Robust real‐time inversion of electrical impedance tomography data for human lung ventilation monitoring

This work presents an innovative strategy for the real‐time monitoring of the human lungs from electrical impedance tomography (EIT) data. The inverse problem at hand is solved within the learning‐by‐examples (LBE) framework by formulating the estimation of the lungs conductivity as a regression problem. Toward this end, the partial least squares (PLS) feature extraction technique is integrated within an adaptive sampling scheme to generate optimal (ie, highly‐informative and low cardinality) training sets for training a support vector regressor (SVR). Accurate predictions are then performed in the on‐line testing phase with remarkable robustness to the noise.