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.