Systems and methods for mapping cardiac activity

Abstract

A plurality of electrophysiological (EP) data points, each comprising an electrogram signal, can be used to visualize cardiac activity. Each EP data point can be characterized as baseline or healthy, and a cloud map of the baseline EP data point can be generated. A graphical representation of the cloud map can be output in conjunction with a graphical representation of the electrophysiological map of the healthy EP data point. In an alternative embodiment, the electrogram signal can be transformed into a wavelet domain to compute a plurality of scalograms, and a wave function can be computed for each scalogram to compute a plurality of wave functions. A propagation map, such as a propagation wave map and / or a propagation wave trajectory map, can then be generated from the wave functions and graphically output.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of U.S. Provisional Application No. 63 / 012,998, filed April 21, 2020, which is incorporated by reference as if fully set forth herein. Technical Field

[0003] The present disclosure relates generally to electrophysiological visualization and mapping. More particularly, the present disclosure relates to systems, methods, and devices for generating visualizations of cardiac activity. Background Art

[0004] Electrophysiological mapping, and more specifically electrocardiogram (ECG) mapping, is an integral part of many cardiac diagnostic and therapeutic procedures. However, as the complexity of such procedures increases, the electrophysiological maps used must improve in quality, density, and the speed and ease with which they can be generated.

[0005] Electrophysiological studies can include creating a local activation time (LAT) map. The LAT map can, for example, provide practitioners with an understanding of how an arrhythmia propagates throughout the heart chambers. In fact, those of ordinary skill in the art will be familiar with the graphical representation of a LAT map in an electroanatomical mapping system.

[0006] However, in general, only a single LAT can be calculated for a given electrogram. This may be undesirable for complex electrograms with low amplitude and long duration fragmented potentials, such as may be common in low voltage myocardium. Summary of the Invention

[0007] A method for visualizing cardiac activity is disclosed herein. The method includes: receiving a plurality of electrophysiological (EP) data points at an electroanatomical mapping system, wherein each of the plurality of EP data points comprises an electrogram signal; classifying, by the electroanatomical mapping system, a first subset of the plurality of EP data points as basal EP data points and a second subset of the plurality of EP data points as healthy EP data points; generating, by the electroanatomical mapping system, a cloud map of the first subset of the plurality of EP data points; and outputting, by the electroanatomical mapping system, a graphical representation of the cloud map of the first subset of the plurality of EP data points combined with a graphical representation of the electrophysiological map of the second subset of the plurality of EP data points.

[0008] In aspects of the present disclosure, the electroanatomical mapping system classifies a given EP data point among the plurality of EP data points as a basal EP data point when the QRS duration metric of the given EP data point exceeds a preset threshold, and classifies it as a healthy EP data point otherwise.

[0009] The method may further include transforming the electrogram signal associated with the EP data point into a wavelet domain to calculate a scalogram, and calculating a peak frequency function of the scalogram. For example, a continuous wavelet transform may be applied to the electrogram signal to calculate the scalogram. The continuous wavelet transform may use a high temporal resolution mother wavelet such as a Paul wavelet.

[0010] According to aspects of the present disclosure, the step of the electroanatomical mapping system generating a cloud map of a first subset of multiple EP data points includes the electroanatomical mapping system: applying a Gaussian splatting algorithm to the first subset of multiple EP data points to create a structured point dataset; and applying an iso-contouring algorithm to the structured point dataset.

[0011] Also disclosed herein is a method for visualizing cardiac activity. The method includes receiving a plurality of electrophysiological (EP) data points at an electroanatomical mapping system, wherein each of the plurality of EP data points includes an electrogram signal. The method further includes, for each of the plurality of EP data points, the electroanatomical mapping system: transforming the electrogram signal of the EP data point into a wavelet domain, thereby computing a scalogram; and computing a wave function of the scalogram, thereby computing a plurality of wave functions. The electroanatomical mapping system generates a propagation wave map from the plurality of wave functions and outputs a graphical representation of the propagation wave map.

[0012] In an embodiment of the present disclosure, the step of transforming the electrogram signal of the EP data point into the wavelet domain includes applying a continuous wavelet transform to the electrogram signal to calculate a scalogram. The continuous wavelet transform may use a high time resolution mother wavelet such as a Paul wavelet.

[0013] The step of calculating the wave function of the scalogram may comprise calculating a peak frequency function of the scalogram. In other embodiments, the step of calculating the wave function of the scalogram may comprise calculating a composite wave function of the scalogram.

[0014] The propagated wave map may include a propagated wave trajectory map and / or an interpolated propagated wave map.

[0015] Also disclosed herein is a system for visualizing cardiac activity, comprising a visualization module configured to: receive a plurality of electrophysiological (EP) data points, wherein each of the plurality of EP data points comprises an electrogram signal; classify a first subset of the plurality of EP data points as basal EP data points, and classify a second subset of the plurality of EP data points as healthy EP data points; generate a cloud map of the first subset of the plurality of EP data points; and output a graphical representation of the cloud map of the first subset of the plurality of EP data points combined with a graphical representation of the electrophysiological map of the second subset of the plurality of EP data points.

[0016] The visualization module may be configured to generate a cloud map of a first subset of the plurality of EP data points by applying a Gaussian snowball algorithm to the first subset of the plurality of EP data points to create a structured point dataset; and applying an isocontour algorithm to the structured point dataset.

[0017] The present disclosure also provides a system for visualizing cardiac activity, comprising a visualization module configured to: receive a plurality of electrophysiological (EP) data points, wherein each of the plurality of EP data points comprises an electrogram signal; calculate a plurality of wave functions from the plurality of EP data points; generate a propagation wave map from the plurality of wave functions; and output a graphical representation of the propagation wave map.

[0018] The visualization module may be configured to calculate a plurality of wave functions from the plurality of EP data points by, for each of the plurality of EP data points: transforming the electrogram signal of the EP data point into a wavelet domain, thereby calculating a scalogram; and calculating a wave function of the scalogram.

[0019] The graphical representation of the propagating wave map may include at least one of a propagating wave trajectory map and an interpolated propagating wave map.

[0020] The foregoing and other aspects, features, details, utilities and advantages of the present invention will become apparent from a reading of the following description and claims, and from a review of the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a schematic diagram of an exemplary electroanatomical mapping system.

[0022] Figure 2 Depicted are exemplary catheters that may be used in conjunction with aspects of the present disclosure.

[0023] Figure 3A and 3B An alphanumeric labeling convention for electrodes carried by a multi-electrode catheter and their associated bipolars is provided.

[0024] Figure 4 is a flow chart of representative steps that may be performed in generating a graphical representation of cardiac activity as a cloud map according to exemplary embodiments disclosed herein.

[0025] Figure 5 The transformation of the electrogram signal into the wavelet domain and the calculation of the peak frequency function from the resulting scalogram are shown.

[0026] Figure 6 A graphical representation of cardiac activity as a static cloud map is shown.

[0027] Figure 7A graphical representation of cardiac activity as a dynamic cloud map is shown.

[0028] Figure 8 is a flow chart of representative steps that may be performed in generating a graphical representation of cardiac activity as a propagating wave according to exemplary embodiments disclosed herein.

[0029] Figure 9 The transformation of the electrogram signal into the wavelet domain and the calculation of the peak frequency function from the resulting scalogram are shown.

[0030] Figure 10 1. Represents a propagation wave trajectory map according to aspects disclosed herein.

[0031] Figure 11 Represents a propagation wave map according to aspects disclosed herein.

[0032] Figure 12 Will Figure 11 The data are depicted as a map of propagation wave trajectories.

[0033] Figure 13 Propagation maps of two cycles of tachycardia are shown.

[0034] While multiple embodiments are disclosed, still other embodiments of the present disclosure will become apparent to those skilled in the art from the following detailed description, which shows and describes illustrative embodiments.Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not restrictive. DETAILED DESCRIPTION

[0035] The present disclosure provides systems and methods for visualizing electrophysiological maps (e.g., electrocardiograms). For illustrative purposes, several exemplary embodiments will be described in detail herein with reference to cardiac electrophysiology programs. More specifically, the present disclosure will be described in conjunction with an electroanatomical mapping system (e.g., EnSite Precision CT Scanner, also from Abbott Laboratories). TM Cardiac mapping systems) use a high-density (HD) mesh catheter (e.g., the Advisor TM Aspects of the present disclosure are described in the context of visualizing cardiac activity using electrophysiological (EP) data points collected using an HD grid mapping catheter. However, one of ordinary skill in the art will understand how to best apply the teachings herein in other situations and / or with respect to other devices.

[0036] Figure 1A schematic diagram of an exemplary electroanatomical mapping system 8 is shown for conducting cardiac electrophysiological studies by navigating a cardiac catheter and measuring electrical activity occurring in a heart 10 of a patient 11 and three-dimensionally mapping the electrical activity and / or information related to or representative of the electrical activity so measured. System 8 can be used, for example, to create an anatomical model of the patient's heart 10 using one or more electrodes. System 8 can also be used to measure electrophysiological data at multiple points along the surface of the heart and store the measured data in association with location information for each measurement point at which the electrophysiological data was measured, for example, to create a diagnostic data map of the patient's heart 10.

[0037] As will be appreciated by those skilled in the art, system 8 typically determines the position of an object in three-dimensional space, and in some aspects, determines the orientation of the object, and expresses these positions as position information determined relative to at least one reference. This is referred to herein as "positioning."

[0038] To simplify the illustration, the patient 11 is schematically depicted as an ellipse. Figure 1 In the illustrated embodiment, three sets of surface electrodes (e.g., patch electrodes) applied to the surface of patient 11 are shown, defining three generally orthogonal axes, referred to herein as the x-axis, the y-axis, and the z-axis. In other embodiments, the electrodes may be positioned in other arrangements, such as multiple electrodes on a particular body surface. As another alternative, the electrodes need not be on the body surface but may be positioned within the body.

[0039] exist Figure 1 , x-axis surface electrodes 12, 14 are applied to the patient along a first axis, such as applied to the sides of the patient's chest area (e.g., applied to the patient's skin under each arm), and can be referred to as left and right electrodes. Y-axis electrodes 18, 19 are applied to the patient along a second axis generally orthogonal to the x-axis, such as applied to the patient along the patient's inner thigh and neck area, and can be referred to as left leg electrodes and neck electrodes. Z-axis electrodes 16, 22 are applied along a third axis generally orthogonal to both the x-axis and the y-axis, such as applied along the patient's sternum and spine in the chest area, and can be referred to as chest electrodes and back electrodes. Heart 10 is located between these surface electrode pairs 12 / 14, 18 / 19, and 16 / 22.

[0040] An additional surface reference electrode (e.g., a "belly patch") 21 provides a reference and / or ground electrode for the system 8. The abdominal patch electrode 21 can be an alternative to the fixed intracardiac electrode 31, described in further detail below. It should also be understood that, alternatively, the patient 11 can have most or all of the conventional electrocardiogram ("ECG" or "EKG") system lead wires in place. In some embodiments, for example, a standard set of 12 ECG lead wires can be used to sense the electrocardiogram on the patient's heart 10. This ECG information is available to the system 8 (e.g., it can be provided as an input to the computer system 20). Insofar as the ECG lead wires are well understood, and to make the figures more clear, the ECG lead wires are shown in FIG. Figure 1 Only a single lead wire 6 and its connection to the computer 20 are shown.

[0041] Also shown is a representative catheter 13 having at least one electrode 17. Throughout the specification, this representative catheter electrode 17 is referred to as a "roving electrode," "mobile electrode," or "measurement electrode." Typically, multiple electrodes 17 on the catheter 13 or on multiple such catheters will be used. For example, in one embodiment, the system 8 may include sixty-four electrodes on twelve catheters disposed within the patient's heart and / or vasculature. In other embodiments, the system 8 may use a single catheter including multiple (e.g., eight) splines, each of which includes multiple (e.g., eight) electrodes.

[0042] However, the foregoing embodiments are merely exemplary, and any number of electrodes and / or catheters may be used. For example, for purposes of this disclosure, Figure 2 , a segment of an exemplary multi-electrode catheter, particularly an HD grid catheter, is shown in FIG. The HD grid catheter 13 includes a catheter body 200 coupled to a paddle 202. The catheter body 200 may also include a first body electrode 204 and a second body electrode 206. The paddle 202 may include a first spline 208, a second spline 210, a third spline 212, and a fourth spline 214, which are coupled to the catheter body 200 via a proximal coupler 216 and coupled to each other via a distal coupler 218. In one embodiment, the first spline 208 and the fourth spline 214 may be one continuous segment, and the second spline 210 and the third spline 212 may be another continuous segment. In other embodiments, the individual splines 208, 210, 212, 214 may be separate segments that are coupled to each other (e.g., via a proximal coupler 216 and a distal coupler 218). It should be understood that the HD catheter 13 may include any number of splines; Figure 2 The arrangement of the four splines shown in is merely exemplary.

[0043] As described above, the splines 208, 210, 212, 214 may include any number of electrodes 17; Figure 2 , sixteen electrodes 17 are shown arranged in a four by four array. It should also be understood that the electrodes 17 may be spaced evenly and / or unevenly when measured along and between the splines 208, 210, 212, 214. For ease of reference in this specification, Figure 3A Alphanumeric labels for the electrodes 17 are provided.

[0044] As will be appreciated by one of ordinary skill in the art, any two adjacent electrodes 17 define a dipole. Thus, the 16 electrodes 17 on the catheter 13 define a total of 42 dipoles—12 along the splines (e.g., between electrodes 17a and 17b, or between electrodes 17c and 17d), 12 across the splines (e.g., between electrodes 17a and 17c, or between electrodes 17b and 17d), and 18 diagonally between the splines (e.g., between electrodes 17a and 17d, or between electrodes 17b and 17c).

[0045] For ease of reference in this manual, Figure 3B Alphanumeric labels for the dipoles along and across the spline are provided. Figure 3B The alphanumeric designations for the diagonal dipoles have been omitted, but this is for clarity in the illustration only. It is expressly contemplated that the teachings herein are also applicable to diagonal dipoles.

[0046] In turn, any bipolar electrode can be used to generate a bipolar electrogram according to techniques familiar to one of ordinary skill in the art. Furthermore, these bipolar electrograms can be combined (e.g., linearly combined) to generate an electrogram that also includes activation timing information in any direction in the plane of the catheter 13 by calculating the E-field loop of the electrode cluster. U.S. Application No. 15 / 953,155 discloses details of calculating the E-field loop of the electrode cluster on the HD grid catheter, and this disclosure is incorporated herein by reference as if fully set forth herein.

[0047] In any case, the catheter 13 can be used to simultaneously acquire a plurality of electrophysiological data points for each bipole defined by the electrodes 17 thereon, each such electrophysiological data point including positioning information (e.g., the position and orientation of the selected bipole) and an electrogram signal for the selected bipole. For purposes of illustration, methods according to the present disclosure will be described with reference to individual electrophysiological data points acquired by the catheter 13. However, it should be understood that the teachings herein can be applied serially and / or in parallel to multiple electrophysiological data points acquired by the catheter 13.

[0048] The catheter 13 (or a plurality of such catheters) is typically introduced into the patient's heart and / or vasculature via one or more introducers using familiar procedures. In fact, various methods of introducing the catheter 13 into the patient's heart, such as the transseptal approach, will be familiar to those of ordinary skill in the art and therefore do not require further description herein.

[0049] Because each electrode 17 is located within the patient's body, the system 8 can simultaneously collect position data for each electrode 17. Similarly, each electrode 17 can be used to collect electrophysiological data from the surface of the heart (e.g., a surface electrogram). Ordinary technicians will be familiar with various ways to obtain and process electrophysiological data points (including, for example, contact and non-contact electrophysiological mapping), so that further discussion thereof is unnecessary for understanding the technology disclosed herein. Similarly, various techniques familiar in the art can be used to generate a graphical representation of the heart geometry and / or cardiac electrical activity from multiple electrophysiological data points. In addition, to the extent that ordinary technicians will understand how to create an electrophysiological map from the electrophysiological data points, various aspects of this document are described only to the extent necessary to understand the present disclosure.

[0050] Return now Figure 1 In some embodiments, an optional fixed reference electrode 31 is shown on the second catheter 29 (e.g., attached to the wall of the heart 10). For calibration purposes, this electrode 31 can be fixed (e.g., attached to or near the heart wall) or disposed in a fixed spatial relationship to the itinerant electrodes (e.g., electrode 17), and thus can be referred to as a "navigation reference" or "local reference." The fixed reference electrode 31 can be used in addition to or as an alternative to the surface reference electrode 21 described above. In many cases, a coronary sinus electrode or other fixed electrode in the heart 10 can be used as a reference for measuring voltage and displacement; that is, the fixed reference electrode 31 can define the origin of a coordinate system, as described below.

[0051] Each surface electrode is coupled to a multiplexing switch 24, and the surface electrode pair is selected by software running on the computer 20, which couples the surface electrodes to a signal generator 25. Alternatively, the switch 24 may be omitted, and multiple (e.g., three) instances of the signal generator 25 may be provided, one for each measurement axis (that is, each surface electrode pair).

[0052] The computer 20 may include, for example, a conventional general-purpose computer, a special-purpose computer, a distributed computer, or any other type of computer. The computer 20 may include one or more processors 28, such as a single central processing unit ("CPU") or multiple processing units, commonly referred to as a parallel processing environment, that can execute instructions to practice various aspects described herein.

[0053] Typically, three nominally orthogonal electric fields are generated by a series of driven and sensed electric dipoles (e.g., surface electrode pairs 12 / 14, 18 / 19, and 16 / 22) to enable catheter navigation in a bioconductor. Alternatively, these orthogonal fields can be decomposed and any surface electrode pair can be driven as a dipole to provide effective electrode triangulation. Similarly, electrodes 12, 14, 18, 19, 16, and 22 (or any number of electrodes) can be positioned in any other effective arrangement for driving current to the electrodes in the heart or sensing the current from the electrodes in the heart. For example, multiple electrodes can be placed on the back, side, and / or abdomen of the patient 11. In addition, this non-orthogonal approach increases the flexibility of the system. For any desired axis, the potential measured across the itinerant electrodes generated by a set of predetermined drive (source-sink) configurations can be algebraically combined to generate an effective potential identical to that obtained by simply driving a uniform current along the orthogonal axis.

[0054] Thus, any two of the surface electrodes 12, 14, 16, 18, 19, 22 can be selected as dipole sources and drains relative to a ground reference (such as the abdominal patch 21), while the unenergized electrodes measure the voltage relative to the ground reference. A roving electrode 17 placed in the heart 10 is exposed to the field from the current pulses and measures relative to ground (such as the abdominal patch 21). In practice, the catheter within the heart 10 may contain more or fewer electrodes than the sixteen shown, and the potential of each electrode may be measured. As previously described, at least one electrode may be fixed to the inner surface of the heart to form a fixed reference electrode 31, which is also measured relative to ground (such as the abdominal patch 21) and can be defined as the origin of the coordinate system of the system 8 relative to its measurement location. Data sets from each of the surface electrodes, internal electrodes, and virtual electrodes can be used to determine the location of the roving electrode 17 within the heart 10.

[0055] System 8 can use the measured voltages to determine the position of an electrode within the heart (such as itinerant electrode 17) relative to a reference location (such as reference electrode 31) in three-dimensional space. That is, the voltage measured at reference electrode 31 can be used to define the origin of a coordinate system, while the voltage measured at itinerant electrode 17 can be used to express the position of itinerant electrode 17 relative to the origin. In some embodiments, the coordinate system is a three-dimensional (x, y, z) Cartesian coordinate system, but other coordinate systems are contemplated, such as polar coordinate systems, spherical coordinate systems, and cylindrical coordinate systems.

[0056] As should be clear from the foregoing discussion, when a surface electrode pair applies an electric field to the heart, data is measured for determining the position of the electrodes within the heart. The electrode data can also be used to create a respiration compensation value that is used to improve the raw position data of the electrode positions, as described, for example, in U.S. Patent No. 7,263,397, which is incorporated herein by reference in its entirety. The electrode data can also be used to compensate for changes in the patient's body impedance, as described, for example, in U.S. Patent No. 7,885,707, which is also incorporated herein by reference in its entirety.

[0057] Thus, in a representative embodiment, system 8 first selects a set of surface electrodes and then drives them with current pulses. While delivering the current pulses, electrical activity, such as voltage measured by at least one of the remaining surface electrodes and the internal body electrodes, is measured and stored. As described above, compensation for artifacts such as respiration and / or impedance shifts can be performed.

[0058] In aspects of the present disclosure, the system 8 can be a hybrid system that combines impedance-based (e.g., as described above) and magnetic-based positioning capabilities. Thus, for example, the system 8 can also include a magnetic source 30 coupled to one or more magnetic field generators. For clarity, Figure 1 Only two magnetic field generators 32 and 33 are depicted, but it should be understood that additional magnetic field generators (e.g., a total of six magnetic field generators defining three generally orthogonal axes similar to those defined by patch electrodes 12, 14, 16, 18, 19, and 22) may be used without departing from the scope of the present teachings. Likewise, one of ordinary skill in the art will understand that, in order to locate catheter 13 within the magnetic field so generated, catheter 13 may include one or more magnetic positioning sensors (e.g., coils).

[0059] In some embodiments, system 8 is Abbott's EnSite TM Velocity TM or EnSite Precision TM Cardiac Mapping and Visualization System. However, other localization systems may be used in conjunction with the present teachings, including, for example, the RHYTHMIA HDX from Boston Scientific Corporation (Marlborough, MA). TM mapping systems, CARTO navigation and positioning systems from Biosense Webster, Inc. (Irvine, California), and the CARTO navigation and positioning systems from Northern Digital Inc. (Waterloo, Ontario). System, Sterotaxis Magnetic Navigation Systems (St. Louis, Missouri) and MediGuide from Abbott TM technology.

[0060] The positioning and mapping systems described in the following patents (all of which are incorporated herein by reference in their entirety) may also be used with the present invention: U.S. Patent Nos. 6,990,370; 6,978,168; 6,947,785; 6,939,309; 6,728,562; 6,640,119; 5,983,126; and 5,697,377.

[0061] Aspects of the present disclosure relate to electrophysiological mapping, and more particularly, to generating visualizations (i.e., graphical representations) of cardiac activity. Such visualizations can be output, for example, on display 23. Thus, system 8 can include a visualization module 58 that can be used to generate various electrophysiological maps and output the electrophysiological maps (e.g., on display 23) as disclosed herein.

[0062] Reference will be made to Figure 4 400 of representative steps are shown to illustrate an exemplary method according to the present teachings. In some embodiments, for example, the flowchart 400 may represent a method that can be performed by Figure 1

[0046] Several exemplary steps are performed by the electroanatomical mapping system 8 (e.g., by the processor 28 and / or the visualization module 58). It should be understood that the representative steps described below can be implemented in hardware or software. For the sake of illustration, the term "signal processor" may be used herein to describe both hardware- and software-based implementations of the teachings herein.

[0063] In block 402, system 8 receives a plurality of electrophysiological (EP) data points, each data point including positioning information and an electrogram signal. For example, in an embodiment of the present disclosure, the positioning information corresponds to a mid-position of catheter 13 during acquisition of the corresponding electrogram signal.

[0064] In block 404, system 8 classifies a first subset of EP data points as basal EP data points and a second subset of EP data points as healthy EP data points. According to aspects of the present disclosure, system 8 utilizes a QRS duration metric of an electrogram associated with a given EP data point for classification. For example, if the QRS duration metric of the corresponding electrogram of the EP data point exceeds a preset (and optionally, user-defined) threshold (e.g., approximately 100 ms), then system 8 may classify the EP data point as basal, and otherwise classify it as healthy. Further details regarding the calculation of the QRS duration metric to distinguish between basal and healthy tissue can be found in U.S. Application No. 16 / 294,313, which is incorporated herein by reference as if fully set forth herein.

[0065] In block 406, system 8 generates a cloud map of a first subset of the plurality of EP data points (ie, base EP data points).As described in further detail below, the cloud map may be dynamic or static.

[0066] For dynamic cloud mapping, the system 8 can transform the electrogram signal associated with each base EP data point into the wavelet domain to calculate the scalogram G(f, t) of each electrogram signal. In an embodiment of the present disclosure, the system 8 applies a continuous wavelet transform to the electrogram signal using a high-time-resolution mother wavelet such as a Paul wavelet. Figure 5 Transformation of an electrogram signal 500 into a wavelet domain scalogram 502 is depicted.

[0067] Once the electrogram has been so transformed, the system 8 can calculate the peak frequency function of the scalogram. According to aspects of the present disclosure, if G(f,t)>Energy Threshold , the peak frequency function of the scalogram is a one-dimensional energy function L(t) = max(f), where f ranges from about 0 Hz to about 1000 Hz, and Energy Threshold is a preset (and optionally user-defined) noise threshold. In the embodiment of the present disclosure, the preset noise threshold is a normalized value of about 0.2. For illustrative purposes, Figure 5 A peak frequency function 504 of the scalogram 502 is shown.

[0068] Whether for static or dynamic cloud maps, the system 8 may generally perform two sub-steps to generate the cloud map in block 406. First, the system 8 applies the Gaussian snowball algorithm to a first subset of EP data points. For example, the system 8 may apply the vtk GaussianSplatter algorithm ( https: / / vtk.org / doc / nightly / html / classvtkGaussianSplatter.html ), which is incorporated herein by reference as if fully set forth herein. The VTK Gaussian Snowball algorithm is a filter that injects input basis EP data points into a structured point dataset. As each point is injected, it "sputters"—that is, it assigns values to neighboring voxels in the structured point dataset according to a Gaussian distribution function. The Gaussian distribution function can be modified with a scalar value, which expands the distribution, and / or with a normal / vector, which creates an ellipsoidal distribution rather than a spherical distribution.

[0069] Typically, the Gaussian distribution function f around a given basis EP data point p is of the form Where x is the current voxel sampling point, r is the absolute distance between x and p, the exponential factor is less than or equal to 0, and the scale factor is a scalar value that can be multiplied by p (e.g., QRS duration). This distribution is spherical.

[0070] However, if a point normal exists, the distribution becomes elliptical: where E is a preset (and optionally, user-defined) eccentricity factor that controls the elliptical shape of the sputter; z is the distance from x to p along the normal N, and rxy is the distance from x to p in the direction perpendicular to the normal N.

[0071] Next, the system 8 applies an isocontour algorithm to the structured point dataset output by the Gaussian snowball algorithm. For example, the system 8 may apply the vtkContourFilter algorithm ( https: / / vtk.org / doc / nightly / html / classvtkContourFilter.html ), which is incorporated herein by reference as if fully set forth herein. The vtk contour filter algorithm takes a structured point dataset as input and generates as output an isosurface at a preset (and optionally, user-defined) base value (e.g., a QRS duration of approximately 100 ms). The output isosurface is then rendered semi-transparent along with a scalar map value derived from the base EP data points. This generates a cloud map; the intensity of the base (e.g., QRS duration) can be represented using color, grayscale, or another suitable display convention.

[0072] In block 408 , the system 8 outputs a graphical representation of the cloud map (eg, from an isocontour algorithm) combined with a graphical representation of the electrophysiological map of the second subset of EP data points (eg, healthy EP data points).

[0073] Figure 6 A graphical representation of a static cloud map is depicted 600. Baseline EP data points may be rendered with cloud scalar values 602, while healthy EP data points may be rendered more traditionally (eg, black dots 604).

[0074] Figure 7 A graphical representation 700 of a dynamic cloud map is depicted (as a sequence of progressive static images 702a-702h). Basal EP data points at any given time step of the sequence can be rendered with cloud scalar values (e.g., peak frequency function values at the given time step), while healthy EP data points can be rendered as the familiar LAT map (e.g., as activation wavefronts).

[0075] Will refer to Figure 8 The flowchart 800 of the representative steps shown illustrates another exemplary method according to the present teachings. In some embodiments, for example, the flowchart 800 may represent a method that can be performed by Figure 1

[0066] The following describes several exemplary steps performed by the electroanatomical mapping system 8 (eg, via the processor 28 and / or the visualization module 58). Again, it should be understood that the representative steps described below may be implemented in hardware or software.

[0076] Block 802 is similar to block 402 discussed above and includes receiving, by the system 8 , a plurality of EP data points.

[0077] In block 804, system 8 transforms the electrogram signal for each EP data point into the wavelet domain, thereby computing a scalogram for each electrogram signal. The transformation of the electrogram signal into the wavelet domain is described above in conjunction with the creation of the dynamic cloud map; block 804 is similar.

[0078] In block 806, the system 8 calculates a wave function for each scalogram, thereby calculating a plurality of wave functions. According to aspects of the present disclosure, the system 8 calculates the wave function by calculating a one-dimensional peak frequency function of the scalogram as described above. In this regard, Figure 9 The electrogram signal 900 is converted into a scalogram 902 and a corresponding one-dimensional peak frequency function 904. The wave function can correspond to a one-dimensional peak frequency function. Alternatively, a composite wave function can be derived from the one-dimensional peak frequency functions of neighboring electrograms (e.g., as an average of such one-dimensional peak frequency functions, a maximum of such one-dimensional peak frequency functions, a minimum of such one-dimensional peak frequency functions, or a sum of such one-dimensional peak frequency functions).

[0079] In block 808, system 8 generates a propagating wave map from the plurality of wave functions, a graphical representation of which may be output in block 810 (eg, in conjunction with a local activation time map, a substrate map, etc.) The present disclosure contemplates propagating wave trajectory maps and propagating wave maps.

[0080] Propagation wave trajectory map

[0081] For propagation wave trajectory mapping, the leading edge of the propagation wave (e.g., the cardiac activation wavefront) is determined as the time point t* at each EP data point where the wave function first exceeds zero. For each such time point t*, a discrete spherical symbol is rendered, with the radius r of the symbol scaled by the factor c and the peak frequency function L(t), e.g. In an embodiment of the present disclosure, c=5, which renders symbols with a radius of 0 cm to 5 cm for a frequency range of about 0 Hz to about 1 kHz.

[0082] Rendered this way, the initial appearance of the symbol is indicative of the propagating wavefront.Trailing activity regions can be identified as regions where the symbol slowly decays / disappears, or where the symbol reappears behind the propagating wavefront (e.g., in a previously active region).

[0083] Figure 10 A graphical representation 1000 of a propagating wave trajectory map as a sequence of progressive static images 1002a-1002h in conjunction with a substrate map (e.g., a peak-to-peak voltage map) is shown. The leading edge 1004 of the propagating wave is annotated in images 1002a-1002d, while the trailing activity region 1006 (i.e., the region of symbols behind the propagating wave front) is annotated in images 1002e-1002h.

[0084] Propagation wave map

[0085] For propagating wave mapping, the system 8 interpolates the wave function for each time point t* over multiple EP data points.Trailing active regions can be identified as regions where the wave function produces more than one activation time.

[0086] For example, Figure 11 Depicted are a series of sequential propagating wave maps 1100a-1100d, and corresponding electrogram traces 1102a-1102d from which propagating wave maps 1100a-1100d are derived. Point 1104 corresponding to electrogram trace 1106 exhibits tailing activity, showing a secondary activation in map 1100d.

[0087] For comparison purposes, Figure 12 Depicts the Figure 11 The same data is rendered as a series of propagation wave trajectory map images 1200a-1200d.

[0088] Although several embodiments have been described above with a certain degree of particularity, those skilled in the art could make numerous alterations to the disclosed embodiments without departing from the spirit or scope of this invention.

[0089] For example, the teachings herein may be applied in real time (eg, during an electrophysiology study) or during post-processing (eg, applied to electrophysiology data points acquired during an electrophysiology study performed at an earlier time).

[0090] As another example, a QRS duration metric can be calculated as a composite QRS duration metric over a user-defined spatial neighborhood or catheter electrode neighborhood. Thus, suitable QRS duration metrics include, but are not limited to, the average QRS duration over the neighborhood, the maximum QRS duration over the neighborhood, the minimum QRS duration over the neighborhood, and the sum of the QRS durations over the neighborhood.

[0091] As yet another example, the teachings herein may be used to visualize multiple cycles of a tachycardia, such as Figure 13 In particular, Figure 13The top row of FIG moves from left to right to show the first period 1300a, 1300b, 1300c, 1300d, and Figure 13 The bottom row of FIG2 shows the second period 1300e, 1300f, 1300g, 1300h moving from right to left. Also shown are the corresponding wave function traces 1302a-1302h of three points on the heart surface; in each wave function trace, the x-axis is time and the y-axis is the wave function value at time t (e.g., L(t)).

[0092] As yet another example, a static cloud map may include additional metrics, such as gradation or signal components.

[0093] All directional references (e.g., up, down, upward, downward, left, right, leftward, rightward, top, bottom, above, below, vertical, horizontal, clockwise, and counterclockwise) are used for identification purposes only to aid the reader's understanding of the present invention and are not intended to limit the present invention in any particular sense, with respect to position, orientation, or use. References to conjunction (e.g., attachment, coupling, connection, etc.) are to be interpreted broadly and may include intermediate members between the connection of elements and relative movement between elements. Thus, references to conjunction do not necessarily infer that two elements are directly connected and in a fixed relationship to each other.

[0094] It is intended that all matter contained in the above description or shown in the accompanying drawings shall be interpreted as illustrative only and not limiting. Changes in detail or structure may be made without departing from the spirit of the invention as defined in the appended claims.

Claims

1. A method for visualizing cardiac activity, comprising: receiving a plurality of electrophysiological EP data points at an electroanatomical mapping system, wherein each EP data point of the plurality of EP data points comprises an electrogram signal; The electroanatomical mapping system classifies a first subset of the plurality of EP data points as basal EP data points and a second subset of the plurality of EP data points as healthy EP data points, wherein the electroanatomical mapping system classifies the first subset of the plurality of EP data points as basal EP data points and classifies the second subset of the plurality of EP data points as healthy EP data points, wherein the electroanatomical mapping system classifies the given EP data point as a basal EP data point when a QRS duration metric of an electrogram associated with the given EP data point in the plurality of EP data points exceeds a preset threshold, and otherwise classifies the given EP data point as a healthy EP data point; The electroanatomical mapping system generates a cloud map of the basal EP data points; and The electroanatomical mapping system outputs a graphical representation of a cloud map of the basal EP data points combined with a graphical representation of an electrophysiological map of the healthy EP data points.

2. The method of claim 1 , further comprising the electroanatomical mapping system: transforming electrogram signals associated with a first subset of the plurality of EP data points into a wavelet domain, thereby computing a scalogram; and The peak frequency function of the scalogram is calculated.

3. The method according to claim 2, wherein: Transforming the electrogram signal associated with the first subset of the plurality of EP data points to a wavelet domain includes applying a continuous wavelet transform to the electrogram signal to calculate the scalogram.

4. The method according to claim 2, wherein: Continuous wavelet transform uses a high time resolution mother wavelet.

5. The method according to claim 4, wherein The high time resolution mother wavelet includes Paul wavelet.

6. The method according to claim 1, wherein The electroanatomical mapping system generates a cloud map of the basal EP data points, comprising: Applying a Gaussian snowball algorithm to the base EP data points to create a structured point dataset; and An isocontour algorithm is applied to the structured point dataset.

7. A system for visualizing cardiac activity, comprising: The visualization module is configured to: receiving a plurality of electrophysiological EP data points, wherein each EP data point of the plurality of EP data points comprises an electrogram signal; classifying a first subset of the plurality of EP data points as basal EP data points, and classifying a second subset of the plurality of EP data points as healthy EP data points, wherein classifying the first subset of the plurality of EP data points as basal EP data points, and classifying the second subset of the plurality of EP data points as healthy EP data points comprises: classifying a given EP data point in the plurality of EP data points as a basal EP data point when a QRS duration metric of an electrogram associated with the given EP data point exceeds a preset threshold, and otherwise classifying the given EP data point as a healthy EP data point; generating a cloud map of the base EP data points; and A graphical representation of the cloud map of the basal EP data points combined with the graphical representation of the electrophysiological map of the healthy EP data points is output.

8. The system according to claim 7, wherein: The visualization module is configured to generate a cloud map of the base EP data points by: Applying a Gaussian snowball algorithm to the base EP data points to create a structured point dataset; and An isocontour algorithm is applied to the structured point dataset.

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