Respiratory Rate Estimation Using U-Net-Based Cascaded Framework From Electrocardiogram and Seismocardiogram Signals.
Respiratory Rate Estimation Using U-Net-Based Cascaded Framework From Electrocardiogram and Seismocardiogram Signals.
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DOI：
10.1109/jbhi.2022.3144990
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发表时间：
2022-06
影响因子：
7.7
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中科院分区：
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尤其是在COVID-19全球大流行的时代，在家监测呼吸至关重要。心电图（ECG）和心震图（SCG）信号--与测量呼吸气流的传统密封面罩相比，以更简单的接触形式测量--是呼吸监测的有前途的解决方案。具体地，可以从ECG导出的呼吸（EDR）和SCG导出的呼吸（SDR）信号来估计呼吸率（RR）。然而，非呼吸伪影可能仍然存在于呼吸信号的这些替代物中，从而妨碍估计的RR的准确性。在本文中，我们提出了一种新的基于U网的级联框架来解决这个问题。EDR和SDR信号被转换到频谱-时间域，随后通过2D U-Net去噪以减少非呼吸伪影。我们已经证明，融合EDR输入和SDR输入的U-Net使用从我们的胸戴式可穿戴贴片收集的数据实现了0.82次呼吸/分钟（bpm）的低平均绝对误差和0.89的决定系数（R2）。我们还定性地提供了EDR和SDR信号之间的互补性的见解，并证明了所提出的框架的普遍性。ECG和SCG收集从胸部佩戴的可穿戴贴片可以相互补充，并产生可靠的RR估计使用所提出的级联框架。我们预计，方便和舒适的心电图和SCG测量系统可以增强与此框架，以促进普及和准确的RR测量。
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. 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. 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. ECG and SCG collected from a chest-worn wearable patch can complement each other and yield reliable RR estimation using the proposed cascaded framework. We anticipate that convenient and comfortable ECG and SCG measurement systems can be augmented with this framework to facilitate pervasive and accurate RR measurement.