Session: 16-01-01: Government Agency Student Poster Competition

Paper Number: 150280

150280 - Finite Element Modeling of Seismocardiogram Signals for Enhanced Screening of Congenital Heart Diseases 

Congenital heart diseases (CHDs) are among the most common birth defects. They affect nearly 1% of all live births globally and according to the Centers for Disease Control and Prevention, about 40,000 infants are born with CHDs each year in the United States alone. This emphasizes the need for advanced screening methods to detect these conditions early on after birth. In that regard, seismocardiography (SCG) is a non-invasive technique that captures the vibrations induced by the heart on the chest surface, offering a promising avenue for assessing cardiac function. However, the genesis of SCG signal patterns under various pathophysiological conditions, including CHDs, remains incompletely understood. Computational modeling can bridge this gap by simulating how cardiac motion translates into SCG signals and providing information about the underlying mechanical processes. Digital twin models, among these computational models, in particular, present a powerful tool for this purpose. These models create a virtual representation of a patient’s heart for personalized simulations and analyses. For instance, to investigate how conditions such as coarctation of the aorta (COA), tetralogy of Fallot (TOF), or patent ductus arteriosus (PDA) alter SCG signals, it is necessary to collect extensive SCG data for each specific case. This process is time-consuming, and data acquisition must be repeated for each new case to create a comprehensive dataset. On the other hand, a digital twin model can simulate the impact of COA, TOF or PDA on the SCG signals of infants. This approach provides a detailed understanding of how a specific condition affects the SCG patterns. This study aims to elucidate the origins of SCG signals by simulating the transmission of cardiac motion to the chest surface and correlating simulated SCG patterns with specific congenital cardiac conditions.

The methodology involved constructing a computational domain from 4D CT images of a healthy subject, encompassing the lungs, ribcage, chest muscles, and fat. The Lucas-Kanade algorithm was employed to track the time-resolved displacements of the heart wall in the 4D CT data throughout a cardiac cycle, which were then used as displacement boundary conditions in the finite element model. The material properties were assigned to the various tissues from previous studies, and the model’s output was compared with actual SCG signals from the literature. In addition, the left ventricular volume was calculated from the CT scans to interpret the SCG waveforms, and key cardiac features were identified on the SCG signals.

Preliminary results demonstrated the feasibility of our methodology, the successful simulation of SCG signals in the dorsoventral direction, and the identification of fiducial points on the SCG waveforms, corresponding to cardiac events such as mitral and aortic valve closures and openings. This modeling approach shows promise in understanding the sources of SCG waveforms, which could have important implications for screening CHDs. In addition to expanding the sample size, validating and conducting a sensitivity analysis of the simulated SCG signal, future work may involve developing a digital twin model based on these simulations. In this model, instead of using the cardiac wall motion as the displacement boundary condition, the blood circulation inside the heart chambers will be coupled with the heart wall for a fluid-structure interaction modeling and the propagation of these movements to the chest surface will be investigated.

Presenting Author: Mohammadali Monfared Mississippi State University

Presenting Author Biography: Mohammadali Monfared holds a Master's degree in Mechanical Engineering, specializing in Energy Conversion and Fluid Mechanics, from Shiraz University, and a Bachelor of Science in Mechanical Engineering, with a focus on Fluid Mechanics, from Persian Gulf University. His undergraduate research involved studying the longitudinal free vibrations of single-walled carbon nanotubes. For his Master's dissertation, titled "Simulation of Aortic Valve and the Left Ventricle during Systole in a Beating Heart," he focused on the biomechanics of heart simulation, specifically addressing Hypertrophic Cardiomyopathy (HCM). He is currently pursuing a Ph.D. in Biomedical Engineering at Mississippi State University under supervision of Dr. Amirtaha Taebi, where he is working on computational modeling of SCG signals.

Authors:

Mohammadali Monfared Mississippi State University
Peshala Thibbotuwawa Gamage Florida Institute of Technology
William Van Wurm University of Mississippi School of Medicine (currently working at Mississippi Pediatric Associates)
Bahram Kakavand Nemours Children's Hospital, Florida
Amirtaha Taebi Mississippi State University