Validation of non-contact sensor quantification of heart rate and respiratory rate dynamics using real-world...

Non-contact mechanical bed sensors can passively and longitudinally monitor the dynamics of cardiopulmonary physiology to detect changes from patient-specific baselines and facilitate care. This requires accurate longitudinal quantification of established metrics like respiratory rate (RR) and heart rate (HR) from the underlying raw waveforms, validated against ground truth labeled datasets like simultaneous polysomnography (PSG). Whereas head-to-head labeled datasets are scarce and costly to collect, unlabeled real-world datasets are often abundant. Here, we show that non-optimized heuristic algorithms can be used to "soft-label" large real-world data ( > 40M minutes across > 50,000 nights) for pretraining models. This enables label-efficient fine-tuning on small numbers of head-to-head PSG-labeled datasets while maximizing generalizability and robustness to hyperparameters. The result is a highly performant validated model with mean absolute errors (MAE) of 0.6 brpm for RR and 1.1 bpm for HR across 1-minute windows. Although demonstrated in the context of bed sensor cardiopulmonary quantification, these methods are applicable to development of sensor algorithms whenever labeled ground truth data is scarce and unlabeled real-world data is abundant.