Computational Modeling of Cardiovascular-Induced Chest Vibrations: A Review and Practical Guide for Seismocardiography Simulation

Abstract

This paper presents a comprehensive examination of finite element modeling (FEM) approaches for seismocardiography (SCG), a non-invasive method for assessing cardiac function through chest surface vibrations. The paper provides a comparative analysis of existing FEM approaches, exploring the strengths and challenges of various modeling choices in the literature. Additionally, we introduce a sample framework for developing FEM models of SCG, detailing key methodologies from governing equations and meshing techniques to boundary conditions and material property selection. This framework serves as a guide for researchers aiming to create accurate models of SCG signal propagation and offers insights into capturing complex cardiac mechanics and their transmission to the chest surface. By consolidating the current methodologies, this paper aims to establish a reference point for advancing FEM-based SCG modeling, ultimately improving our understanding of SCG waveforms and enhancing their reliability and applicability in cardiovascular health assessment.

Keywords: computational modeling; finite element modeling; heart vibrations; seismocardiography.

FIGURE 1

(a) SCG signals of a healthy subject in 3 directions: X, Y, and Z are in the right to left, head‐to‐foot, and dorsoventral directions, respectively [3, 4]. (b) Placement of the sensor on the chest to measure triaxial SCG. The sensor shown in the figure can also measure ECG signals simultaneously. (c) Two types of accelerometers used in SCG measurement: Shimmer3 Ebio (ShimmerSensing, Ireland) and 356A32 (PCB Piezotronics, Depew, NY). (d) Dorsoventral SCG signals of a subject with valvular heart disease [10] and another subject during standard right heart catheterization (RHC) procedure [11] during one cardiac cycle (an ECG P–P interval).

FIGURE 2

The effect of chest soft tissue stiffness on the dorsoventral SCG waveform modeled at the 4th intercostal space [24].

FIGURE 3

(a) and (c) Modeled SCG distributions on the chest surface. (b) and (d) Accelerations beneath the chest muscle within the thoracic cavity [24].

FIGURE 4

An overview of the current SCG modeling studies.

FIGURE 5

Computationally modeled dorsoventral SCG maps at the timing of key fiducial points. (a) Mitral valve closure, MC. (b) Isovolumic contraction, IC. (c) Aortic valve opening, AO. (d) Aortic valve closure, AC. (e) Mitral valve opening, MO. (f) These fiducial points are labeled on the dorsoventral SCG signal measured at the 5th intercostal space. All measurements are in $\mathrm{mm}/{\mathrm{s}}^2$ [24].

FIGURE 6

Overview of a sample image‐based patient‐specific FEM of SCG signals. The framework begins with the processing of MRI or CT scan images to reconstruct the 3D geometry of the heart and surrounding thoracic structures. Cardiac wall motion is then extracted using motion tracking algorithms like the Lucas–Kanade method [65, 66, 67]. This motion data is applied as input boundary conditions to the 3D geometry within the FEM solver. The final steps involve mesh generation and setting up the analysis parameters, ultimately allowing for the computation and analysis of chest surface acceleration.