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 (R2) 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.
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 (R2) 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.
Authors: Juan Pablo Martínez; Rute Almeida; Salvador Olmos; Ana Paula Rocha; Pablo Laguna Journal: IEEE Trans Biomed Eng Date: 2004-04 Impact factor: 4.538
Authors: Marco A F Pimentel; Alistair E W Johnson; Peter H Charlton; Drew Birrenkott; Peter J Watkinson; Lionel Tarassenko; David A Clifton Journal: IEEE Trans Biomed Eng Date: 2016-11-18 Impact factor: 4.538
Authors: Peter H Charlton; Timothy Bonnici; Lionel Tarassenko; David A Clifton; Richard Beale; Peter J Watkinson Journal: Physiol Meas Date: 2016-03-30 Impact factor: 2.833