Coronary artery stenosis is characterized by the obstruction or narrowing of coronary arteries, impairing blood flow to the cardiac muscle. Cardiovascular diseases rank among the leading global causes of death. Understanding the mechanisms, risk factors, and impacts of coronary artery stenosis (CAD) is essential for developing effective strategies for prevention, early diagnosis, and treatment, aiming to enhance cardiovascular health and reduce adverse consequences associated with this condition. Previous studies explore the potential of acoustic devices in non-invasive CAD detection through the analysis of cardiac sounds. A theoretical model utilized a cardiac microphone and signal processing to generate sounds in partially obstructed coronary arteries. Emphasizing the model's sensitivity to geometric and dynamic parameters, this article integrates Bond Graph (BG) models into the analog sound circuit, aiming to develop a dynamic representation of biological processes in complex systems like coronary arteries. This research seeks to construct a comprehensive mathematical model using a state-space approach to enhance understanding, diagnostics, and interventions in cardiovascular conditions. The utilization of BG demonstrated notable similarity to the pathology of the disease, providing an accurate representation of its effects. The model's sensitivity to geometric and dynamic parameters allowed an in-depth understanding of the specific physiological characteristics of the disease, highlighting its ability to reproduce patterns observed in patients with CAD.