Respiratory Modulation of Sternal Motion in the Context of Seismocardiography

• Main Authors: Aboulezz, E. (Author), Clairmonte, N. (Author), D'Mello, Y. (Author), Hakim, S. (Author), Lortie, M. (Author), Plant, D.V (Author), Skoric, J. (Author)
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2022
• Subjects: accelerometers; Accelerometers; Amplitude modulation; Baseline wandering; Biomedical monitoring; cardiovascular monitoring; Cardiovascular monitoring; Electrocardiography; Frequency modulation; gyrocardiography; Gyrocardiography; Monitoring; Patient monitoring; Recording; respiration; Respiration; Respiratory modulation; Seismocardiography; Vibration; Vibrations; wearable devices; Wearable devices; Wearable technology
• Online Access: View Fulltext in Publisher

 Wearable sensing has enabled physiological monitoring in everyday life. Clinically, cardiovascular and respiratory monitoring commonly requires separate devices. However, for feasibility in wearable systems, they are integrated into a single device. Since seismocardiography (SCG) records the motion of the chest wall, it has the potential to monitor both vital signs. The primary application of SCG is cardiovascular monitoring. As a result, its ability to detect respiration has been less defined. This study characterizes how SCG is affected by respiration and consequently, how respiratory metrics can be derived from these effects. The sternal motion of 40 subjects was recorded using an inertial measurement unit with a spirometer for reference. Three scenarios were examined with the subject at rest, holding their breath at peak inhalation, and holding their breath when exhaled. Three main aspects of respiratory modulation were explored: amplitude modulation, frequency modulation, and baseline wandering. The SCG amplitude was observed to be dependent on respiratory volume whereas baseline and frequency modulation were dependent on inhalation phase. All three effects were employed separately in the calculation of respiratory rate. Estimations from baseline wandering, amplitude modulation, and heart rate modulation produced r-squared values of 0.71, 0.44, and 0.66, respectively. The accuracy of tidal volume measurement was limited by a high inter-subject variability. Estimation of the respiratory waveform produced an average r-squared of 0.76 using multivariate linear regression. This demonstrates the potential of SCG as a tool for respiratory monitoring within an integrated, non-invasive cardiorespiratory monitoring system from a single point of contact. Author