Machine Learning based Estimation of Aortic Pressure Curves by Electrical Impedance Tomography

Central aortic pressure is a key hemodynamic parameter to monitor and target in clinical practice. As the gold standard method is highly invasive and conventional noninvasive methods are not long-term compatible or inaccurate, the need for alternative monitoring capabilities arises. Electrical impedance tomography (EIT) is a non-invasive monitoring technique using an electrode belt around the torso. In this paper, EIT recordings from an in-vivo animal model and simultaneously recorded central aortic pressure measurements from invasive catheters are used to train a convolutional neural network predicting aortic pressure curves from EIT voltages. Different parametric representations of the aortic pressure time series are considered to reduce network complexity. A hyperparameter tuning is conducted to optimize the network. Results demonstrate that the estimation of aortic pressure curves by a trained network is feasible even on unknown test data, however, random offsets are observed.