio, an open-source PyQt6-based desktop application integrating transparent HRV analysis with automated quality-control (QC) diagnostics. Validation included large-scale agreement with NeuroKit2, targeted Kubios benchmarking, spectral-method comparison, synthetic perturbation testing, recording-duration sensitivity analysis, and arrhythmia-focused QC stress testing. HRV Studio showed near-identical agreement for the widely used time-domain indices RMSSD and SDNN under matched conditions. In the primary five-minute NeuroKit2 comparison, frequency-domain median relative errors were 1.35% for LF, 0.18% for HF, and 1.41% for LF/HF, while VLF remained more convention-sensitive (37.79%). Nonlinear Poincar\'e indices also demonstrated high consistency. Sequence-harmonized Kubios benchmarking confirmed near-identical agreement for time-domain and nonlinear indices and strong agreement for most frequency-domain measures. Extended ten-minute analyses reproduced the same overall pattern with lower disagreement for some convention-sensitive spectral outputs. Synthetic and arrhythmia stress tests maintained 100% numerical stability while consistently triggering QC warnings. Overall, HRV Studio provides a transparent and reproducible platform for HRV research, with strong cross-platform consistency when NN sequences, preprocessing, and analytical conventions are harmonized. Stress-test results indicate computational robustness rather than clinical validation.
