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.

Disclosed are various examples and embodiments of systems, devices, components and methods configured to extract atrial signals from electrical signals acquired from a patient suffering from atrial fibrillation. The electrical signals acquired from the patient may be intra-cardiac signals or body surface electrode signals, or both. At least portions of QRS or QRS-T complexes corresponding to determined initial synchronization times are used to generate Fast Fourier Transforms (FFTs) corresponding to the extracted QRS complexes. A series of steps follow to generate isolated atrial signals corresponding to each electrical signal by subtracting generated reconstructed signals corresponding to each such electrical signal therefrom.

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.

The present disclosure generally relates to systems and methods and systems of a noninvasive technique for characterizing cardiac chamber size and cardiac mechanical function. A mathematical analysis of three-dimensional (3D) high resolution data may be used to estimate chamber size and cardiac mechanical function. For example, high-resolution mammalian signals are analyzed across multiple leads, as 3D orthogonal (X,Y,Z), or 10-channel data, for 30 to 800 seconds, to derive estimates of cardiac chamber size and cardiac mechanical function. Multiple mathematical approaches may be used to analyze the dynamical and geometrical properties of the data.

A system and method for display of subcutaneous cardiac monitoring data are provided. Cutaneous action potentials of a patient and other sensed data associated with the patient are recorded as electrocardiogram (EGC) data over a set time period using a subcutaneous insertable cardiac monitor. A set of R-wave peaks is identified within the ECG data and an R-R interval plot is constructed. A difference between recording times of successive pairs of the R-wave peaks in the set is determined. A heart rate associated with each difference is also determined. The pairs of the R-wave peaks and associated heart rate are plotted as the R-R interval plot. A diagnosis of cardiac disorder is facilitated based on patterns of the plotted pairs of the R-wave peaks, the associated heart rates in the R-R interval plot, and background data based on the other sensed data.

An animated electrophysiology map is generated from a plurality of data points, each including measured electrophysiology information, location information, and timing information. The electrophysiology and location information can be used to generate the electrophysiology map, such as a local activation time, peak-to-peak voltage, or fractionation map. Animated timing markers can be superimposed upon the electrophysiology map using the electrophysiology, location, and timing information. For example a series of frames can be displayed sequentially, each including a static image of the electrophysiology map at a point in time and timing markers corresponding to the state or position of an activation wavefront at the point in time superimposed thereon. The visibility or opacity of the timing markers can be adjusted from frame to frame, dependent upon a distance between the timing marker and the activation wavefront, to give the illusion that the timing markers are moving along the electrophysiology map.

An animated electrophysiology map is generated from a plurality of data points, each including measured electrophysiology information, location information, and timing information. The electrophysiology and location information can be used to generate the electrophysiology map, such as a local activation time, peak-to-peak voltage, or fractionation map. Animated timing markers can be superimposed upon the electrophysiology map using the electrophysiology, location, and timing information. For example a series of frames can be displayed sequentially, each including a static image of the electrophysiology map at a point in time and timing markers corresponding to the state or position of an activation wavefront at the point in time superimposed thereon. The visibility or opacity of the timing markers can be adjusted from frame to frame, dependent upon a distance between the timing marker and the activation wavefront, to give the illusion that the timing markers are moving along the electrophysiology map.

Disclosed systems include an electrocardiogram sensor configured to sense electrocardiograms of a subject and a processing device operatively coupled to the electrocardiogram sensor. The processing device receives an electrocardiogram from the electrocardiogram sensor. The electrocardiogram is input into a machine learning model, the machine learning model to generate an output based on the received electrocardiogram. The processing device determines based on the electrocardiogram, that the output does not match an expected range of outputs for the target subject and generates an alert indicating a possible change in a status of the subject in response to the output not matching the expected range of outputs for the target subject.

Disclosed systems include an electrocardiogram sensor configured to sense electrocardiograms of a subject and a processing device operatively coupled to the electrocardiogram sensor. The processing device receives an electrocardiogram from the electrocardiogram sensor. The electrocardiogram is input into a machine learning model, the machine learning model to generate an output based on the received electrocardiogram. The processing device determines based on the electrocardiogram, that the output does not match an expected range of outputs for the target subject and generates an alert indicating a possible change in a status of the subject in response to the output not matching the expected range of outputs for the target subject.