This codes implements an algorithm to learn the Purkinje network of a patient, given a cardiac anatomy and a reference electrocardiogram (ECG). It is based on:
- The Purkinje network is modeled as a fractal tree, which depends on a set of geometrical parameters.
- The cardiac activation is obtained with the Eikonal equation. Then, with the activation times we compute the surface ECG.
With these models, the necessary steps to learn the network are:
- A Bayesian optimization approach is used to find networks that produce ECGs similar to the reference.
- With approximate Bayesian computation we estimate the posterior distribution of the Purkinje network parameters. Then, with rejection sampling we obtain samples from this distribution.
In ECG_BO_demo.ipynb you can run the Bayesian optimization and then find the posterior samples, here it uses a simplified cardiac geometry. In ECG_BO_results.ipynb you can see the simulation results: the ECGs of the posterior samples and a pairplot showing the parameters distributions.
