Automated Identification of Persistent Time-Domain Features in Seismocardiogram Signals

Abstract

In the field of cardiac monitoring, the seismocardiogram (SCG) measures the movement of the chest wall using accelerometers and gyroscopes. A key limitation of SCG signals is their sensitivity to transient signal disruptions primarily due to motion artifacts. This work describes a method for automated extraction of time-domain features in SCG signals in the presence of such artifacts, using an iterative method of clustering and re-sampling features to optimize consistency. The accelerometer (axl) and gyroscope (gyr) features extracted with this method are shown to correlate more strongly (median $R^{2}=0.88\ (\mathbf{axl}), 0.88 (\mathbf{gyr})$) with the reference standard for pre-ejection period (PEP), impedance cardiography (ICG), than both peak-counting $(R^{2}=0.29\ (\mathbf{axl}), 0.48\ (\mathbf{gyr}))$ and manual labeling $(R^{2}=0.44\ (\mathbf{axl}), 0.38 (\mathbf{gyr}))$ in the post-exercise period. This result has implications for the feasibility of at-home SCG monitoring.