Computational localization of fibrillation sources
A system for computational localization of fibrillation sources is provided. In some implementations, the system performs operations comprising generating a representation of electrical activation of a patient's heart and comparing, based on correlation, the generated representation against one or more stored representations of hearts to identify at least one matched representation of a heart. The operations can further comprise generating, based on the at least one matched representation, a computational model for the patient's heart, wherein the computational model includes an illustration of one or more fibrillation sources in the patient's heart. Additionally, the operations can comprise displaying, via a user interface, at least a portion of the computational model. Related systems, methods, and articles of manufacture are also described.
1. A method performed by one or more computing systems for identifying a course of action for treating an arrhythmia of a patient, the method comprising:
accessing a patient cardiogram collected from the patient;
identifying cycles of the patient cardiogram relating to the arrhythmia;
for each of a plurality of cycles,
identifying a computational model based on a correlation between the patient cardiogram and a model cardiogram of the computational model; and
identifying an arrhythmia source location associated with the identified model cardiogram;
creating a composite computational model based on the identified source locations;
creating a new computational model based on the composite computational model with an identified source location removed; and
evaluating a change in the arrhythmia between the composite computational model and the new computational model to help identify the course of action.
2. The method of claim 1 , wherein the arrhythmia is a fibrillation.
3. The method of claim 1 , further comprising displaying at least a portion of a composite heart model of the composite computational model and a new heart model of the new computational model, the composite heart model indicating percent of arrhythmia cycles associated with the composite computational model and the new heart model indicating percent of arrhythmia cycles associated with the new computational model.
4. The method of claim 3 , wherein the displayed portion is a fibrillatory source map.
5. The method of claim 4 , wherein the fibrillatory source map is color-coded based on percent of arrhythmia cycles associated with a source location.
6. The method of claim 1 , wherein the course of action is based on percent of arrhythmia cycles of the composite computational model and the new computational model.
7. The method of claim 1 , wherein the composite computational model is based on the identified computational models.
8. The method of claim 1 , further comprising repeating the creating of a composite computational model and a new computational model and evaluating the change in arrhythmia based on various combinations of source locations removed.
9. The method of claim 1 , wherein the computational models are identified from a filtered set of computational models based on shape of the patient's heart.
10. The method of claim 1 , wherein the computational models are generated based on heart models having one or more of varying shapes, fiber orientations, scars, source types, and source locations.
11. The method of claim 1 , wherein the computational models are not patient-specific computational models.
12. The method of claim 1 , wherein the computational models are generated by simulating electrical activations based on an ionic model of a heart.
13. One or more computing systems for identifying a course of action for treating an arrhythmia of a patient, the one or more computing systems comprising:
one or more computer-readable storage mediums that store computer-executable instructions for controlling the one or more computing systems to:
identify patient cycles within a patient cardiogram of the patient;
for each of a plurality of the patient cycles,
identify a computational model having cycle that correlates to the patient cycle; and
identify a source location associated with the computational model;
generate a first computational model that is associated with identified source locations; and
generate a second computational model that is associated with a subset of the identified source locations, the subset not including a removed source location
wherein comparison of the first computational model and the second computational model to assess whether removal of the removed source location would be beneficial to the patient; and
one or more processors for controlling the one or more computing systems to execute one or more of the computer-executable instructions.
14. The one or more computing systems of claim 13 , wherein the computational models are not patient-specific computational models.
15. The one or more computing systems of claim 13 , wherein the instructions include instructions to display at least a portion of a first heart model of the first computational model and a second heart model of the second computational model, the first heart model indicating percent of arrhythmia cycles associated with the first computational model and the second heart model indicating percent of arrhythmia cycles associated with the second computational model.
16. The one or more computing systems of claim 15 , wherein the displayed portion is a fibrillatory source map.
17. The one or more computing systems of claim 13 , wherein the instructions include instructions to repeat the generating of the first computational model and the second computational for various subsets of the identified source locations.
18. The one or more computing systems of claim 13 , wherein the first computational model and the second computational model are not patient-specific computational models.
19. One or more computer-readable storage mediums that store computer-executable instructions for controlling one or more computing systems to:
identify patient cycles within a patient cardiogram of a patient;
for each of a plurality of the patient cycles, identify an arrhythmia source location associated with a computational model having cycle that correlates to the patient cycle;
generate a first computational model that is associated with identified arrhythmia source locations;
generate a second computational model that is associated with a subset of the identified arrhythmia source locations, the subset not including a removed arrhythmia source location; and
output an indication of differences between the first computational model and the second computational model to help in guide treatment of an arrhythmia
wherein the first computational model and the second computational model are not patient-specific computational models.
20. The one or more computer-readable storage mediums of claim 19 , wherein the instructions include instructions to repeat the generating of the first computational model and the second computational for various subsets of the identified arrhythmia source locations to provide predictive ablation outcome resulting from an ablation targeting a removed arrhythmia source location.