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Abstract Cardiovascular diseases remain the leading cause of mortality worldwide, underscoring the need for improved diagnostic tools. Seismocardiography (SCG), a noninvasive technique that records chest surface vibrations generated by cardiac activity, holds promise for such applications. However, the mechanistic origins of SCG waveforms, particularly under varying physiological conditions, remain insufficiently understood. This study presents a finite element modeling approach to simulate SCG signals by tracking the propagation of cardiac wall motion to the chest surface. The computational model, constructed from 4D CT scans of healthy adult subjects, incorporates the lungs, ribcage, muscles, and adipose tissue. Cardiac displacement boundary conditions were extracted using the Lucas-Kanade algorithm, and elastic properties were assigned to different tissues. The simulated SCG signals in the dorsoventral direction were compared to realistic SCG recordings, showing consistency in waveform morphology. Key cardiac events, such as mitral valve closure, aortic valve opening, and closure, were identified on the modeled SCG waveforms and validated with concurrent CT images and left ventricular volume changes. A systematic sensitivity analysis was also conducted to examine how variations in tissue properties, soft tissue thickness, and boundary conditions influence SCG signal characteristics. The results highlight the critical role of personalized anatomical modeling in accurately capturing SCG features, thereby improving the potential of SCG for individualized cardiovascular monitoring and diagnosis. 

Abstract Cardiovascular diseases, the leading cause of global mortality, demand refined diagnostic methods. Seismocardiography (SCG), a noninvasive method of measuring cardiovascular-induced vibrations on the chest surface, offers promise in assessing cardiac function. The cardiac wall movements are transmitted to the organs around the heart and eventually damped onto the chest surface, where they manifest as visible vibrations. These chest surface vibrations can be measured using an accelerometer via SCG. Although SCG signals are widely used in literature, further investigations are needed to understand the genesis of their patterns under different pathophysiological conditions. The goal of this study is to improve our understanding of the origin of SCG signals by simulating the transmission of cardiac motion reaching the chest surface using finite element method, and linking back the patterns of the simulated SCG signals to specific cardiac events. The computational domain, extracted from 4D computed tomography (CT) images of a healthy subject, comprised the lungs, ribcage, and chest muscles and fat. Using the Lukas-Kanade algorithm, the cardiac wall motion was extracted from the 4D CT scan images and was used as a displacement boundary condition. The elastic material properties were assigned to the lungs, muscles, fat, and rib cage. The dorsoventral SCG component from the finite element modeling was compared with two actual SCG signals obtained from the literature. The left ventricular volume was also calculated from the CT scans and was used to interpret the SCG waveforms. Important cardiac phases were labeled on the SCG signal extracted from the computationally modeled acceleration map near the xiphoid. This type of analysis can provide insights into various cardiac parameters and SCG patterns corresponding to the mitral valve closing, mitral valve opening, aortic valve opening, and aortic valve closure. These findings suggested the effectiveness of this modeling approach in understanding the underlying sources of the SCG waveforms.