US-12616420-B2 - Computing local propagation velocities for cardiac maps

US12616420B2US 12616420 B2US12616420 B2US 12616420B2US-12616420-B2

Abstract

A method includes obtaining multiple local activation times (LATs) at different respective measurement locations on an anatomical surface of a heart. The method further includes computing respective directions of electrical propagation at one or more sampling locations on the anatomical surface, by, for each sampling location, selecting a respective subset of the measurement locations for the sampling location, constructing a set of vectors, each of at least some of the vectors including, for a different respective measurement location in the subset, three position values derived from respective position coordinates of the measurement location and an LAT value derived from the LAT at the measurement location, and computing the direction of electrical propagation at the sampling location based on a Principal Component Analysis (PCA) of a 4×4 covariance matrix for the set of vectors. The method further includes indicating the directions of electrical propagation on a display.

Inventors

Leonid Zaides
Elad Nakar
Eliyahu Ravuna
Yoav Benaroya
Fady Massarwi
Jonathan Yarnitsky
Lior Greenbaum

Assignees

BIOSENSE WEBSTER (ISRAEL) LTD.

Dates

Publication Date: 20260505
Application Date: 20220216

Claims (20)

1 . A system, comprising: a display; and a processor, configured to: obtain multiple local activation times (LATs) at different respective measurement locations on an anatomical surface of a heart, compute respective directions of electrical propagation at one or more sampling locations on the anatomical surface, by, for each sampling location of the sampling locations: selecting a respective subset of the measurement locations for the sampling location, constructing a set of vectors, each of at least some of the vectors including, for a different respective measurement location in the subset, three position values derived from respective position coordinates of the measurement location and an LAT value derived from the LAT at the measurement location, and computing the direction of electrical propagation at the sampling location based on a Principal Component Analysis (PCA) of a 4×4 covariance matrix for the set of vectors, and indicate the directions of electrical propagation on the display.
2 . The system according to claim 1 , wherein the processor is configured to construct the set of vectors by: computing a scaling factor based on a variance of the LATs across the subset of the measurement locations, and for each measurement location in the subset: scaling, by the scaling factor, a parameter selected from the group of parameters consisting of: the position coordinates of the measurement location, and the LAT at the measurement location, and constructing the vector corresponding to the measurement location from the scaled parameter.
3 . The system according to claim 1 , wherein the processor is configured to compute the direction of electrical propagation at the sampling location by projecting a first principal component of the covariance matrix onto respective dimensions of the position coordinates.
4 . The system according to claim 1 , wherein the processor is further configured to compute a speed of electrical propagation at each of the sampling locations, by: for a hypothetical line passing through the sampling location and oriented in the direction of electrical propagation at the sampling location, computing respective distances along the line at which lie respective projections, onto the line, of the subset of the measurement locations, and computing the speed as a slope of a regression function fitted to a group of regression points, each of which includes, for a different respective measurement location belonging to the subset, (i) the distance along the line at which the projection of the measurement location lies, and (ii) the LAT at the measurement location.
5 . The system according to claim 1 , wherein the processor is further configured to smooth the directions of electrical propagation prior to indicating the directions of electrical propagation.
6 . The system according to claim 1 , wherein the processor is configured to select the respective subset of the measurement locations for the sampling location by: identifying those of the measurement locations that are within a predefined distance of the sampling location, and selecting the subset of the measurement locations from the identified measurement locations.
7 . The system according to claim 1 , wherein the measurement locations correspond to different respective measurement points on a digital model surface representing the anatomical surface, the measurement points being associated with the LATs, respectively, wherein the processor is further configured to designate, on the digital model surface, a plurality of sampling points, and wherein the sampling locations correspond to the sampling points, respectively.
8 . The system according to claim 7 , wherein the processor is configured to indicate the directions of electrical propagation by displaying the model surface with respective markers overlaying the model surface at the sampling points and oriented in the directions of electrical propagation, respectively.
9 . The system according to claim 8 , wherein the processor is further configured to: compute respective speeds of electrical propagation at the sampling locations, and vary at least one property of the markers in accordance with the speeds.
10 . The system according to claim 8 , wherein the processor is further configured to, prior to displaying the model surface with the markers overlaying the model surface: iteratively: recompute the directions of electrical propagation, and shift the sampling points toward each other in response to the directions of electrical propagation.
11 . A method, comprising: obtaining multiple local activation times (LATs) at different respective measurement locations on an anatomical surface of a heart; computing respective directions of electrical propagation at one or more sampling locations on the anatomical surface, by, for each sampling location of the sampling locations: selecting a respective subset of the measurement locations for the sampling location, constructing a set of vectors, each of at least some of the vectors including, for a different respective measurement location in the subset, three position values derived from respective position coordinates of the measurement location and an LAT value derived from the LAT at the measurement location, and computing the direction of electrical propagation at the sampling location based on a Principal Component Analysis (PCA) of a 4×4 covariance matrix for the set of vectors; and indicating the directions of electrical propagation on a display.
12 . The method according to claim 11 , wherein constructing the set of vectors comprises: computing a scaling factor based on a variance of the LATs across the subset of the measurement locations; and for each measurement location in the subset: scaling, by the scaling factor, a parameter selected from the group of parameters consisting of: the position coordinates of the measurement location, and the LAT at the measurement location, and constructing the vector corresponding to the measurement location from the scaled parameter.
13 . The method according to claim 11 , wherein computing the direction of electrical propagation at the sampling location comprises computing the direction of electrical propagation by projecting a first principal component of the covariance matrix onto respective dimensions of the position coordinates.
14 . The method according to claim 11 , further comprising computing a speed of electrical propagation at each of the sampling locations, by: for a hypothetical line passing through the sampling location and oriented in the direction of electrical propagation at the sampling location, computing respective distances along the line at which lie respective projections, onto the line, of the subset of the measurement locations, and computing the speed as a slope of a regression function fitted to a group of regression points, each of which includes, for a different respective measurement location belonging to the subset, (i) the distance along the line at which the projection of the measurement location lies, and (ii) the LAT at the measurement location.
15 . The method according to claim 11 , further comprising, prior to indicating the directions of electrical propagation, smoothing the directions of electrical propagation.
16 . The method according to claim 11 , wherein selecting the respective subset of the measurement locations for the sampling location comprises: identifying those of the measurement locations that are within a predefined distance of the sampling location; and selecting the subset of the measurement locations from the identified measurement locations.
17 . The method according to claim 11 , wherein the measurement locations correspond to different respective measurement points on a digital model surface representing the anatomical surface, the measurement points being associated with the LATs, respectively, wherein the method further comprises designating, on the digital model surface, a plurality of sampling points, and wherein the sampling locations correspond to the sampling points, respectively.
18 . The method according to claim 17 , wherein indicating the directions of electrical propagation comprises indicating the directions of electrical propagation by displaying the model surface with respective markers overlaying the model surface at the sampling points and oriented in the directions of electrical propagation, respectively.
19 . The method according to claim 18 , further comprising: computing respective speeds of electrical propagation at the sampling locations; and varying at least one property of the markers in accordance with the speeds.
20 . The method according to claim 18 , further comprising, prior to displaying the model surface with the markers overlaying the model surface: iteratively: recomputing the directions of electrical propagation, and shifting the sampling points toward each other in response to the directions of electrical propagation.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS The present application claims the benefit of U.S. Provisional Application 63/192,221, entitled “Computing local propagation velocities for cardiac maps,” filed May 24, 2021, whose disclosure is incorporated herein by reference, and U.S. Provisional Application 63/192,231, entitled “Computing local propagation velocities in real-time,” filed May 24, 2021, whose disclosure is incorporated herein by reference. FIELD OF THE DISCLOSURE The present disclosure is related to the field of cardiac mapping. BACKGROUND The local activation time (LAT) at any portion of cardiac tissue is the difference between (i) the time at which the tissue becomes electrically activated during any cardiac cycle, and (ii) a reference time during the same cycle. The reference time may be set, for example, to a point in the QRS complex of a body-surface electrocardiogram (ECG) recording. US Patent Application Publication 2015/0196770 describes a system including an active medical device with means for delivering defibrillation shocks, means for continuous collection of the patient current cardiac activity parameters. and evaluator means with neuronal analysis comprising a neural network with at least two layers. The neural network comprises upstream three neural sub-networks receiving the respective parameters divided into separate sub-groups corresponding to classes of arrhythmogenic factors, and downstream an output neuron coupled to the three sub-networks and capable of outputting an index of risk of ventricular arrhythmia. The risk index is compared with a given threshold, to enable or disable at least one function of the device in case of crossing of the threshold. US Patent Application Publication 2010/0268059 describes a method including accessing cardiac information acquired via a catheter located at various positions in a venous network of a heart of a patient where the cardiac information comprises position information, electrical information and mechanical information, mapping local electrical activation times to anatomic positions to generate an electrical activation time map, mapping local mechanical activation times to anatomic positions to generate a mechanical activation time map, generating an electromechanical delay map by subtracting local electrical activation times from corresponding local mechanical activation times, and rendering at least the electromechanical delay map to a display. Cantwell, Chris D., et al., “Techniques for automated local activation time annotation and conduction velocity estimation in cardiac mapping,” Computers in biology and medicine 65 (2015): 229-242 surveys algorithms designed for identifying local activation times and computing conduction direction and speed. Roney, Caroline H., et al., “An automated algorithm for determining conduction velocity, wavefront direction and origin of focal cardiac arrhythmias using a multipolar catheter,” 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, 2014 describes automated algorithms for identifying conduction velocity from multipolar catheter data with any arrangement of electrodes, whilst providing estimates of wavefront direction and focal source position. BRIEF DESCRIPTION OF THE DRAWINGS The present disclosure will be more fully understood from the following detailed description of examples thereof, taken together with the drawings, in which: FIG. 1 is a schematic illustration of a system for electroanatomical mapping, in accordance with some examples of the present disclosure; FIG. 2 is a flow diagram for an algorithm for computing and displaying propagation velocities, in accordance with some examples of the present disclosure; FIG. 3 is a schematic illustration of a propagation-velocity computation, in accordance with some examples of the present disclosure; FIG. 4 is a flow diagram for a selecting step shown in FIG. 2, in accordance with some examples of the present disclosure; FIG. 5 is a schematic illustration of a surface of a digital model, in accordance with some examples of the present disclosure; FIG. 6 is a flow diagram for an instance of the selecting step shown in FIG. 2, in accordance with some examples of the present disclosure; FIG. 7 is a flow diagram for an interpolating step shown in FIG. 6, in accordance with some examples of the present disclosure; FIG. 8 is a flow diagram for a clustering step shown in FIG. 7, in accordance with some examples of the present disclosure; FIGS. 9A-B are schematic illustrations of a displayed model, in accordance with some examples of the present disclosure; FIG. 10 is a flow diagram for an iterative condensing algorithm, in accordance with some examples of the present disclosure; FIG. 11 is a schematic illustration of a probe and an anatomical surface, in accordance with some examples of the present disclosure; FIG. 12 is a schematic illustration of a real-time visual indication of propagation veloci