Digital twin-based investigation of seismocardiogram sensitivity to tissue mechanics and myocardial motion.

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.