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.