Validation report of the respiratory rate (RESP) estimation algorithm, which is available in Kubios HRV Premium software, is now available at Kubios publications www.kubios.com/publications/.

Respiratory rate is one of the vital signs that is widely used in a range of clinical settings as well as in exercise  monitoring. In addition, RESP can be used in heart rate variability (HRV) analysis, specifically to improve the  accuracy of respiratory sinus arrhythmia (RSA) assessment. Respiration can be directly measured using various techniques, but RESP can also be estimated from electrocardiogram (ECG) or RR interval recordings.

Kubios HRV Premium (version 3.5 or later) includes two algorithms for RESP estimation. The first one uses both ECG and RR data features in RESP estimation and is utilized only when ECG data is available. The second algorithm uses only RR data in RESP estimation and is utilized when ECG data is not available. The accuracies of the algorithms were validated with resting HRV recordings from 262 participants and with maximal exercise test recordings from 123  participants.

The observed correlation with true RESP was strong to moderate in resting HRV recordings (R = 0.892 vs. R = 0.676) and strong in exercise HRV recordings (R = 0.922 vs. R = 0.881), with higher correlations corresponding to the  estimates that used both ECG and RR data in RESP estimation. Overall, the accuracy of the algorithm which utilized both ECG and RR data for RESP estimation was better with an average bias of 0.021 Hz and accuracy (error SD) of 0.062 Hz.

In conclusion, the bias and accuracy of both respiratory rate estimation algorithms is good. The algorithm that uses both ECG and RR interval data in RESP estimation performs better at high respiratory rates. The difference in high respiratory rates may be explained by the decreased or almost vanished HRV during high-intensity exercise.