IDENTIFYING AN ATTRIBUTE OF AN ELECTROMAGNETIC SOURCE CONFIGURATION BY MATCHING SIMULATED AND PATIENT DATA

• US 20190328456A1
• Filed: 07/23/2018
• Published: 10/31/2019
• Est. Priority Date: 04/26/2018
• Status: Active Grant

• Current Assignee: Vektor Medical, Inc.
• Original Assignee: Vektor Medical, Inc.
• Inventors: Villongco, Christopher
• Granted Patent: US 11,622,732 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 2034/105 Modelling of the patient, e.g. for ligaments or bones
• CPC Class Codes: A61B 34/10 Computer-aided planning, simulation or modelling of surgical operations
• CPC Class Codes: A61B 5/02028 Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction
• CPC Class Codes: A61B 5/1075 for measuring dimensions by non-invasive methods, e.g. for determining thickness of tissue layer A61B8/0858 takes precedence
• CPC Class Codes: A61B 5/25 Bioelectric electrodes therefor
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/319 Circuits for simulating ECG signals
• CPC Class Codes: A61B 5/339 Displays specially adapted therefor
• CPC Class Codes: A61B 5/341 Vectorcardiography [VCG]
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/361 Detecting fibrillation
• CPC Class Codes: A61B 5/363 Detecting tachycardia or bradycardia
• CPC Class Codes: A61B 5/364 Detecting abnormal ECG interval, e.g. extrasystoles, ectopic heartbeats
• CPC Class Codes: A61B 5/6805 Vests
• CPC Class Codes: A61B 5/7235 Details of waveform analysis A61B5/349 takes precedence
• CPC Class Codes: A61B 5/725 using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/7425 : Displaying combinations of multiple images regardless of image source, e.g. displaying a reference anatomical image with a live image geometric transforms for image registration G06T3/14; enhancement using image fusion G06T5/50; algorithms for registration of images G06T7/30
• CPC Class Codes: A61B 5/743 : Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots
• CPC Class Codes: A61B 5/7435 : Displaying user selection data, e.g. icons in a graphical user interface
• CPC Class Codes: A61B 5/7445 : Display arrangements, e.g. multiple display units
• CPC Class Codes: A61B 6/032 : Transmission computed tomography [CT]
• CPC Class Codes: A61B 6/503 : for diagnosis of the heart
• CPC Class Codes: G06F 18/22 : Matching criteria, e.g. proximity measures
• CPC Class Codes: G06F 18/23213 : with fixed number of clusters, e.g. K-means clustering
• CPC Class Codes: G06F 18/2453 : non-linear, e.g. polynomial classifier
• CPC Class Codes: G06F 30/20 : Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G06N 20/00 : Machine learning
• CPC Class Codes: G06N 20/10 : using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 3/042 : Knowledge-based neural networks; Logical representations of neural networks
• CPC Class Codes: G06N 3/044 : Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 3/045 : Combinations of networks
• CPC Class Codes: G06N 3/047 : Probabilistic or stochastic networks
• CPC Class Codes: G06N 3/048 : Activation functions
• CPC Class Codes: G06N 3/08 : Learning methods
• CPC Class Codes: G06N 3/088 : Non-supervised learning, e.g. competitive learning
• CPC Class Codes: G06N 5/04 : Inference or reasoning models
• CPC Class Codes: G06N 5/046 : Forward inferencing; Production systems
• CPC Class Codes: G06N 7/01 : Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06T 17/20 : Finite element generation, e.g. wire-frame surface description, tesselation
• CPC Class Codes: G06T 17/205 : Re-meshing
• CPC Class Codes: G06T 19/20 : Editing of 3D images, e.g. changing shapes or colours, aligning objects or positioning parts
• CPC Class Codes: G06T 2210/41 : Medical
• CPC Class Codes: G06T 3/4007 : based on interpolation, e.g. bilinear interpolation image demosaicing G06T3/4015; edge-driven or edge-based scaling G06T3/403
• CPC Class Codes: G06V 10/00 : Arrangements for image or video recognition or understanding character recognition in images or video G06V30/10
• CPC Class Codes: G06V 10/764 : using classification, e.g. of video objects
• CPC Class Codes: G06V 2201/03 : Recognition of patterns in medical or anatomical images
• CPC Class Codes: G09B 23/285 : for injections, endoscopy, bronchoscopy, sigmoidscopy, insertion of contraceptive devices or enemas
• CPC Class Codes: G09B 23/30 : Anatomical models G09B23/281 - G09B23/288 take precedence
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders

Abstract

Systems are provided for generating data representing electromagnetic states of a heart for medical, scientific, research, and/or engineering purposes. The systems generate the data based on source configurations such as dimensions of, and scar or fibrosis or pro-arrhythmic substrate location within, a heart and a computational model of the electromagnetic output of the heart. The systems may dynamically generate the source configurations to provide representative source configurations that may be found in a population. For each source configuration of the electromagnetic source, the systems run a simulation of the functioning of the heart to generate modeled electromagnetic output (e.g., an electromagnetic mesh for each simulation step with a voltage at each point of the electromagnetic mesh) for that source configuration. The systems may generate a cardiogram for each source configuration from the modeled electromagnetic output of that source configuration for use in predicting the source location of an arrhythmia.

Claims

1. A method performed by a computing system for identifying derived electromagnetic data that matches patient electromagnetic data collected from a patient, the electromagnetic data representing electromagnetic output an electromagnetic source within a body, the method comprising:

for each of a plurality of model source configurations of the electromagnetic source, accessing a mapping of that model source configuration to derived electromagnetic data that is derived based upon that model source configuration;

accessing a patient source configuration representing a source configuration for the electromagnetic source within the patient;

identifying model source configurations that match the patient source configuration; and

identifying, from the derived electromagnetic data to which the identified model source configurations are mapped, derived electromagnetic data that matches the patient electromagnetic data.

2. The method of claim 1 wherein derived electromagnetic data is derived from modeled electromagnetic output generated based on a model source configuration using a computational model of the electromagnetic source.

3. The method of claim 2 wherein the modeled electromagnetic data for a model source configuration includes, for each of a plurality of time intervals, an electromagnetic mesh with a modeled electromagnetic value for each of a plurality of locations of the electromagnetic source.

4. The method of claim 2 further comprising the situation when an identified model source configuration has a value of an anatomical parameter that does not match the value of that anatomical parameter of the patient source configuration, generating adjusted derived electromagnetic data based on the modeled electromagnetic output for that model source configuration and difference in the value.

5. The method of claim 1 wherein a source configuration includes configuration parameters including anatomical parameters and electrophysiology parameters.

6. The method of claim 5 wherein the electromagnetic source is a heart and the configuration parameters are selected from the group consisting of torso anatomy, normal and abnormal cardiac anatomy, normal and abnormal cardiac tissue, scar, fibrosis, inflammation, edema, accessory pathways, congenital heart disease, malignancy, sites of prior ablation, sites of prior surgery, sites of external radiation therapy, pacing leads, implantable cardioverter-defibrillator leads, cardiac resynchronization therapy leads, pacemaker pulse generator location, implantable cardioverter-defibrillator pulse generator location, subcutaneous defibrillator lead location, subcutaneous defibrillator pulse generator location, leadless pacemaker location, other implanted hardware, external defibrillation electrodes, surface ECG leads, surface mapping leads, a mapping vest, and other normal and pathophysiologic feature location or locations within the heart, action potential dynamics for the heart, a conductivity for the heart, and an arrhythmia substrate location.

7. The method of claim 5 wherein the electromagnetic source is a heart and the anatomical parameters include dimensions of chambers of the heart, wall thicknesses of the heart, fiber architecture of the heart, and orientation of the heart.

8. The method of claim 1 wherein the electromagnetic source is a heart and derived electromagnetic data is a cardiogram.

9. The method of claim 1 wherein a model source configuration includes a disorder parameter relating to an attribute of the electromagnetic source such that derived electromagnetic data for the model source configuration is based on that attribute.

10. The method of claim 9 wherein the electromagnetic source is a heart and the attribute is based on an arrhythmia.

11. The method of claim 1 wherein the identifying of derived electromagnetic data that matches the patient electromagnetic data is based on a Pearson correlation coefficient.

12. The method of claim 1 wherein the identifying of derived electromagnetic data that matches the patient electromagnetic data is based on a root-mean-squared error.

13. The method of claim 1 wherein a source configuration includes configuration parameters, the model source configurations include a value for each configuration parameter, and the patient source configuration includes a value for only a proper subset of the configuration parameters.

14. The method of claim 1 wherein the derived electromagnetic data is based on a model orientation of the electromagnetic source and further comprising when a patient orientation of the electromagnetic source of the patient is different from the model orientation, the identifying of the derived electromagnetic data factors in the difference between the model orientation and the patient orientation.

15. The method of claim 14 wherein the electromagnetic source is a heart and the electromagnetic data is a vectorcardiogram and wherein the identifying of the derived electromagnetic data includes generating a rotation matrix based on the difference between the model orientation and the patient orientation and rotating a vectorcardiogram based on the rotation matrix.

16. A method performed by a computing system for generating a classification for a patient based on patient electromagnetic data representing electromagnetic output of an electromagnetic source within the patient, the method comprising:

for each of a plurality of clusters of model source configurations of the electromagnetic source, accessing a classifier for that cluster that is trained based on the model source configurations of that cluster to generate a classification for derived electromagnetic data;

accessing a patient source configuration representing a source configuration for the electromagnetic source within the patient;

identifying a cluster whose model source configurations matches the patient source configuration; and

applying the classifier for the identified cluster to the patient electromagnetic data to generate a classification for the patient.

17. The method of claim 16 wherein derived electromagnetic data is derived from modeled electromagnetic output generated based on a model source configuration using a computational model of the electromagnetic source.

18. The method of claim 16 wherein a source configuration includes configuration parameters including anatomical parameters and electrophysiology parameters.

19. The method of claim 18 wherein the electromagnetic source is a heart and the configuration parameters include a scar location within the heart, an action potential for the heart, and a conductivity for the heart.

20. The method of claim 18 wherein the electromagnetic source is a heart and the configuration parameters include a fibrosis location within the heart, an action potential for the heart, and a conductivity for the heart.

21. The method of claim 18 wherein the electromagnetic source is a heart and the configuration parameters include a pro-arrhythmic substrate location within the heart, an action potential for the heart, and a conductivity for the heart.

22. The method of claim 18 wherein the electromagnetic source is a heart and the anatomical parameters include dimensions of chambers of the heart, wall thicknesses of the heart, fiber architecture of the heart, and orientation of the heart.

23. The method of claim 16 wherein the electromagnetic source is a heart and derived electromagnetic data is a cardiogram.

24. The method of claim 23 wherein the cardiogram is a vectorcardiogram.

25. The method of claim 16 wherein a model source configuration matches the patient source configuration based on a cosine similarity.

26. The method of claim 16 further comprising:

generating clusters of model source configurations; and

for each of the clusters,

for each of the model configuration sources of that cluster,

generating a simulated electromagnetic output of the electromagnetic source based on that model configuration source; and

generating derived electromagnetic data for that model source configuration from the simulated electromagnetic output based on that model source configuration.

27. The method of claim 16 wherein a source configuration includes configuration parameters, the model source configurations include a value for each configuration parameter, and the patient source configuration includes a value for only a proper subset of the configuration parameters.

28. The method of claim 16 wherein the derived electromagnetic data is based on a model orientation of the electromagnetic source and further comprising when a patient orientation of the electromagnetic source of the patient is different from the model orientation, adjusting the patient electromagnetic data based on the difference between the model orientation and the patient orientation.

29. The method of claim 16 wherein the electromagnetic source is a heart and the classification is based on source location of an arrhythmia.

30. A computing system for identifying a model cardiogram that matches a patient cardiogram collected from a patient, the computing system comprising:

one or more computer-readable storage mediums storing

for each of a plurality of model source configurations of a heart, a modeled cardiogram for that model source configuration; and

a patient source configuration representing the patient'"'"'s heart;

computer-executable instructions that, when executed control, the computing system to;

identify model source configurations that match the patient source configuration; and

identify, from the modeled cardiograms for the identified model source configurations, those modeled cardiograms that match the patient cardiogram; and

one or more processor for executing the computer-executable instructions stored in the one or more computer-readable storage mediums.

31. The computing system of claim 30 wherein a modeled cardiogram is generated from modeled electromagnetic output of a heart with a model source configuration using a computational model of the heart.

32. The computing system of claim 30 wherein a cardiogram is adjusted based on differences in values of an anatomical parameter for a model source configuration and the patient source configuration.

33. The computing system of claim 30 wherein the model source configurations include a scar location within the heart, an action potential for the heart, a conductivity for the heart, and an arrhythmia location.

34. The computing system of claim 30 wherein the model source configurations include a fibrosis location within the heart, an action potential for the heart, a conductivity for the heart, and an arrhythmia location.

35. The computing system of claim 30 wherein the model source configurations include a pro-arrhythmic substrate location within the heart, an action potential for the heart, a conductivity for the heart, and an arrhythmia location.

36. The computing system of claim 30 wherein the model source configurations include dimensions of chambers of the heart, wall thicknesses of the heart, fiber architecture of the heart, and orientation of the heart.

37. The computing system of claim 30 wherein a source configuration includes configuration parameters, the model source configurations include a value for each configuration parameter, and the patient source configuration includes a value for only a proper subset of the configuration parameters.

38. The computing system of claim 30 wherein modeled cardiograms are based on a model orientation of a heart and when a patient orientation of the heart is different from the model orientation, the identifying of modeled cardiograms factors in the difference between the model orientation and the patient orientation.