Modeling of cardiovascular circulation for the early detection of coronary arterial blockage

Karan Jain1, Srinivasu Maka1, Amit Patra1

  • 1Department of Electrical Engineering, Indian Institute of Technology Kharagpur 721302, India.

Insights

This study developed a cardiovascular model to detect early coronary artery disease by analyzing coronary arterial resistance. Increased resistance indicates blockage, reducing heart function and serving as an early detection index.

Area of Science:

  • Cardiovascular physiology
  • Biomedical engineering
  • Nonlinear systems analysis

Background:

  • Coronary artery disease (CAD) develops gradually, often delaying diagnosis until severe.
  • Early detection of CAD is crucial for preventive action and improved patient outcomes.
  • Current diagnostic methods may not identify early-stage arterial blockages effectively.

Purpose of the Study:

  • To develop an early-stage coronary arterial blockage detection method using cardiovascular modeling.
  • To explicitly model the coronary arterial path, separating it from the systemic load.
  • To establish coronary resistance as a quantifiable index for CAD detection.

Main Methods:

  • Modeled cardiovascular circulation as a sixth-order nonlinear system using a pressure-voltage analogy.
  • Separated the coronary circulation path from the systemic load for detailed analysis.
  • Estimated coronary arterial resistance from blood pressure measurements.

Main Results:

  • Increased coronary resistance was correlated with reduced left ventricular ejection fraction.
  • A critical threshold for coronary resistance was identified, beyond which systolic heart function deteriorates.
  • The model demonstrated promising results in detecting CAD in a sample of 20 subjects.

Conclusions:

  • The developed cardiovascular model effectively detects early coronary arterial blockage by estimating coronary resistance.
  • Reduced left ventricular ejection fraction serves as a reliable index for identifying CAD.
  • This modeling approach offers a promising tool for early CAD diagnosis and intervention.

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