Soliman Moamen M, Ganti Venu G, Inan Omer T
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332.
Bioengineering Graduate Program, Georgia Institute of Technology, Atlanta, GA 30332.
IEEE Sens J. 2022 Sep 15;22(18):18093-18103. doi: 10.1109/jsen.2022.3196601. Epub 2022 Aug 10.
The current COVID-19 pandemic highlights the critical importance of ubiquitous respiratory health monitoring. The two fundamental elements of monitoring respiration are respiration rate (the frequency of breathing) and tidal volume (TV, the volume of air breathed by the lungs in each breath). Wearable sensing systems have been demonstrated to provide accurate measurement of respiration rate, but TV remains challenging to measure accurately with wearable and unobtrusive technology. In this work, we leveraged electrocardiogram (ECG) and seismocardiogram (SCG) measurements obtained with a custom wearable sensing patch to derive an estimate of TV from healthy human participants. Specifically, we fused both ECG-derived and SCG-derived respiratory signals (EDR and SDR) and trained a machine learning model with gas rebreathing as the ground truth to estimate TV. The respiration cycle modulates ECG and SCG signals in multiple different ways that are synergistic. Thus, here we extract EDRs and SDRs using a multitude of different demodulation techniques. The extracted features are used to train a subject independent machine learning model to accurately estimate TV. By fusing the extracted EDRs and SDRs, we were able to estimate the TV with a root-mean-square error (RMSE) of 181.45 mL and Pearson correlation coefficient (r) of 0.61, with a global subject-independent model. We further show that SDRs are better TV estimators than EDRs. Among SDRs, amplitude modulated (AM) SCG features are the most correlated to TV. We demonstrated that fusing EDRs and SDRs can result in moderately accurate estimation of TV using a subject-independent model. Additionally, we highlight the most informative features for estimating TV. This work presents a significant step towards achieving continuous, calibration free, and unobtrusive TV estimation, which could advance the state of the art in wearable respiratory monitoring.
当前的新冠疫情凸显了无处不在的呼吸健康监测的至关重要性。监测呼吸的两个基本要素是呼吸频率（呼吸的频次）和潮气量（TV，肺部每次呼吸所吸入的空气量）。可穿戴传感系统已被证明能够准确测量呼吸频率，但使用可穿戴且不引人注意的技术来准确测量潮气量仍然具有挑战性。在这项工作中，我们利用通过定制可穿戴传感贴片获得的心电图（ECG）和心震图（SCG）测量数据，来推导健康人类参与者的潮气量估计值。具体而言，我们融合了源自心电图和心震图的呼吸信号（EDR和SDR），并以气体再呼吸作为基准真值训练了一个机器学习模型来估计潮气量。呼吸周期以多种协同的不同方式调制心电图和心震图信号。因此，在这里我们使用多种不同的解调技术来提取EDR和SDR。提取的特征用于训练一个独立于个体的机器学习模型，以准确估计潮气量。通过融合提取的EDR和SDR，我们能够使用一个全局独立于个体的模型，以均方根误差（RMSE）为181.45毫升和皮尔逊相关系数（r）为0.61来估计潮气量。我们进一步表明，SDR比EDR是更好的潮气量估计指标。在SDR中，调幅（AM）心震图特征与潮气量的相关性最高。我们证明，融合EDR和SDR能够使用一个独立于个体的模型对潮气量进行适度准确的估计。此外，我们突出了用于估计潮气量的最具信息量的特征。这项工作朝着实现连续、无需校准且不引人注意的潮气量估计迈出了重要一步，这可能推动可穿戴呼吸监测领域的技术发展。
