Evaluating a ballistocardiography derived respiratory rate algorithm through comprehensive clinical validation across multiple settings

Continuous respiratory rate (RR) monitoring is key for early deterioration detection, but conventional methods require contact. Ballistocardiography (BCG) offers an unobtrusive alternative using an under-mattress sensor. This study evaluated a BCG-based RR algorithm across diverse clinical settings and populations. BCG data from 11 studies involving 400 subjects across wards, ICUs, sleep labs, and healthy volunteers were analyzed. Reference RR was measured using capnography or polysomnography. An algorithm processed BCG signals via filtering,, dynamic thresholding, and median filtering to estimate RR. Performance was assessed by mean absolute error (MAE), detection rate (DR), and agreement with reference using Bland-Altman, Deming regression, and Pearson’s coefficient. Across 68,342 reference datapoints the algorithm achieved an MAE of 1.29 BrPM and a 92.68% detection rate. Performance was consistent across studies, with MAE ranging from 0.96 to 1.8 BrPM and detection rates from 85 to 97%. Accuracy was higher in controlled settings (e.g., sleep lab: MAE − 0.96 BrPM, 95.8% detection) and slightly lower in wards (MAE ~ 1.6 BrPM, 86–93% detection). Subgroup analyses by geography, demographics, and comorbidities showed consistently low error and good detection, except in COPD patients, where detection dropped to ~ 78% (MAE-1.3 BrPM). Bland–Altman and Deming regression showed minimal bias (–0.39 BrPM) and strong correlation (Pearson’s r = 0.86). A continuous, unobtrusive BCG-based RR measure offers high accuracy and detection, supporting its use in early respiratory event detection and improved patient care.