Estimation of Cardiac Time Intervals From the Mechanical Activity of the Heart Using Machine Learning

Pre-ejection period (PEP) and total systolic time (TST) are hemodynamic indices which provide vital information about left ventricular performance. In this study, we suggested a method for estimating PEP and TST using the features extracted from the mechanical activity of the heart without finding the fiducial points associated with the opening and closure of heart valves. We recorded seismocardiogram (SCG) and gyrocardiogram (GCG) signals from 50 healthy subjects using a 3-axial micro-electromechanical joint accelerometer-gyroscope sensor. For each axis of the SCG and GCG signals, 18 features were extracted per cardiac cycle. Several regression models were implemented using the features from multiple combinations of SCG and GCG and were validated against the measurements from tissue Doppler imaging (TDI) as the reference. The model that was fitted over the SCG z-axis features provided the most accurate estimates for PEP and TST intervals with the root mean square error of 9.5 and 17.2 millisecond, respectively. The estimation of cardiac time intervals investigated in this study broadens the potential of SCG and GCG in the monitoring of cardiovascular performance. As these technologies yield themselves to wearable applications, they could be used outside of hospital/clinical settings to detect abnormalities and malfunctions of the cardiovascular system.