Respiratory Rate Estimation Using U-Net-Based Cascaded Framework From Electrocardiogram and Seismocardiogram Signals

Objective: At-home monitoring of respiration is of critical urgency especially in the era of the global pandemic due to COVID-19. Electrocardiogram (ECG) and seismocardiogram (SCG) signals-measured in less cumbersome contact form factors than the conventional sealed mask that measures respiratory air flow-are promising solutions for respiratory monitoring. In particular, respiratory rates (RR) can be estimated from ECG-derived respiratory (EDR) and SCG-derived respiratory (SDR) signals. Yet, non-respiratory artifacts might still be present in these surrogates of respiratory signals, hindering the accuracy of the RRs estimated. Methods: In this paper, we propose a novel U-Net-based cascaded framework to address this problem. The EDR and SDR signals were transformed to the spectro-temporal domain and subsequently denoised by a 2D U-Net to reduce the non-respiratory artifacts. Major Results: We have shown that the U-Net that fused an EDR input and an SDR input achieved a low mean absolute error of 0.82 breaths per minute (bpm) and a coefficient of determination (R-2) of 0.89 using data collected from our chest-worn wearable patch. We also qualitatively provided insights on the complementariness between EDR and SDR signals and demonstrated the generalizability of the proposed framework. Conclusion: ECG and SCG collected from a chest-worn wearable patch can complement each other and yield reliable RR estimation using the proposed cascaded framework. Significance: We anticipate that convenient and comfortable ECG and SCG measurement systems can be augmented with this framework to facilitate pervasive and accurate RR measurement.