Automatic tuning of respiratory model for patient-based simulation

This paper is an overview of a method recently published in a biomedical journal (IEEE Transactions on Biomedical Engineering, http://tbme.embs.org). The method is based on an optimisation technique called ``evolutionary strategy'' and it has been designed to estimate the parameters of a complex 15-D respiration model. This model is adaptable to account for patient's specificities. The aim of the optimisation algorithm is to finely tune the model so that it accurately fits real patient datasets. The final results can then be embedded, for example, in high fidelity simulations of the human physiology. Our algorithm is fully automatic and adaptive. A compound fitness function has been designed to take into account for various quantities that have to be minimised (here topological errors of the liver and the diaphragm geometries). The performance our implementation is compared with two traditional methods (downhill simplex and conjugate gradient descent), a random search and a basic real-valued genetic algorithm. It shows that our evolutionary scheme provides results that are significantly more stable and accurate than the other tested methods. The approach is relatively generic and can be easily adapted to other complex parametrisation problems when ground truth data is available.