In one embodiment, a medical system, includes a catheter to be inserted into a chamber of a heart of a living subject, and including catheter electrodes configured to contact tissue at respective locations within the chamber of the heart, a display, and processing circuitry to receive signals from the catheter, and in response to the signals assess a respective quality of contact of each of the catheter electrodes with the tissue in the heart, and render to the display respective intracardiac electrograms traces representing electrical activity in the tissue that is sensed by the catheter electrodes at the respective locations, while modifying a visual feature of at least some of the traces responsively to the respective quality of contact of the catheter electrodes with the tissue of the heart at the respective locations.

A method includes assigning, to first voxels in a model of tissue of a chamber of a heart, respective first values of a parameter at respective locations on the tissue, the first voxels representing the locations, respectively. Some of the locations are on an endocardial surface of the tissue, and others of the locations are on an epicardial surface of the tissue. The method further includes assigning respective second values to second voxels in the model, a subset of which represent a portion of the tissue between the endocardial surface and the epicardial surface, by interpolating the first values. Other embodiments are also described.

An electrophysiology mapping system utilizes multiple surface electrodes on a body of a patient for visualization of internal bodily structures, and especially cardiac structures. Some such systems can further utilize a magnetic field source adjacent to the patient for internal bodily structure visualization. In place of an intra-cardiac electrode or other intra-cardiac sensor, an echo probe is utilized external to the body. The electrode or other sensor on the echo probe is spaced a known distance from a sound wave detector, such as a piezoelectric crystal which also generates sound waves, the sensor assisting in correlating echo probe sensed patient structural data with patient structural data otherwise gathered by the EP mapping system. This data is integrated together for visualization on the EP mapping display without requiring an intra-cardiac electrode or other intra-cardiac sensor.

An ECG data management system is disclosed which includes a first memory portion configured to store ECG data having values corresponding to electrical signals of a heart acquired over time via a plurality of electrodes disposed at different areas of the heart. The system also includes a second memory portion configured to store the ECG data and a processing device configured to manage mapping of the ECG data by performing a mapping procedure including generating map data and one or more maps from the ECG data for display; concurrently storing the ECG data in the first memory portion and the second memory portion; and in response to a request to export the ECG data, stopping the storing of the ECG data in the second memory portion and synchronizing the ECG data stored in the second memory portion with the map data while continuing to perform the mapping procedure.

A method includes receiving an input mesh representation of a cardiac chamber, a set of measured locations on a wall tissue of the cardiac chamber, and a respective set of local activation times (LATs) measured at the locations. The input mesh is re-meshed into a regular mesh including regularized polygons. The set of measured locations and respective LATs is data fitted to the regularized polygons. Respective LAT values, and respective probabilities that the wall tissue includes scar tissue, are iteratively calculated for the regularized polygons, so as to obtain an electrophysiological (EP) activation wave over the regular mesh that indicates scar tissue. An electroanatomical map overlaid on the regular mesh, the map including the EP activation wave and the scar tissue, is presented.

A system for facilitating display of cardiac information includes a display device configured to present a cardiac map; and a processing unit configured to: receive electrical signals and indications of measurement locations corresponding to the electrical signals; generate, based on the electrical signals, the cardiac map, which includes annotations representing cardiac signal features; and determine a set of interesting cardiac signal features. The processing unit also may determine, based on the set of interesting cardiac signal features, a region of interest; and facilitate display, via the display device, of the cardiac map and a representation of the region of interest. The representation of the region of interest includes a first display parameter value that is different from a second display parameter value, where the second display parameter value is associated with at least one cardiac signal feature that is not included within the region of interest.

Techniques that include applying machine learning models to episode data, including a cardiac electrogram, stored by a medical device are disclosed. In some examples, based on the application of one or more machine learning models to the episode data, processing circuitry derives, for each of a plurality of arrhythmia type classifications, class activation data indicating varying likelihoods of the classification over a period of time associated with the episode. The processing circuitry may display a graph of the varying likelihoods of the arrhythmia type classifications over the period of time. In some examples, processing circuitry may use arrhythmia type likelihoods and depolarization likelihoods to identify depolarizations, e.g., QRS complexes, during the episode.

Disclosed are devices, systems, and methods for determining the dipole densities on a cardiac surface using electrodes positioned on a torso of a patient. Electrodes are integrated into a piece of clothing worn by a patient. The clothing serves to fix the position of the electrodes adjacent a patient's torso. Ultrasonic transducers and sensors are used to determine a distance between the epicardial surface and the electrodes and are also used to detect epicardial surface motion as well as epicardial wall thickness.

Disclosed are various examples and embodiments of systems, devices, components and methods configured to detect a location of a source of at least one cardiac rhythm disorder in a patient's heart. In some embodiments, electrogram signals are acquired from a patient's body surface, and subsequently normalized, adjusted and/or filtered, followed by generating a two-dimensional spatial map, grid or representation of the electrode positions, processing the amplitude-adjusted and filtered electrogram signals to generate a plurality of three-dimensional electrogram surfaces corresponding at least partially to the 2D map, one surface being generated for each or selected discrete times, and processing the plurality of three-dimensional electrogram surfaces through time to generate a velocity vector or other type of map using one or more of optical flow, video tracking analysis, motion capture analysis, motion estimation analysis, data association and segmentation tracking analysis, particle tracking analysis, and single-particle tracking analysis methods corresponding at least partially to the 2D map. Trained atrial discriminative machine learning models that facilitate the foregoing systems and methods, and that provide predictions or results concerning a patient's condition, are also disclosed.

Disclosed are various examples and embodiments of systems, devices, components and methods configured to detect a location of a source of at least one cardiac rhythm disorder in a patient's heart, such as atrial fibrillation, and to classify same. Velocity vector maps reveal the location of the source of the at least one cardiac rhythm disorder in the patient's heart, which may be, by way of example, an active rotor in the patient's myocardium and atrium. The resulting velocity vector map may be further processed and/or analyzed to classify the nature of the patient's cardiac rhythm disorder, e.g., as Type A, B or C atrial fibrillation. The resulting cardiac rhythm classification then can be used to determine the optimal, most efficacious and/or most economic treatment or surgical procedure that should be provided to the individual patient. A simple and computationally efficient intra-cardiac catheter-based navigation system is also described.