IP Library › Granted Patent US 12,322,044
Granted Patent B2
US 12,322,044 · App. 18/895,382 · Granted Jun 3, 2025

Computational localization of fibrillation sources

Inventors: David E. Krummen (Del Mar, CA); Andrew D. McCulloch (San Diego, CA); Christopher T. Villongco (San Diego, CA); Gordon Ho (San Diego, CA)
Assignee: The Regents of the University of California
G06T17/20A61B5/341A61B5/361A61B5/6823A61B5/7246A61B5/7278A61B5/7445G06T5/20G06T7/0012G06T2207/10081G06T2207/10088G06T2207/10116G06T2207/10121G06T2207/30048
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Quick Facts
Patent No.
US 12,322,044
App. No.
18/895,382
Granted
Jun 3, 2025
Kind
B2
Abstract

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.

Claims (62)

1. A system comprising:

at least one processor; and

at least one memory storing instructions which, when executed by the at least one processor, cause operations comprising:

receiving a patient representation of electrical activation of a patient's heart, the patient representation of electrical activation being an electrocardiogram;

generating computational models of hearts that are not specific to the patient's heart, each computational model generated based on simulating electrical activity of a heart that is not specific to the patient;

for each generated computational model, generating a simulated representation of electrical activation representing simulated electrical activity of that computational model, the simulated representation of electrical activation being an electrocardiogram;

comparing the patient representation of electrical activation against one or more simulated representations of electrical activation to identify at least one matched simulated representation of electrical activation of a heart;

generating, based on the matched simulated representation of electrical activation an illustration that includes as representation of the patient's heart, the matched simulated representation of electrical activation, and one or more fibrillation sources in the patient's heart; and

displaying, via a user interface, at least a portion of the computational model.

2. The system of claim 1 , wherein the comparing comprises determining, for each of the one or more simulated representations of electrical activation, a correlation factor between the simulated representation of electrical activation and the patient representation of electrical activation, and wherein the at least one matched simulated representation of electrical activation is identified as the simulated representation of electrical activation with a highest correlation factor.

3. The system of claim 1 , wherein the one or more fibrillation sources comprises a rotor or a focal source.

4. The system of claim 1 wherein the one or more fibrillation sources comprises a source of ventricular fibrillation or atrial fibrillation.

5. A method performed by one or more computing systems for identifying a map of electrical activation of the heart of a patient, the method comprising:

receiving one or more patient electrocardiograms (ECGs) collected from the patient;

for each of a plurality of source locations of electrical activations, heart shapes, and electrical conduction properties of a heart, generating a computational model that includes based on that heart shape, those electrical conductance properties of a heart, and that source location wherein the computational model is a general model that is not specific to the heart of the patient;

for each of a plurality of the computational models,

simulating electrical activity of a heart, based on that computational model, to generate a map of electrical activation; and

generating, for that generated map of electrical activation, a simulated representation of that electrical activation, a simulated representation of electrical activation being an ECG;

identifying one or more maps of electrical activation of the computational models based on one or more of the simulated representations of electrical activation and one or more of the patient representations of electrical activation; and

displaying, via a user interface, an indication of a heart illustrating an identified maps of electrical activation.

6. The method of claim 5 further comprising displaying an indication of the source location of the computational model based on which the identified map of electrical activation that is illustrated was generated.

7. The method of claim 5 wherein the simulating of electrical activity of a heart is based on a mono-domain or a bi-domain model.

8. The method of claim 5 wherein the plurality of source locations are generated using machine learning.

9. The method of claim 5 wherein a computational model further includes orientation of muscle fiber of a heart.

10. The method of claim 5 wherein the simulating of electrical activity simulates propagation of action potential through the heart.

11. The method of claim 5 wherein the source location is of a focal source.

12. The method of claim 5 wherein the source location is the location of a source of an arrhythmia.

13. The method of claim 5 wherein the one or more patient representations of electrical activation are generated based on a sensor device that is applied to the surface of the patient's skin.

14. One or more computing systems for identifying a map of electrical activation of the heart of a patient, the one or more computing systems comprising:

at least one processor; and

one or more machine-readable mediums that store:

one or more patient representations of electrical activation of the patient's heart; and

instructions which, when executed by the at least one processor, cause operations comprising:

for each of a plurality of source locations of electrical activation, heart shapes, and electrical conduction properties of a heart, generating a computational model that includes that heart shape, those electrical conductance properties of a heart, and that source location wherein the computational model is not specific to heart shape of the patient's heart and electrical conduction properties of the patient's heart;

for each of a plurality of the computational models,

simulating electrical activity of a heart, based on that computational model, to generate a map of electrical activation; and

generating, based on that generated map of electrical activation, a simulated representation of electrical activation;

identifying one or more maps of electrical activation of the computational models based on one or more of the simulated representations of electrical activation and one or more of the patient representations of electrical activation.

15. The one or more computing systems of claim 14 wherein the one or more machine-readable mediums that store instructions which, when executed by the at least one processor, cause operations comprising displaying, via a user interface, an indication of a heart illustrating an identified map of electrical activation.

16. The one or more computing systems of claim 14 wherein the one or more machine-readable mediums that store instructions which, when executed by the at least one processor, cause operations comprising displaying an indication of the source location of the computational model based on which the identified map of electrical activation that is illustrated was generated.

17. The one or more computing systems of claim 14 wherein the simulating of electrical activity of a heart is based on a mono-domain or a bi-domain model.

18. The one or more computing systems of claim 14 wherein the plurality of source locations are generated using machine learning.

19. The one or more computing systems of claim 14 wherein a computational model further includes orientation of muscle fiber of a heart.

20. The one or more computing systems of claim 14 wherein the simulating of electrical activity simulates propagation of action potential through the heart.

21. The one or more computing systems of claim 14 wherein the source location is a focal source.

22. The one or more computing systems of claim 21 wherein the focal source is the source of an arrhythmia.

23. The one or more computing systems of claim 14 wherein the source location is the location of a source of an arrhythmia.

24. A method performed by one or more computing systems to identify a map of electrical activations of the hearts of patients, the method comprising:

for each patient, receiving one or more patient representations of electrical activation of that patient's heart;

for each of a plurality heart shapes, electrical conductance properties of a heart, and source locations of electrical activations, generating a computational model that includes that heart shape, those electrical conductance properties, and that source location;

for each of a plurality of the computational models,

simulating electrical activity of a heart, based on that computational model, to generate a map of electrical activations; and

generating, based on that generated map of electrical activation, a generated representation of electrical activation; and

for each patient,

identifying a map of electrical activation based on one or more of the generated representations of electrical activation and the patient representation of electrical activation of that patient; and

displaying, via a user interface, an indication of a heart illustrating an identified map of electrical activation.

25. The method of claim 24 further comprising for each patient, displaying an indication of the source location of a computational model based on which the identified map of electrical activation that is illustrated was generated.

26. The method of claim 24 wherein the simulating of electrical activity of a heart is based on a mono-domain or a bi-domain model.

27. The method of claim 24 wherein the plurality of source locations are generated using machine learning.

28. The method of claim 24 wherein a computational model further includes orientation of muscle fiber of a heart.

29. The method of claim 24 wherein the simulating of electrical activity simulates propagation of action potential through the heart.

30. The method of claim 24 wherein the source location is the location of a source of an arrhythmia.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 13, 2025
From: KRUMMEN, DAVID E.; MCCULLOCH, ANDREW D.; VILLONGCO, CHRISTOPHER; HO, GORDON
To: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA
Reel/Frame 069890/0637 →
Continuity (6)
Continuation 18318661 · May 16, 2023
Continuation 17833109 · Jun 6, 2022
Continuation 16295934 · Mar 7, 2019
Continuation 15389245 · Dec 22, 2016
Provisional Application 62271113 · Dec 22, 2015
Related Publication 20250014283A1 · Jan 9, 2025
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