Biosense Webster (israel) Ltd.

Biosense Webster, Inc.

Jonathan Yarnitsky

Meir Bar-Tal

Natan Sharon Katz

Aharon Turgeman

Roy Urman

Elad Nakar

Lior Botzer

Eliyahu Ravuna

Goren Cohn

Lior Greenbaum

Shmuel Auerbach

Assaf Cohen

Fady Massarwa

Fady Massarwi

Vladimir Rubinstein

Shiran Eliyahu

Stanislav Goldberg

Matityahu Amit

Sigal Altman

Yariv Avraham Amos

Leonid Zaides

Yoav Benaroya

Amiram Ben Dor

Dor Zeev Keren Tal

Ana Kaufman

Lior Zar

Dor Zeev Keren-Tal

Amir Ben-Dor

Smhuel Auerbach

A method includes, based on respective signals acquired by a plurality of electrodes on an anatomical surface of a heart, computing respective local activation times (LATs) at respective locations of the electrodes. The method further includes, based on the LATs, computing respective directions of electrical propagation at the locations. The method further includes selecting pairs of adjacent ones of the electrodes such that, for each of the pairs, a vector joining the pair is aligned, to within a predefined threshold degree of alignment, with the direction of electrical propagation at the location of one of the electrodes belonging to the pair. The method further includes associating respective bipolar voltages measured by the pairs of electrodes with a digital model of the anatomical surface. Other examples are also described.

Computing local propagation velocities in real-time

A method is provided. The method is implemented by a device orientation engine being executed by one or more processors. The method includes determining a movement between each electrode group of a catheter to provide movements and determining a total movement of electrodes of the catheter. The method also includes removing a standard component from the movements and the total movement and outputting a movement indication for the catheter based on the movements and the total movement with the standard component.

Signal analysis of movements of a reference electrode of a catheter in a coronary sinus vein

Systems and methods are disclosed for generating a pace-mapping prediction model. Techniques are provided that utilize a training dataset associated with biometrics of patients' hearts, including electrophysiological data associated with cardiac arrhythmia, pace-mapping datasets, and correlation data measuring the degree of correlation between the pace-mapping datasets and the electrophysiological data. Based on the training dataset, the pace-mapping prediction model is trained to predict a degree of correlation between a patient's electrophysiological data and a pace-mapping dataset. Based on the predicted degree of correlation, a cardiac location in the heart of a patient is predicted as the location for the next pace-mapping. Further systems and methods are disclosed for generating a pacing maneuver prediction model. The pacing maneuver prediction model is trained to predict interval measurement based on a pacing maneuver obtained during cardiac pace-mapping.

System and method to detect and identify cardiac pace-mapping sites and pacing maneuvers

In one embodiment, a medical system includes a catheter configured to be inserted into a heart of a living subject, and including electrodes configured to capture electrical activity of the heart at respective position in the heart, a display, and processing circuitry configured to receive position signals from the catheter, and in response to the position signals compute the respective positions of the electrodes, generate an anatomical map responsively to respective ones of the computed positions, find an anatomical feature of the heart and a position of the anatomical feature responsively to the respective positions of, and electrical activity captured by, respective ones of the electrodes, automatically segment the anatomical map responsively to the found position of the anatomical feature, and render the anatomical map to the display.

Automatic anatomical feature identification and map segmentation

A system and method for detecting a mapping annotation for an electrophysiological (EP) mapping system. The system includes a processor comprising a machine learning algorithm configured to receive a first heartbeat at an identified cardiac spatial location including a first set of attributes information corresponding to the first heartbeat; receive a second heartbeat at the identified cardiac spatial location including a second set of attributes information corresponding to the second heartbeat; compare the first set of attributes information with the second set of attributes information; and determine which of the first heartbeat and the second heartbeat has optimal characteristics based on the compared attribute information.

System and method to detect stable arrhythmia heartbeat and to calculate and detect cardiac mapping annotations

In one embodiment, a medical system includes respective electrodes for application to a body of a subject and to output a set of respective activation signals in response to electrical activity of a heart of the subject captured over a sequence of heartbeat intervals, and a processor to classify a first heartbeat interval of the set of activation signals as a first morphological template, compute a measure of similarity between a second heartbeat interval of the set of activation signals and the first morphological template, group the second heartbeat interval of the set of activation signals in a first morphological group with the first morphological template responsively to the measure exceeding a predefined threshold, and classify the second heartbeat interval of the set of activation signals as a second morphological template responsively to the measure not exceeding the predefined threshold, and repeat the above, mutatis mutandis, for subsequent heartbeat intervals.

Automatic pattern acquisition

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Haifa, Israel

Jonathan Yarnitsky