Differences in cardiac time intervals (CTIs) have previously been shown in different patient groups with varying levels of cardiac function. These studies relied on methods such as conventional echocardiography or tissue doppler imaging performed by a specialist to extract CTIs. The goal of this study was to evaluate the ability of using a combination of single lead ECG and seismocardiography (SCG) from a sensor placed on a subject’s sternum to automatically extract CTIs.
For each subject, the SCG Z-axis was segmented based on the RR-intervals extracted from the ECG signal. The cardiac cycles were grouped using a dynamic time warping similarity measure. Similar cardiac cycles were combined to create an ensemble averaging after which a peak detection algorithm was applied to the ensemble waveform to extract PEP, LVET, TST, and TDT which were normalized by the mean heart rate representing the entire recording.
The groups consisted of younger subjects with no known history of cardiac disease (abbrv: NKHCD, N=51, MeanAge=NA), older subjects with a history of cardiac disease excluding valve heart disease (abbrv: HCD, N=49, MeanAge =66), subjects with severe aortic stenosis before transcatheter aortic valve implantation (TAVI) (abbrv: PRE-TAVI, N=57, MeanAge =79), and 23 PRE-TAVI subjects who had received a TAVI procedure (abbrv: POST-TAVI, N=23, MeanAge =76).
LVET was on average 20.5 % shorter in the NKHCD group vs PRE-TAVI (p-value<0.05) and 5.9% shorter in the HCD group vs PRE-TAVI (p-value =.053). Comparing CTIs between the subjects who had data recorded before and after receiving a TAVI procedure, a 12.6% postoperative reduction in LVET (p-value<0.05) was found on average as well as a 30.2% increase in PEP/LVET (p-value<0.05).
These results are in line with literature where LVET increases with age and heart disease and decrease after TAVI procedures when echocardiography was the main methodology used to extract CTIs.