Data-Driven Approach for Left Ventricular Volume Estimation using Intracardiac Admittance

Current Directions in Biomedical Engineering, vol. 11, pp. 218–221

Abstract

Abstract Accurate estimation of left ventricular volume (LVV) is essential for managing cardiovascular diseases such as heart failure and myocardial infarction. A promising approach for continuous LVV estimation is using intracardiac admittance measurements. The prevalent methods in literature are based on analytical models, for which several simplifying assumptions had to be made. It is not clear if the models are the optimal choice to describe the relationship between the admittance measurement and the LVV. This study explores intracardiac admittance measurements as a promising alternative for continuous LVV estimation. We utilized symbolic regression to derive mathematical models describing the relationship between intracardiac admittance measurements and LVV without predefined structures and compare their performance with classical models from literature. Through simulations based on a cylindrical left ventricle model, we rediscovered the model of Baan and identified further interpretable models that outperform the approaches by Baan and Wei on our dataset. Our findings support that symbolic regression can uncover meaningful patterns in biomedical data, paving the way for more efficient diagnostic tools in cardiovascular health monitoring.

Authors 2

1. Steffen K. Leonhardt Aachen Chair for Medical Information Technology

   RWTH Aachen University

   Affiliation as printed

   Chair for Medical Information Technology, RWTH Aachen University, Aachen , Germany

2. Marian Walter Aachen Chair for Medical Information Technology

   RWTH Aachen University

   Affiliation as printed

   Chair for Medical Information Technology, RWTH Aachen University, Aachen , Germany