Toward Wearable Estimation of Tidal Volume via Electrocardiogram and Seismocardiogram Signals

The current coronavirus disease (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 an ECG-derived respiratory signal (EDR) and an SCG-derived respiratory signal (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 modulation (AM) SCG features are the most correlated with TV. We demonstrated that fusing EDRs and SDRs can result in a moderately accurate estimation of the TV using a subject-independent model. Additionally, we highlight the most informative features for estimating TV. This work presents a significant step toward achieving continuous, calibration-free, and unobtrusive TV estimation, which could advance the state of the art in wearable respiratory monitoring.