A map of cardiac activation wavefronts can be created from a plurality of mesh nodes, each of which is assigned a conduction velocity vector. Directed edges are defined to interconnect the mesh nodes, and weights are assigned to the directed edges, thereby creating a weighted directed conduction velocity graph. A user can select one or more points within the weighted directed conduction velocity graph (which do not necessarily correspond to nodes), and one or more cardiac activation wavefronts passing through these points can be identified using the weighted directed conduction velocity graph. The cardiac activation wavefronts can then be displayed on a graphical representation of the cardiac geometry.

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.

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.

A flexible circuit that is substantially planar may be assembled into an electrophysiologic catheter. The flexible circuit may include various location sensing portions and force sensing portions. The flexible circuit may be deformed in a manner that improves the catheter's functionality concerning force feedback and location feedback, and then further deformed to be assembled into a small volume of the catheter.

A flexible circuit that is substantially planar may be assembled into an electrophysiologic catheter. The flexible circuit may include various location sensing portions and force sensing portions. The flexible circuit may be deformed in a manner that improves the catheter's functionality concerning force feedback and location feedback, and then further deformed to be assembled into a small volume of the catheter.

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.

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.

A method and system for determining a patient's risk of ventricular tachycardia are disclosed. The method includes receiving ECG signals from a patient and filtering the collected ECG signals to generate filtered ECG signals. The method further includes identifying a heart vector from the filtered ECG signals, and measuring a velocity of the heart vector movement. A change in curvature of the identified heart vector movement is quantified and a risk of ventricular tachycardia is determined based at least on the measured velocity and the quantified change in curvature of the identified heart vector movement.

Techniques are described herein for converting time series data such as electrocardiogram (“ECG”) data into forms suitable for application across machine learning models, and for applying those converted data as input across machine learning models to, for instance, determine health conditions of underlying subjects. In various embodiments, a two-dimensional image may be generated (601) based on vectorcardiography (“VCG”) data, wherein the VCG data is measured directly or is based on electrocardiogram (“ECG”) data measured from a subject. The two-dimensional image may be applied (612) as input across a machine learning model to generate output, wherein the machine learning model is configured for use in processing two-dimensional images. A health condition of the subject may be determined (614) based on the output.

Techniques are described herein for converting time series data such as electrocardiogram (“ECG”) data into forms suitable for application across machine learning models, and for applying those converted data as input across machine learning models to, for instance, determine health conditions of underlying subjects. In various embodiments, a two-dimensional image may be generated (601) based on vectorcardiography (“VCG”) data, wherein the VCG data is measured directly or is based on electrocardiogram (“ECG”) data measured from a subject. The two-dimensional image may be applied (612) as input across a machine learning model to generate output, wherein the machine learning model is configured for use in processing two-dimensional images. A health condition of the subject may be determined (614) based on the output.