Session: 06-02-01: Vibration and Acoustics in Biomedical Applications

Paper Number: 145441

145441 - Investigating Seismicardiogram Patterns: A Computational Modeling of Cardiac Wall Motion Propagation to the Chest Surface 

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. During contraction and relaxation of the heart, different types of motion occur within the cardiac wall including longitudinal, radial, circumferential, and twisting motions. These movements are transmitted to the organs around the heart and eventually damped onto the chest surface, where it manifests 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 objective of this study is to improve our understanding of the origin of SCG signals by simulating the propagation of heart movements to the chest surface and linking back the patterns of the simulated SCG signals to specific cardiac events. For this purpose, this study presents a 3D modeling of SCG signals using finite element method (FEM) and medical image processing. The computational domain, extracted from computed tomography (CT) images, comprised the lungs, ribcage, muscles, and fat of a healthy adult subject. Using the Lukas-Kanade algorithm, the cardiac wall motion was extracted from 4D CT scan images and was used as a displacement boundary condition. Fixed boundary conditions were assumed at both end face of the ribs and the posterior side of the lungs, adjacent to the spine. The FEM discretized the computational domain using tetrahedral elements. The elastic material properties were assigned to the lungs, muscles, fat, and rib cage. The total duration of a cardiac cycle was assumed to be 1 s, divided into 120 timesteps. The timestep study and mesh study procedure was conducted using transient structural simulation in ANSYS. Equations of motion were solved to calculate the chest surface acceleration map at a sampling frequency of 120 Hz. A bandpass filter with cutoff frequencies of 1-30 Hz was employed to remove the low frequency respiration noise and the higher frequency chest vibrations. To mitigate potential boundary effects due to filtering, a signal representing 10 cardiac cycles was synthesized by concatenating the original SCG signal. In this study, the dorsoventral SCG signal obtained from FEM was compared with an SCG signal recorded by an accelerometer. The left ventricular volume was also calculated from CT scan images. Integrating this data with the SCG graph allowed for the interpretation of the SCG patterns. The fiducial points on the SCG signal extracted from the acceleration map near the xiphoid were determined. These fiducial points provided insights into various cardiac parameters, including SCG patterns corresponding to the mitral valve closing, mitral valve opening, aortic valve opening, and aortic valve closure. The results of this study suggested that this modeling method was effective in understanding the underlying sources of the SCG waveforms. Future work involves validating the method by comparing the simulated SCG waveform with gold-standard SCG signals measured by accelerometer arrays. Furthermore, incorporating more realistic material properties into the model would enhance its accuracy. Overall, this study provides a promising approach to modeling the SCG signal and may have important implications for the diagnosis of cardiovascular diseases.

Presenting Author: Mohammadali Monfared Mississippi State University

Presenting Author Biography: Mohammadali Monfared is a PhD student of Biomedical Engineering at Mississippi State University. He holds an MSc in Mechanical Engineering with a focus on Energy Conversion and Fluid Mechanics from Shiraz University. He also earned a BSc in Mechanical Engineering with a specialization in Fluid Mechanics from Persian Gulf University.

Authors:

Mohammadali Monfared Mississippi State University
Peshala Thibbotuwawa Gamage Florida Institute of Technology
Amirtahà Taebi Mississippi State University