Classifying lung congestion in congestive heart failure using electrical impedance - a 3D model

In congestive heart failure (CHF) patients an increased interstitial fluid leads to collection of large amounts of fluids in the lungs which are major cause of mortality. Classifying and monitoring pulmonary congestions is a significant clinical challenge, due to lack of direct access to the pleural cavity. In this study, we investigate the feasibility of the Parametric Electrical Impedance Tomography (pEIT) technique in classifying and monitoring pleural effusion. The investigation is based on pEIT with a reduced number of electrodes applied in a computerized 3D model of the human thorax. The Forward Problem for Poisson's equation was implemented using Finite Volume Method (FVM) to estimate the potentials developed on the body surface. Significant linear regression (r-square>0.81) was found in 7 and 6 out of 8 independent projections for the right and the left lung, respectively, indicating an increase in surface potential while increasing lungs fluids. Moreover, the study results show that the projection's sensitivity is higher for cross sectional projections during pleural effusion. Hence, monitoring and classifying pleural effusion can be achieved with the pEIT technique making it feasible in monitoring lung fluid status in patients with pleural effusion.