Using confidence intervals to assess the reliability of instantaneous heart rate and respiratory rate

Physiological waveform signals collected from unstructured environments are noisy, requiring automated algorithms to assess the reliability of the derived vital signs, such as heart rate (HR) and respiratory rate (RR), before they can be used for automated decision support. We recently proposed a weighted regularized least squares method to estimate instantaneous HR (HR_R), which readily provides analytically based confidence intervals (CIs). Accordingly, this method can be extended to the estimation of instantaneous RR (RR_R). In this study, we aim to investigate whether we can use CIs to select reliable HR_R and RR_R. We calculated HR_R and RR_R for 532 and 370 trauma patients, respectively, grouped the rates according to their CIs, and investigated their reliability by determining their ability to diagnose major hemorrhage. The areas under a receiver operating characteristic curve of HR_R and RR_R with CI ≤ 5 bpm (beats per minute for HR and breaths per minute for RR) were 0.70 and 0.66, respectively. RR_R was superior to the average output of the clinical monitor (p <; 0.05 by DeLong´s test), while HR_R was equivalent. HR_R and RR_R provide a new approach to systematically and automatically assess the reliability of noisy, field-collected vital signs.