Exploration of analytical tools for heart rate variability analysis: Insights into differences

Objectives: Heart rate variability (HRV) provides insight into autonomic modulation of the heart rate. HRV indices – classified as time domain, frequency domain and non-linear indices – are computed using software tools. While the calculation of time domain indices is relatively straightforward, there exists heterogeneity in the methodology of spectral analysis of HRV parameters across different platforms. We compared HRV indices derived from electrocardiogram (ECG) data recorded in healthy adults using Kubios ™ (‘K’), LabChart ™ (‘L’) and Nevrokard ™ (‘N’) software solutions. Materials and Methods: A single observer analysed HRV indices from 5-min Lead II ECG epochs using ‘K’, ‘L’ and ‘N’ software. Time domain and frequency domain indices were computed and compared. Bias between the software was assessed using Bland–Altman analysis by choosing ‘N’ as the reference method. Mean bias and limits of agreement were compared. Results: Data of 53 healthy adults (40 males, 13 females; mean age = 29.89 ± 6.94 years) were analysed. Time domain indices, while exhibiting statistically significant differences, had slight differences between ‘K’, ‘L’ and ‘N’. Median frequency domain indices derived from ‘K’, ‘L’ and ‘N’ demonstrated statistically significant differences, especially low frequency (LF), high frequency (HF) and normalised powers of LF and HF. Furthermore, they demonstrated reasonable bias between tools. Conclusion: HRV indices, especially frequency domain indices, may be affected by the analytical methodology adopted by the software tool. While we cannot establish superiority of one software over another, we recommend detailed documentation of analytical approach of HRV data in scientific literature. In addition, comparison of HRV indices using different analytical tools merits a cautious approach.