Cardiac time intervals are important hemodynamic indices and provide information about left ventricular performance. Phonocardiography (PCG), impedance cardiography (ICG), and recently, seismocardiography (SCG) have been unobtrusive methods of choice for detection of cardiac time intervals and have potentials to be integrated into wearable devices. The main purpose of this study was to investigate the accuracy and precision of beat-to-beat extraction of cardiac timings from the PCG, ICG and SCG recordings in comparison to multimodal echocardiography (Doppler, TDI, and M-mode) as the gold clinical standard. Recordings were obtained from 86 healthy adults and in total 2,120 cardiac cycles were analyzed. For estimation of the pre-ejection period (PEP), 43% of ICG annotations fell in the corresponding echocardiography ranges while this was 86% for SCG. For estimation of the total systolic time (TST), these numbers were 43, 80, and 90% for ICG, PCG, and SCG, respectively. In summary, SCG and PCG signals provided an acceptable accuracy and precision in estimating cardiac timings, as compared to ICG.

The results obtained from this study can be employed to enhance the extraction of clinically valuable information such as systolic time intervals.

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Objective: Assessment of cardiac time intervals (CTIs) is essential for monitoring cardiac performance. Recently, gyrocardiography (GCG) has been introduced as a non-invasive technology for cardiac monitoring. GCG measures the chest’s angular precordial vibrations caused by myocardium wall motion using a gyroscope sensor attached to the sternum. In this study, we investigated the accuracy and reproducibility of estimating CTIs from the GCG recordings of 50 adults. Approach: We proposed five fiducial points for the GCG waveforms associated with the opening and closure of aortic and mitral valves. Two annotators annotated the suggested points on each cardiac cycle. The points were compared to the corresponding opening and closing of cardiac valves delineated on Tissue Doppler imaging (TDI) recordings. The fiducial points were annotated on seismocardiography (SCG) and impedance cardiography (ICG) signals recorded simultaneously. Main results: For estimating the timing of mitral valve closure, aortic valve opening, aortic valve closure, and mitral valve opening, 40%, 67%, 75%, and 70% of GCG annotations fell in the corresponding echocardiography ranges, respectively. The results showed moderate-to-excellent (r = 0.4-0.92; p-value < 0.01) correlation between the measured and the reference CTls. A myocardial performance index (Tei index) adapted using joint GCG and SCG resulted in a moderate correlation (r = 0.4; p-value < 0.001). Significance: The findings showed that the CTIs can be easily measured using GCG. Also, we found that using SCG and GCG recordings together could provide an opportunity to estimate CTIs more accurately, and make it possible to calculate the Tei index as an indicator of myocardial performance.

Coronary artery disease (CAD) is the most common cause of death globally. Patients with suspected CAD are usually assessed by exercise electrocardiography (ECG). Subsequent tests, such as coronary angiography and coronary computed tomography angiography (CCTA) are performed to localize the stenosis and to estimate the degree of blockage. The present study describes a non-invasive methodology to identify patients with CAD based on the analysis of both rest and exercise seismocardiography (SCG). SCG is a non-invasive technology for capturing the acceleration of the chest induced by myocardial motion and vibrations. SCG signals were recorded from 185 individuals at rest and immediately after exercise. Two models were developed using the characterization of the rest and exercise SCG signals to identify individuals with CAD. The models were validated against related results from angiography. For the rest model, accuracy was 74%, and sensitivity and specificity were estimated as 75 and 72%, respectively. For the exercise model accuracy, sensitivity, and specificity were 81, 82, and 84%, respectively. The rest and exercise models presented a bootstrap-corrected area under the curve of 0.77 and 0.91, respectively. The discrimination slope was estimated 0.32 for rest model and 0.47 for the exercise model. The difference between the discrimination slopes of these two models was 0.15 (95% CI: 0.10 to 0.23, p < 0.0001). Both rest and exercise models are able to detect CAD with comparable accuracy, sensitivity, and specificity. Performance of SCG is better compared to stress-ECG and it is identical to stress-echocardiography and CCTA. SCG examination is fast, inexpensive, and may even be carried out by laypersons.