Real-time calculation of local propagation velocity

Abstract

To perform cardiac mapping.SOLUTION: 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, 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.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] (Cross-reference of related applications) This application claims the benefits of U.S. Provisional Application No. 63 / 192,221, filed May 24, 2021, entitled "Computing local propagation velocities for cardiac maps," whose disclosure is incorporated herein by reference, and U.S. Provisional Application No. 63 / 192,231, filed May 24, 2021, entitled "Computing local propagation velocities in real-time," whose disclosure is incorporated herein by reference.

[0002] (Field of Invention) This disclosure relates to the field of cardiac mapping. [Background technology]

[0003] The local excitation time (LAT) in any part of cardiac tissue is the difference between (i) the time the tissue is electrically excited during any cardiac cycle and (ii) a reference time during the same cycle. The reference time can be set, for example, at a point in the QRS complex of a surface electrocardiogram (ECG) recording.

[0004] U.S. Patent Application Publication No. 2015 / 0196770 describes an active medical device comprising means for delivering a defibrillating shock, means for sequentially collecting current cardiac activity parameters of a patient, and evaluation means using neuronal analysis including a neural network having at least two layers. This neural network includes three upstream subnetworks that receive parameters divided into separate subgroups corresponding to classes of arrhythmia-causing factors, and downstream output neurons coupled to these three subnetworks and capable of outputting an index of ventricular arrhythmia risk. The risk index is compared to a given threshold, and if it crosses the threshold, at least one function of the device is enabled or disabled.

[0005] U.S. Patent Application Publication No. 2010 / 0268059 describes a method for accessing cardiac information obtained via catheters placed at various locations within the venous network of a patient's heart, the cardiac information including location information, electrical information, and mechanical information, the method comprising: mapping local electrical activation times to anatomical locations to generate an electrical activation time map; mapping local mechanical activation times to anatomical locations 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.

[0006] Cantwell, Chris D. et al., in "Techniques for automated local activation time annotation and conduction velocity estimation in cardiac mapping" (Computer in biology and medicine 65(2015):229~242), outlines an algorithm designed to identify local activation time and calculate conduction direction and velocity.

[0007] Roney, Caroline H. et al.'s paper, "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 an automated algorithm that determines conduction velocity from multipolar catheter data using an arbitrary electrode arrangement, while simultaneously estimating the wavefront direction and the location of the epicenter.

Brief Description of the Drawings

[0008] A more complete understanding of the present disclosure will be obtained by reading the following detailed description of the embodiments of the present disclosure in conjunction with the drawings. [Figure 1] Schematic diagram of a system for electroanatomical mapping according to some examples of the present disclosure. [Figure 2] Flow diagram of an algorithm for calculating and displaying propagation speed according to some examples of the present disclosure. [Figure 3] Schematic diagram of propagation speed calculation according to some examples of the present disclosure. [Figure 4] Flow diagram of the selection step shown in FIG. 2 according to some examples of the present disclosure. [Figure 5] Schematic diagram of the surface of a digital model according to some examples of the present disclosure. [Figure 6] Flow diagram of an example of the selection step shown in FIG. 2 according to some examples of the present disclosure. [Figure 7] Flow diagram of the interpolation step shown in FIG. 6 according to some examples of the present disclosure. [Figure 8] Flow diagram of the clustering step shown in FIG. 7 according to some examples of the present disclosure. [Figure 9A] Schematic diagram of the displayed model according to some examples of the present disclosure. [Figure 9B] Schematic diagram of the displayed model according to some examples of the present disclosure. [Figure 10] Flow diagram of an iterative condensation algorithm according to some examples of the present disclosure. [Figure 11] Schematic diagram of a probe and an anatomical surface according to some examples of the present disclosure. [Figure 12] Schematic diagram of a real-time visual display of propagation speed according to some examples of the present disclosure. [Figure 13] Schematic diagram of a method for selecting bipolar voltage according to some examples of the present disclosure. [Figure 14] This diagram illustrates some examples of methods for selecting a bipolar voltage. [Figure 15] This is a flowchart of an algorithm for selecting electrode pairs for bipolar voltage measurement, with some examples from the present disclosure. [Figure 16] This is a flowchart of an algorithm for calculating the respective LAT at each electrode position, using some examples from this disclosure. [Modes for carrying out the invention]

[0009] Overview During electroanatomical mapping, an internal probe equipped with multiple electrodes at its distal end is moved along the surface of the heart. Based on the bioelectrical signals acquired from the surface by the electrodes, various electrical properties of the surface, such as LAT, are estimated at various locations on the surface. (The process of estimating LAT based on one or more acquired signals is hereafter referred to as "measurement" of LAT.)

[0010] Examples of this disclosure provide algorithms for accurately calculating propagation velocity with low spatial resolution based on LAT. Examples of this disclosure further provide techniques for visually indicating propagation velocity to physicians to facilitate appropriate diagnosis and treatment.

[0011] In particular, for each "sampling location" on the surface where the velocity is calculated, the computer processor selects a suitable set of "measurement locations" near where each LAT was measured. Advantageously, this set excludes any measurement locations separated from the sampling location by electrically inert tissue. Then, for each measurement location in the set (and the sampling location itself, if the LAT was measured at the sampling location), a four-dimensional vector is constructed. Each vector contains three position values ​​derived from the position coordinates of the measurement location, along with the LAT value derived from the LAT measured at the measurement location.

[0012] The processor then performs a Principal Component Analysis (PCA) on the vectors using a 4x4 covariance matrix and calculates the propagation velocity at the sampling locations based on the PCA. For example, the processor may calculate the direction of propagation by projecting the principal components of the covariance matrix onto the three position dimensions. The processor may then project the measurement locations in the set onto a line that passes through the sampling locations and is oriented in the direction of propagation. Next, the processor may calculate a regression function that approximates the relationship between the projection and the corresponding LAT. Finally, the processor may estimate the magnitude of the velocity (i.e., the propagation velocity) as the gradient of this function.

[0013] In some examples, propagation velocity is calculated following a complete mapping of the cardiac surface, where a probe is moved across the cardiac surface to measure multiple LATs at each measurement location. Specifically, following the complete mapping, the processor constructs a model of the surface, where each measurement point on the model surface corresponds to a measurement location. Next, the processor designates multiple sampling points on the model surface, corresponding to each sampling location on the anatomical surface. Then, the processor calculates the propagation velocity at each sampling location based on a preferred set of measurement locations.

[0014] In such examples, following the calculation of the propagation velocity, the processor typically displays the model with markers superimposed, each representing one or more characteristics of the propagation velocity. For example, at each sampling point, the processor may place a marker oriented in the direction of the propagation velocity at the sampling point. Sampling points where the magnitude of the propagation velocity is below a predetermined threshold may be marked differently from other sampling points so that a physician can easily identify areas of slow conduction.

[0015] (Note that in the description of such examples in this specification, references to points on the model surface may be replaced with references to corresponding locations on the anatomical surface, and vice versa. For example, a LAT measured at a particular measurement location can be associated with a measurement point on the model surface corresponding to that measurement location. Similarly, the (x,y,z) position coordinates of a location on the anatomical surface can be called the position coordinates of a corresponding point on the model surface.)

[0016] Alternatively or additionally, propagation velocity can be calculated in real time during the mapping procedure. In particular, after each round of LAT measurements (typically performed once per cardiac cycle), the processor may iterate over the entire range of measurement locations (i.e., electrode locations). For each measurement location, the processor can identify a preferred set of adjacent measurement locations. The processor can then construct a vector for each measurement location, perform PCA on the corresponding covariance matrix, and calculate the propagation velocity based on that PCA.

[0017] In such examples, the processor typically repeatedly refreshes the display of an icon at the distal end of the probe, which has electrodes, with markers superimposed that indicate one or more characteristics of the propagation velocity. For example, for each electrode, the processor may place a marker oriented in the direction of the propagation velocity at the electrode. As with non-real-time displays, the characteristics of the markers may change as a function of the propagation velocity.

[0018] Another challenge when performing electroanatomical mapping is that if the pair of electrodes used to measure bipolar voltages are oriented perpendicular to each other with respect to the local propagation direction, the measured bipolar voltages may misrepresent electrically inert tissue on the cardiac surface.

[0019] To address this challenge, an example of the present disclosure uses the aforementioned real-time calculations to select the pair of electrodes that are most closely aligned with the local propagation direction. The bipolar voltage between the selected pair of electrodes is associated with a model of the cardiac surface, while other bipolar voltages are omitted from the model.

[0020] Examples of this disclosure further provide enhanced LAT calculations based on multiple bipolar voltages.

[0021] System Description First, refer to Figure 1, which is a schematic diagram of a system 20 for electroanatomical mapping, with some examples of the present disclosure.

[0022] Figure 1 shows a physician 30 moving the distal end of a probe 26 along the anatomical surface of a portion of a subject's 22 heart 24, such as the endocardial surface of the ventricle. While the distal end of the probe 26 is moving along the surface, a processor 32 belonging to system 20 uses an electrode 28 at the distal end of the probe to measure the local excitation time (LAT) at various measurement locations on the surface. In particular, once the electrode 28 acquires a potentiometric signal at the measurement location, the processor processes these signals to calculate the LAT. Typically, the potentiometric signal includes both unipolar signals, i.e., the signal between the electrode and a common reference electrode, and bipolar voltages, i.e., the voltage between adjacent pairs of electrodes.

[0023] (Typically, when mapping is performed during the occurrence of periodic arrhythmia, calculating LAT involves two steps. First, a standard LAT calculation is performed as described above in the background technique. Then, the absolute value of LAT is p * If it is greater than CL (CL is the length of each period of a periodic arrhythmia, and p ≥ 0.5 is specified by the physician), the absolute value of LAT is p * Add CL to LAT, or subtract CL from LAT, so that the result is less than CL.

[0024] In some examples, the distal end of the probe 26 includes a plurality of parallel splines 29, each spline 29 comprising a linear arrangement of electrodes. Alternatively, the electrode grid may be arranged on a balloon, an expandable printed circuit board (PCB), or any other suitable structure at the distal end of the probe.

[0025] Typically, the processor is housed in a console 40 that has an electrical interface 34, such as a port or socket. The probe is connected to the console 40 via the electrical interface 34, thereby the potential diagram signal acquired by the electrodes is received by the processor via the electrical interface 34. Typically, the signal is carried along a wire through the probe in analog form, and the console further includes an analog-to-digital (A / D) converter configured to convert the signal into a digital format for processing by the processor 32.

[0026] During the mapping procedure, the processor tracks the position of the distal end of the probe. Based on the tracking and the electrographic signals received from the electrodes, the processor can construct a digital model 38 of this part of the heart, also referred to herein as a “map”. The processor can further store the model in a memory 33 having any preferred type of volatile or nonvolatile memory, and / or display the model 38 on a display 36.

[0027] In some examples, to facilitate the aforementioned tracking, the distal end of the probe is equipped with one or more electromagnetic sensors. In the presence of the generated magnetic field, these sensors output signals to the processor (e.g., via electrical interface 34) indicating the position of each sensor. Such position tracking techniques are disclosed, for example, in U.S. Patents No. 5,391,199, 5,443,489, and 6,788,967 by Ben-Haim, U.S. Patent No. 6,690,963 by Ben-Haim et al., U.S. Patent No. 5,558,091 by Acker et al., and U.S. Patent No. 6,177,792 by Govar, whose respective disclosures are incorporated herein by reference.

[0028] In other examples, impedance is measured between electrode 28 (and / or other electrodes at the distal end of the probe) and electrode patches coupled to the body of subject 22. Based on the impedance measurement, the processor determines the electrode positions. Typically, in such examples, the processor utilizes a position map calibrated beforehand using an electromagnetic sensor, as described, for example, in U.S. Patent No. 7,536,218 by Govari et al. and U.S. Patent No. 8,456,182 by Bar-Tal et al., whose respective disclosures are incorporated herein by reference.

[0029] In yet another example, current flows between electrode patches. Based on the voltage measured at electrode 28, the processor determines the position of the electrode. Such techniques are described, for example, in U.S. Patent No. 5,983,126 to Wittkampf, U.S. Patent No. 6,456,864 to Swanson, and U.S. Patent No. 5,944,022 to Nardella, whose respective disclosures are incorporated herein by reference.

[0030] System 20 may further include one or more input devices such as a keyboard, mouse, or touchscreen belonging to the display 36. The physician 30 may use the input device to input any preferred input, such as one of the various thresholds described below.

[0031] In general, the processor 32 may be embodied as a single processor or as a set of collaboratively networked or clustered processors. Some functions of the processor 32 may be implemented in hardware only, for example, using one or more fixed-function or general-purpose integrated circuits, application-specific integrated circuits (ASICs), and / or field-programmable gate arrays (FPGAs). Alternatively, these functions may be implemented in software, at least partially. For example, the processor 32 may be embodied as a programmed processor comprising, for example, a central processing unit (CPU) and / or a graphics processing unit (GPU). Program code, including software programs, and / or data may be uploaded to RAM for execution and processing by the CPU and / or GPU. The program code and / or data can be downloaded to the processor in electronic form, for example, over a network. Alternatively or additionally, the program code and / or data may be provided and / or stored on a non-temporary tangible medium such as a magnetic memory device, optical memory device, or electronic memory device. When such program code and / or data is provided to the processor, it creates a machine or dedicated computer configured to perform the tasks described herein.

[0032] Calculation of propagation speed Next, we refer to Figure 2, a flowchart of algorithm 53 for calculating and displaying propagation speed according to some examples of the present invention. The processor 32 (Figure 1) can execute algorithm 53 in real time while the potential diagram signal is being acquired or immediately following its acquisition.

[0033] Algorithm 53 begins with an LAT acquisition step 55 in which the processor acquires multiple LATs at different measurement locations on the anatomical surface of the heart 24 (Figure 1). For example, the processor can acquire the LATs by calculating them as described above with reference to Figure 1, or by reading them from memory 33 (Figure 1) or from an external storage device such as a flash drive when executing the algorithm after mapping.

[0034] Following the acquisition of the LAT, the processor evaluates in evaluation step 57 whether it should attempt to calculate the propagation velocity for at least one sampling location. If yes, the processor selects the sampling locations for the velocity calculation in selection step 59, along with a subset of measurement locations for which the LAT can be used in the calculation. (Selection step 59 is further described below with reference to Figure 4.) Next, in subset size evaluation step 63, the processor evaluates whether the subset of measurement locations is large enough to perform the calculation, i.e., whether the subset contains a threshold number of measurement locations.

[0035] If the subset of measurement locations is sufficiently large, the processor constructs a set of vectors corresponding to the subset of measurement locations. Each vector contains, for each distinct measurement location within the subset, three position values ​​derived from the position coordinates of each measurement location, and a LAT value derived from the LAT measured at the measurement location. (Therefore, each vector is four-dimensional.) Alternatively, if the subset is not sufficiently large, the processor returns to evaluation step 57.

[0036] Typically, to construct a set of vectors, the processor first calculates a scaling factor in step 65, based on the distribution of LAT across the entire subset of measurement locations. Then, for each measurement location within the subset, the processor scales either the position coordinates of the measurement location or the LAT at the measurement location by the scaling factor, and constructs a vector corresponding to the measurement location from the scaled parameter. (The same parameter is scaled for each measurement location.)

[0037] For example, the processor can first scale the LAT by a scaling factor in the LAT scaling step 67. Subsequently, the processor can construct a vector from the position coordinates and the scaled LAT in the vector construction step 69. For example, for any specific measurement position, the processor can construct a vector [x0 y0 z0 s * L0 can be constructed, where (x0, y0, z0) are the position coordinates of the measurement location, L0 is the LAT at the measurement location, and s is the scaling factor.

[0038] In examples where LAT is scaled, the scaling factor is typically the target LAT dispersion across a subset of measurement locations.

[0039]

number

[0040]

number

[0041]

number

[0042] In some examples, the target LAT variance is calculated according to a predetermined (increasing) function of a given distance D0, as shown below with reference to Figure 3. For example, the target LAT variance is c * D0 2 It may also be equal to , where c is a constant between 5 and 10, for example. In other examples, the spatial variance of a subset of measurement locations is calculated for three axes: x, y, and z, and the target LAT variance is calculated as a multiple of the largest spatial variance.

[0043] Following the construction of a set of vectors, the processor calculates the direction of electrical propagation at selected sampling locations based on principal component analysis (PCA) of the 4x4 covariance matrix of the set of vectors. For example, the processor can project the first principal component of the covariance matrix (a 4-dimensional vector) onto each dimension of the position coordinates (thus yielding a 3-dimensional vector).

[0044] As an example, Table 1 below shows a set of eight vectors constructed from experimental data. (In this particular example, LAT was scaled up by a factor of 6.)

[0045] [Table 1]

[0046] Table 2 below shows the covariance matrix for this set of vectors.

[0047] [Table 2]

[0048] The first principal component of this matrix is ​​[-0.138 -1.165 * 10 -16 [-0.016 -0.990], and the projection of the first principal component onto the XYZ space is [-0.138 -1.165] * 10-16 -0.016], which, when expressed as a unit direction vector, is [0.993 8.406 * 10 -16 0.119].

[0049] Following the calculation of the propagation direction, the processor calculates the propagation speed at the selected sampling position in speed calculation step 73. (Speed calculation step 73 will be described below with reference to FIG. 3.) The processor then returns to evaluation step 57.

[0050] In evaluation step 57, in addition to confirming that there is no further speed to be calculated, the processor indicates the speed on display 36 (FIG. 1), as will be further described below with reference to FIGS. 9A - 9B and FIG. 12.

[0051] In an alternative example, the processor calculates the direction of electrical propagation without calculating the speed and indicates the direction on the display without indicating the speed.

[0052] Next, for further details regarding some examples of algorithm 53, reference is made to FIG. 3, which is a schematic diagram of propagation speed calculation according to some examples of the present disclosure.

[0053] Figure 3 shows multiple measurement locations 44 on the anatomical surface 42. Following the acquisition of LAT at the measurement locations 44 by performing the LAT acquisition step 55, the processor performs the subsequent steps of the algorithm 53 described above. Thus, for example, when performing the selection step 59, the processor can first select a sampling location 45 on the anatomical surface 42. (As will be further explained below with reference to Figure 5, the sampling location 45 does not necessarily coincide with one of the measurement locations 44.) Subsequently, the processor can identify the measurement locations 44 that are within a predetermined distance D0 from the sampling location 45, and then select a subset of measurement locations from the identified measurement locations. For example, according to the example in Figure 3, the processor selects measurement locations 44a, 44b, and 44c from the measurement locations within a distance D0 from the sampling location 45, excluding measurement locations 44d and 44e.

[0054] As will be further explained below with reference to Figures 5 to 10, if algorithm 53 is not executed in real time, D0 is typically 6 mm to 12 mm. As will be further explained below with reference to Figures 11 to 12, if algorithm 53 is executed in real time, D0 is typically 4 mm to 8 mm, such as 5 mm to 7 mm. More generally, D0 can be a function of the density of the measurement location.

[0055] (Note that although it appears two-dimensional in Figure 3, the anatomical surface 42 is three-dimensional. Therefore, the processor typically uses geodesic distance measurement to calculate the distance along the surface.) In some examples, if the processor subsequently determines in the subset size evaluation step 63 that the subset is too small, the processor may repeat the selection process each time to increase the distance D0 one or more times (up to a predetermined maximum number of times).

[0056] Once it is confirmed that the subset is sufficiently large, the processor calculates the propagation direction as described above. Subsequently, the processor calculates the propagation velocity in the velocity calculation step 73.

[0057] In some examples, to calculate the propagation velocity, the processor first projects a subset of measurement locations onto a virtual line 47, which passes through the sampling location 45 and is oriented in the direction of electrical propagation at the sampling location. The processor then calculates the distance along the line 47 where the projection exists for each location. Next, for each different measurement location within the subset, the processor defines a group of points 49, each containing (i) the distance along the line where the projection of the measurement location exists, and (ii) the LAT at the measurement location. Subsequently, the processor fits a regression function 51 to the points 49 (or to a subset of points 49 near the LAT measured at the sampling location 45, or interpolated relative to the sampling location 45 as described below with reference to Figure 5), and then calculates the electrical propagation velocity as the gradient of the function 51.

[0058] For example, function 51 may be a line, and the electrical propagation velocity may be calculated as the gradient of that line. Alternatively, the function may be a polynomial of degree two or higher, a spline function, or any other suitable type of function. In such an example, the velocity may be calculated as the gradient of the function in LAT measured at sampling position 45 or interpolated for sampling position 45. Alternatively, the processor may calculate the electrical propagation velocity in any other suitable manner. For example, the processor may calculate a 2x2 covariance matrix of point 49 and then calculate the electrical propagation velocity based on the first principal component of this matrix. Alternatively, the processor may divide the projection into two groups: a first group on one side of sampling position 45 and a second group on the other side of sampling position 45. (Therefore, according to the example in Figure 3, the first group includes the projections of measurement positions 44a and 44b, while the second group includes the projection of measurement position 44c.) Subsequently, the processor may calculate the centroid of each group, where each centroid is the average position of the projections within the group. Next, the velocity can be calculated as the gradient of the line passing between the two centers of gravity.

[0059] For further details regarding selection step 59, refer similarly to Figure 4, a flowchart of selection step 59 with some examples in this disclosure.

[0060] The selection step 59 begins with the sampling location selection step 60, in which the processor selects the sampling location 45. Next, in the check step 62, the processor checks whether any measurement locations within a distance D0 from the sampling location have not yet been processed. If yes, in the measurement location identification step 70, the processor identifies one of these measurement locations for processing. Subsequently, in the difference calculation step 74, the processor calculates the difference between the LAT at the measurement location and the LAT at the sampling location.

[0061] Based on the calculated LAT difference, the processor calculates a propagation velocity estimate in the velocity estimation step 76, which estimates the propagation velocity between the sampling position and the measurement position. Typically, this estimate is the quotient of the LAT difference and the distance between the sampling position and the measurement position, which is calculated by the processor in the check step 62.

[0062] Next, in check step 78, the processor determines that the propagation velocity estimate is equal to a predetermined velocity estimation threshold v T1 Check if it exceeds v. If yes, add the measurement location to the selected subset of measurement locations in subset increment step 80. Otherwise, do not add the measurement location to the subset. (If the propagation velocity estimate is v T1 If it does not exceed this limit, there is a block between the sampling position and the measurement position, which may lead to inaccurate subsequent calculations of the propagation velocity when the measurement position is added to the subset.

[0063] Following the subset increment step 80, or when the propagation velocity estimate is v T1 If it does not exceed the limit, the processor returns to check step 62. Once it is confirmed that all measurement locations within distance D0 have been processed, selection step 59 ends.

[0064] Model-based calculations and displays Next, refer to Figure 5, which is a schematic diagram of the surface 42' of a digital model 38, according to some examples of the present disclosure.

[0065] In some examples, the processor constructs a model 38 from a point cloud corresponding to multiple positions at the distal end of the probe 26 (Figure 1) on an anatomical surface 42 (Figure 3), typically by performing a triangle tessellation of the point cloud. The measurement positions 44 (Figure 3) correspond to different measurement points 44' on the surface 42' of the model 38. (Note that since the surface 42' is a "best fit" that does not necessarily pass through all points in the point cloud, some measurement points 44' can be calculated by projecting points from the point cloud onto the surface 42'.)

[0066] In such cases, the calculation of propagation velocity may be performed after the model has been built. Specifically, the processor may specify multiple sampling points 45' on the surface 42' by, for example, uniformly sampling the surface. The processor may then iterate over all the sampling points when executing algorithm 53 (Figure 2). Specifically, in evaluation step 57 of algorithm 53, the processor may evaluate whether any sampling point has not yet been processed. If yes, the processor may perform an example of selection step 59, which is called selection step 59' based on the model below. Subsequently, if the selected subset of measurement locations is sufficiently large, the processor may calculate the propagation velocity of the sampling points as described above, with reference to Figures 2-3.

[0067] Next, we refer further to Figure 6, which is a flowchart of the model-based selection step 59', with some examples from this disclosure.

[0068] The model-based selection step 59' begins with the sampling point selection step 60', in which the processor selects the sampling points.

[0069] In some examples, following the sampling point selection step 60', the processor checks in check step 71 whether any measurement point is within a distance D0 from the selected sampling point. If not, the model-based selection step 59' ends. Otherwise, the processor checks in check step 61 whether the selected sampling point is within a predetermined distance D1 of any of the measurement points 44'. Typically, D1 is much smaller than D0, for example, less than or equal to 0.5 mm.

[0070] If the sampling point lies within D1 of at least one measurement point, the processor assumes that this sampling point coincides with the nearest measurement point. (In other words, the processor assumes that the sampling location corresponding to the sampling point coincides with the nearest measurement location from which the LAT was acquired.) Therefore, in the LAT assignment step 58, the processor assigns the LAT of the nearest measurement point to the sampling point.

[0071] Alternatively, if the measurement point is not within the D1 of the sampling point, the processor calculates the interpolated LAT of the sampling point by interpolating at least some of the LATs associated with the measurement points identified in the check step 71, as further described below with reference to Figures 7 and 8, in the interpolation step 56, and assigns the interpolated LAT to the sampling point.

[0072] Therefore, for example, as shown in Figure 5, the first sampling point 45'a may be assigned the LAT of the nearest measurement point 44'a, but the interpolated LAT may be calculated for a second sampling point 45'b that is not close enough to any measurement point.

[0073] Following the assignment of LAT to the sampling points, the processor identifies unprocessed measurement points for processing in the measurement point identification step 70'. Subsequently, in the difference calculation step 74', the processor calculates the difference between the LAT associated with the sampling points and the LAT associated with the measurement points. Based on the calculated LAT difference, the processor estimates the propagation velocity between the measurement points and the sampling points in the velocity estimation step 76'. Subsequently, in the check step 78, the processor checks if the propagation velocity estimate is equal to a predetermined velocity estimation threshold v T1 Check if it exceeds a certain threshold. If yes, add a measurement point to the selected subset of measurement locations in subset increment step 80'. Otherwise, do not add a measurement point to the subset.

[0074] Following the subset increment step 80, or when the propagation velocity estimate is v T1 If the value does not exceed this, the processor checks in check step 62' whether there are any other unprocessed measurement points within distance D0. Once it is confirmed that all measurement points within distance D0 have been processed, the selection step 59' ends.

[0075] In some examples, in order to compute the interpolated LAT in interpolation step 56, the processor first estimates the propagation velocity between pairs of measurement points for each cluster, v T1 Unlike a predetermined velocity estimation threshold v T2 The measurement points that are within a distance D0 from the sampling point are clustered into one or more clusters so that the result exceeds a certain value. The processor then identifies one of the clusters based on the distance between the sampling point and each cluster. The processor then calculates the interpolated LAT of the sampling point as a weighted average of the LATs associated with at least some of the measurement points in the identified cluster.

[0076] In this regard, next we refer to Figure 7, a flowchart of interpolation step 56, with some examples from the present disclosure.

[0077] Interpolation step 56 begins with clustering step 84, in which the processor clusters the measurement points that are located at a distance D0 from the sampling point, as described above. An example of clustering step 84 is described below with reference to Figure 8.

[0078] Following the clustering step 84, each cluster is selected in the first cluster selection step 86. For each selected cluster, the processor calculates the distance from the sampling point to the cluster in the distance calculation step 88. In general, any preferred definition of this distance can be used. For example, the processor may calculate this distance as the average distance between the sampling point and the N closest measurement points within the cluster, where N is, for example, 2, 3, or 4.

[0079] Next, in check step 90, the processor checks if there are any other clusters that should be selected. If yes, the processor returns to cluster selection step 86. Otherwise, in cluster identification step 92, the processor identifies the cluster that has the shortest distance from the sampling point. Following cluster identification, in weighted averaging step 94, the processor calculates the interpolated LAT as a weighted average of LAT associated with at least some of the measurement points in the identified cluster, such as the N measurement points closest to the sampling point. Typically, the weight of each i-th measurement point on the LAT is w i teeth,

[0080]

number

[0081] By performing interpolation as described above, the processor generally avoids the interpolated LAT being based on measurement points separated from the sampling point by electrically inert tissue.

[0082] Next, we refer to Figure 8, a flowchart of the clustering step 84, with some examples from this disclosure.

[0083] To perform the clustering step 84, the processor iteratively selects each measurement point within a distance D0 from the sampling point in the first measurement point selection step 96. For each selected measurement point, referred to as "MP1" in Figure 8, the processor checks in the check step 98 whether any cluster exists and has not yet been selected. If it does not exist, the processor initializes a cluster at MP1 in the cluster initialization step 112. Otherwise, the processor selects the next cluster that has not yet been selected in the second cluster selection step 100.

[0084] Following the selection of the cluster, in the second measurement point selection step 102, the processor selects one of the measurement points within the cluster, which is called "MP2" in Figure 8. Next, in another velocity estimation step 104, the processor calculates a propagation velocity estimate that estimates the propagation velocity between MP1 and MP2. The processor then checks in step 106 that the propagation velocity estimate is below the threshold v T2 The processor checks if the value exceeds a certain threshold. If it does not, the processor returns to check step 98. Otherwise, in check step 108, the processor checks if any measurement points in the cluster have not yet been selected. If there is at least one measurement point that has not yet been selected, the processor returns to second measurement point selection step 102 and selects the next measurement point MP2. Otherwise, in cluster growth step 110, the processor adds MP1 to the cluster.

[0085] Following the cluster growth step 110 or cluster initialization step 112, the processor checks in the check step 114 whether any measurement point within distance D0 from the sampling point has not yet been selected. If yes, the processor returns to the first measurement point selection step 96 and selects the next measurement point MP1. Otherwise, the clustering step 84 ends.

[0086] Next, refer to Figure 9A, which is a schematic diagram of the displayed model, illustrated by some examples of the present disclosure.

[0087] Following the calculation of the propagation velocity, the processor displays the model 38 (in particular, the model surface 42') with each marker 116 superimposed on the model at the sampling point 45' and oriented in the direction of electrical propagation. (For better visibility, each marker 116 may be slightly offset from its sampling point, for example, in a direction parallel to the model surface 42'.) Each marker 116 may have any preferred shape, such as the arrowhead shape or full arrow shape shown in Figure 9A. Optionally, the location of the sampling point on the model surface 42' may be marked with additional markers, such as circles shown in Figure 9A.

[0088] In addition to orienting the marker 116 to indicate the direction of electrical propagation, the processor may change at least one other characteristic of the marker, such as the color, shape, length, or thickness, according to the electrical propagation speed. For example, that characteristic may change based on a predetermined speed threshold v T3 Not exceeding (or as described below, the confidence threshold measurement is v T3 Each velocity can be set to a first value (not exceeding) and, otherwise, to a second value. As a specific example, if the velocity is v T3 A thicker marker 116a may be placed at each sampling point that does not exceed a certain value, while a thinner marker 116b may be placed at other sampling points. Thus, the thicker marker 116a indicates areas of slow conduction on anatomical surfaces that may be of interest to the physician.

[0089] In some examples, the velocity threshold v T3For each velocity that does not exceed a certain value, a confidence level is calculated. If the confidence level exceeds a predetermined confidence threshold, the marker characteristic is set to the first value; otherwise, the characteristic is set to the second value. The confidence level can be defined, for example, as the ratio of (i) the number of adjacent sampling points whose velocity does not exceed the velocity threshold to (ii) the total number of adjacent sampling points. The adjacent sampling point S2 to sampling point S1 is, for example, when the estimated propagation velocity between S1 and S2 is v T1 It can be defined as any sampling point that is within a predetermined distance from S1, provided that it exceeds a certain threshold.

[0090] In some examples, the model is displayed to further illustrate other properties of the anatomical surface. For example, the model surface 42' may be colored to show the LAT on the anatomical surface.

[0091] In some cases, the processor smooths the direction of electrical propagation before visually showing the direction of electrical propagation. For example, the processor can perform Laplacian smoothing, thereby making the unit propagation direction vector V[i] at each sampling point equal to α during each i-th iteration of the smoothing. * V[i-1]+(1-α) * V A [i] is calculated as, and in the formula, V A v is the average unit propagation direction vector of the neighboring points of the sampling point. (As mentioned above, the neighboring points are the estimated propagation velocity between the two points v T1 (This may be any other sampling point within a predetermined distance from the sampling point, provided that it exceeds a certain value.) Figure 9A shows an exemplary result of such a smoothing operation by showing one of the markers that has been reoriented in approximately the same direction as its neighbor.

[0092] Next, refer to Figure 9B, which is another schematic diagram of the displayed model, with some examples from this disclosure.

[0093] In some cases, instead of, or in addition to, smoothing the direction of electrical propagation, the processor condenses the sampling points so that they approximately follow one or more average propagation paths 120 before displaying the model with the markers superimposed. Thus, the processor facilitates the interpretation of the displayed model by the physician.

[0094] Typically, condensation is performed by executing an iterative algorithm. In each iteration of the algorithm, the processor recalculates the direction of electrical propagation and then shifts the sampling points toward each other according to the direction of electrical propagation.

[0095] In this regard, next, we refer to Figure 10, which is a flowchart of one such iterative condensation algorithm step 121, by some examples of the present disclosure.

[0096] At the start of each iteration of algorithm 121, the processor recalculates the propagation direction in recalculation step 128. Typically, in this step, the calculation of the propagation velocity is performed as described above with reference to Figure 2, with the proximity points of the sampling points replaced by a subset of the measurement points. For example, for each sampling point, the processor may calculate the propagation direction by (i) constructing the respective vectors of the sampling point and its proximity points, (ii) performing PCA on the corresponding 4x4 covariance matrix, and (iii) based on the PCA, for example, by projecting the first principal component of the covariance matrix onto each dimension of the position coordinates.

[0097] Next, in check step 123, the processor checks whether there are other sampling points that should be selected. If yes, in sampling point selection step 125, the processor selects the next sampling point. Then, in average position calculation step 122, the processor calculates the average position of neighboring points of the sampling point. These points include the selected sampling point, along with its neighbors. As described above with reference to Figure 9A, the neighbors are such that the propagation velocity estimate between the two sampling points is the velocity estimation threshold v T1 It may be defined as any other sampling point that is within a predetermined distance from the selected sampling point, provided that it exceeds a certain threshold.

[0098] Following the average position calculation step 122, the processor calculates the projection of the sampling point onto a line oriented in the propagation direction at the sampling point and passing through the average position in the projection calculation step 124. The processor then moves the sampling point toward the projection in the point movement step 126. For example, during each i-th iteration of the algorithm, the processor moves the new position P[i] of the sampling point to α * P[i-1]+(1-α) * P P [i] can be calculated as, in the formula, P P This is the projection of the sampling point. Following the movement of the sampling point, the processor returns to check step 123.

[0099] In check step 123, once it is confirmed that all sampling points have been selected during this iteration, the processor evaluates in evaluation step 130 whether to perform another iteration. If "yes," the processor performs the next iteration of the algorithm. Otherwise, the execution of the algorithm terminates.

[0100] Generally, the evaluation step 130 may be based on any preferred criterion. For example, the processor may terminate the execution of the algorithm if, during this iteration, none of the sampling points have moved beyond a predetermined threshold distance, or if a predetermined maximum number of iterations have been performed.

[0101] Real-time calculation and display Next, refer to Figure 11, which shows schematic diagrams of the probe 26 and anatomical surface 42 according to some examples of the present disclosure.

[0102] As described above with reference to Figures 1 and 2, the LAT at the measurement position 44 is calculated by the processor based on the signal acquired by the electrode 28 belonging to the probe 26. In some examples, the processor further calculates the propagation velocity (or at least the propagation direction) at at least some of the measurement positions 44 in real time, i.e., while the electrode is at the measurement position. (In other words, in real time, the processor treats at least some of the measurement positions 44 as sampling positions 45 and thus calculates the propagation velocity at these measurement positions without using a digital model of the anatomical surface.) For example, the propagation velocity may be calculated once per cardiac cycle. This real-time calculation may be performed in place of, or in addition to, the calculation based on the model described above, as described above with reference to Figures 2 to 4.

[0103] Next, refer to Figure 12, which is a schematic diagram of real-time visual indication of propagation speed, as illustrated by some examples of the present disclosure.

[0104] Following each real-time calculation of the propagation speed, the processor displays the speed. Typically, to indicate the direction of electrical propagation, the processor displays a probe icon 26' and places each marker 117 oriented in the direction of electrical propagation in the portion 28' of the icon corresponding to the electrode whose direction is located at the calculated sampling position. (For simplicity, markers 117 are shown in Figure 12 for only a few electrodes.) In some examples, before indicating the direction of electrical propagation, the processor smooths its orientation as described above with reference to Figure 9A.

[0105] Typically, the processor changes at least one characteristic of the marker (e.g., color, shape, length, or thickness) according to the electrical propagation speed. For example, the marker may (i) have a velocity that falls within a first range (e.g., a velocity greater than v) (e.g., a velocity greater than v T3 (i) those that are larger than (ii) have a first shape and a first thickness, and (ii) have a velocity that is lower than the first range and belongs to a second range (for example, v T3 Less than, but the lower threshold v T4 (iii) those that are larger than the first shape and second thickness, and those with a velocity that belongs to a third range smaller than the second range (for example, v T4 For speeds less than a certain speed, a second shape may be present. Figure 12 shows such an example, in which (i) the first marker 117a includes a longer and thinner arrow indicating a normal propagation speed, (ii) the second marker 117b includes a shorter and thicker arrow indicating a slower propagation speed, and (iii) the third marker 117c includes a circle indicating electrically inert tissue. (Typically, to avoid mismarking, electrically inert tissue is marked only when it is known that the relevant electrode is in contact with the tissue.)

[0106] Optionally, other properties of the marker may be modified according to the LAT. For example, the marker may be colored according to a color scale based on the LAT.

[0107] As shown in Figure 12, the probe icon may be superimposed on an image 42" of the mapped anatomical surface. Alternatively, the icon may be superimposed on the surface of a model. In some examples, the display of the icon and marker is refreshed multiple times per cardiac cycle to account for the movement of the probe during the cardiac cycle.

[0108] Optimal selection of bipolar voltage Next, refer to Figure 13, which is a schematic diagram illustrating a method for selecting a pair of electrodes for bipolar voltage measurement, according to some examples of the present disclosure.

[0109] In some examples, following the calculation of the direction of electrical propagation at each electrode 28 location, the processor selects pairs 139 of adjacent electrodes such that, for each pair 139, the vector 140 connecting the pairs to each other aligns with the direction of electrical propagation at one of the electrodes belonging to the pair within a predetermined threshold alignment degree. (Thus, pair selection is based on the direction of propagation, not the propagation velocity.) Following the selection of electrode pairs, the processor associates the respective bipolar voltages measured by the electrode pairs with the model 38 (Figure 1). Thus, advantageously, bipolar voltages with less relevance are omitted from the model.

[0110] In a rectangular grid of electrodes as shown in Figure 13, the distance between adjacent (or "nearby") electrodes in the same row is the same as the distance between adjacent rows, and the threshold alignment is generally 45°. For each electrode (typically one of the corner electrodes), the processor decides whether to pair this electrode with an adjacent electrode in the same row, with an adjacent electrode in an adjacent row, or not to pair the electrode at all. In particular, for each potentially pairable adjacent electrode, the processor calculates the angle θ between a vector 140 pointing from the electrode to the adjacent electrode (or vice versa) and another vector 118 oriented in the propagation direction. If θ (or |180°-θ|) is less than the threshold angle, the adjacent electrode is paired with that electrode.

[0111] (Typically, only a single adjacent electrode in the same row is potentially pairable, and this electrode is always to the left or always to the right of the electrode being paired. Similarly, typically, only a single adjacent electrode in an adjacent row is potentially pairable, and the adjacent row is always above or always below the electrode being paired. Therefore, electrode pairs are never selected multiple times.)

[0112] Therefore, in the example shown in Figure 13, the first electrode 28a is paired with the adjacent electrode below electrode 28a in the same column, and the second electrode 28b is paired with the adjacent electrode in the column to the right of the second electrode 28b.

[0113] Next, we refer further to Figure 14, which shows the method for a hexagonal arrangement of electrodes, as illustrated in Figure 13, by some example of the present disclosure.

[0114] In some examples, as described in U.S. Patent Application No. 17 / 092,627, whose disclosure is incorporated herein by reference, the electrodes 28 are arranged in a hexagonal grid such that each electrode is equidistant from up to six neighboring electrodes. For example, the rows of electrodes on the spline 29 (Figure 13) may be staggered. In such examples, the threshold alignment is generally 30°, and the processor considers up to three neighboring electrodes for pairing.

[0115] Next, for further details, refer to Figure 15, a flowchart of algorithm 142 for selecting electrode pairs for bipolar voltage measurement, using several examples from this disclosure.

[0116] Typically, algorithm 142 is executed at least once per cardiac cycle, for example, exactly once. In each algorithm 142, each of the electrodes (typically except one of the corner electrodes that does not have a potentially pairable neighbor electrode) is selected in the electrode selection step 144. Following electrode selection, a potentially pairable neighbor electrode (e.g., a right-hand or downward neighbor electrode of the selected electrode) is selected in the neighbor selection step 146. The processor then calculates the angle θ of the electrode pair (i.e., the selected electrode and its selected neighbor electrode) in the angle calculation step 148 (Figures 13-14).

[0117] Following the calculation of θ, the processor checks in the angle comparison step 150 whether θ (or |180°-θ|) is less than a threshold angle (e.g., 45° or 30°). If yes, this pair of electrodes is selected in the pair selection step 152 for bipolar voltage measurement (i.e., the bipolar voltage between the pair is selected for association with Model 38 (Figure 5)). Otherwise, the processor checks in the check step 154 ​​whether any potentially pairable neighbor electrodes have not yet been selected. If yes, the processor returns to the neighbor selection step 146 to select the next potentially pairable neighbor electrode.

[0118] Following the pair selection step 152, or confirming that any potentially pairable neighboring electrodes remain unselected, the processor checks in the check step 156 whether any electrodes remain to be selected. If yes, the processor returns to the electrode selection step 144 and selects the next electrode. Otherwise, in the model increment step 158, the processor associates the bipolar voltage measured by the selected electrode pair with the model 38 (Figure 5). For example, the processor may color the surface 42' of the model according to a color scale that spans the entire range of bipolar voltages. Alternatively or additionally, in response to moving the mouse pointer over a point on the surface 42', or clicking the mouse over a point, the processor may display an indication of the electrode pair from which the bipolar voltage was acquired at that point, and / or the bipolar voltage signal itself.

[0119] Enhanced LAT calculation Next, we refer to Figure 16, a flowchart of algorithm 160 for calculating each LAT at each electrode position according to some examples of the present invention. Algorithm 160 can be executed by the processor at any point during the electroanatomical mapping procedure.

[0120] As an introduction, it should be noted that algorithm 160 utilizes a function configured to return a set of candidate LATs at an arbitrary position based on unipolar and bipolar voltage signals obtained from that position. Such a function is described, for example, in U.S. Patent No. 9,380,953 by Houben et al., the disclosure of which is incorporated herein by reference.

[0121] Each iteration of algorithm 160 begins with an electrode selection step 144, in which the processor selects electrode E1 belonging to the probe. Following the selection of E1, the processor selects electrode E2 adjacent to E1 (i.e., adjacent to E1) in a proximity selection step 162. Next, in a signal input step 164, the processor inputs two signals to the aforementioned function: a unipolar voltage signal representing the unipolar voltage between E1 and the reference electrode, and a bipolar voltage signal representing the bipolar voltage between E1 and E2. Subsequently, in an output reception step 166, the processor receives as output from the function a set of candidate LATs calculated by the function based on the inputs. For example, this output may include a bipolar voltage signal with annotations marking candidate LATs.

[0122] Next, in check step 168, the processor checks whether E1 has other near-near electrodes. If yes, the processor returns to the proximity selection step 162 and selects the next near-near electrode for E1. Otherwise, in LAT selection step 170, the processor selects a LAT from all received candidate sets. For example, the processor may select a candidate LAT that has the largest derivative of the unipolar signal compared to other candidate LATs.

[0123] Therefore, for example, given a rectangular grid of electrodes where each electrode has up to four equidistant neighboring electrodes, the processor can select a LAT from up to four candidate sets. Given a hexagonal array where each electrode has up to six equidistant neighboring electrodes, the processor can select a LAT from up to six candidate sets. [Examples]

[0124] The following embodiments relate to various non-exclusive methods by which the teachings herein can be combined or applied. It should be understood that the following embodiments are not intended to limit the scope of any claims that may be presented in this application or in any subsequent filings relating to this application. No waiver of any rights is intended. The following embodiments are given solely for illustrative purposes. Various teachings herein are intended to be constructed and applied in many other ways. In some modifications, certain features mentioned in the following embodiments may be omitted. Accordingly, none of the embodiments or features mentioned below should be considered essential unless explicitly indicated thereafter by the inventors or their successors. If any claim is presented in this application or in any subsequent filing relating to this application that includes further features other than those mentioned below, those further features should not be assumed to have been added for any reason relating to patentability.

[0125] (Example 1) The system includes a display and a processor configured to acquire multiple local excitation times (LATs) at different measurement locations on the anatomical surface of the heart. The processor is further configured to calculate the direction of electrical propagation at one or more sampling locations on the anatomical surface by: selecting a subset of measurement locations relative to each sampling location; constructing a set of vectors, each of which at least some vectors includes, for each different measurement location in the subset, three position values ​​derived from the position coordinates of each measurement location and a LAT value derived from the LAT at the measurement location; and calculating the direction of electrical propagation at the sampling location based on principal component analysis (PCA) of a 4x4 covariance matrix of the set of vectors. The processor is further configured to display the direction of electrical propagation on the display.

[0126] (Example 2) The processor is Calculating the scaling factor based on the variance of LAT across the entire subset of measurement locations, For each measurement location within the subset, The scaling factor scales a parameter selected from a group of parameters consisting of the position coordinates of the measurement location and the LAT at the measurement location. From scaled parameters, construct a vector corresponding to the measurement position, The system described in Example 1 is configured to construct a set of vectors.

[0127] (Example 3) The system according to Example 1 or 2, wherein the processor is configured to calculate the direction of electrical propagation at the sampling location by projecting the first principal component of the covariance matrix onto each dimension of the position coordinates.

[0128] (Example 4) The processor determines the rate of electrical propagation at each sampling location. For a virtual line that passes through the sampling position and is oriented in the direction of electrical propagation at the sampling position, calculate the respective distances along the virtual line where each projection of a subset of measurement positions exists onto the virtual line, The calculation involves determining the velocity as the gradient of a regression function fitted to a group of regression points, wherein each regression point, for each different measurement location belonging to the subset, includes (i) the distance along the line on which the projection of the measurement location exists, and (ii) the LAT at the measurement location. The system according to any one of Examples 1 to 3, further configured to perform calculations by...

[0129] (Example 5) The system according to any one of Examples 1 to 4, wherein the processor is further configured to smooth the direction of electrical propagation before indicating the direction of electrical propagation.

[0130] (Example 6) The processor processes each subset of measurement locations relative to the sampling location. Identifying measurement locations that are within a predetermined distance from the sampling location, Selecting a subset of measurement locations from the identified measurement locations, The system according to any one of Examples 1 to 5, configured to be selected by

[0131] (Example 7) The processor uses a subset of For each measurement location within a predetermined distance from the sampling location, This involves calculating a propagation velocity estimate to determine the propagation velocity between the sampling position and the measurement position, The measurement location is selected under the condition that this propagation speed exceeds a predetermined velocity estimation threshold, The system according to Example 6, which is configured to be selected by

[0132] (Example 8) The system according to Example 7, wherein the processor is further configured to calculate an interpolated LAT of the sampling position, and the processor is configured to calculate a propagation velocity estimate based on this interpolated LAT.

[0133] (Example 9) The predetermined velocity estimation threshold is the first predetermined velocity estimation threshold, and the propagation velocity estimate is the first propagation velocity estimate. The processor uses the interpolated LAT. For each cluster, for each pair of measurement locations within the cluster, the measurement locations that are within a predetermined distance from the sampling location are clustered into one or more clusters such that the second propagation velocity estimate, which estimates the propagation velocity between pairs of measurement locations, exceeds a second predetermined velocity estimation threshold. Identifying one of the clusters based on the respective distances between the sampling location and the cluster, The interpolated LAT is calculated as a weighted average of LAT at at least some of the measurement locations in one of the identified clusters, The system according to Example 8, which is configured to perform calculations by...

[0134] (Example 10) The measurement locations correspond to different measurement points on the digital model surface representing the anatomical surface, and these measurement points are each associated with LAT. The processor is further configured to specify multiple sampling points on the surface of the digital model. The sampling positions correspond to each sampling point. The system described in any one of Examples 1 to 9.

[0135] (Example 11) The system according to Example 10, wherein the processor is configured to indicate the direction of electrical propagation by displaying the model surface with each marker superimposed on the model surface at the sampling point and oriented in the direction of electrical propagation.

[0136] (Example 12) The processor is Calculate the respective rates of electrical propagation at the sampling location. Change at least one property of the marker according to this speed. The system described in Example 11 is further configured as follows.

[0137] (Example 13) The processor, before displaying the model surface with the markers overlapping it, To repeat, Recalculate the direction of electrical propagation, Depending on the direction of electrical propagation, the sampling points are shifted toward each other. The system according to Example 11 or 12, further configured as follows.

[0138] (Example 14) The processor is configured to acquire LAT by calculating it based on the signals acquired by each electrode belonging to the in-vivo probe. The sampling location includes at least some of the measurement locations. The processor is configured to indicate the direction of electrical propagation while the electrodes are each at the measurement position. The system described in any one of Examples 1 to 9.

[0139] (Example 15) The processor determines the direction of electrical propagation. Displaying the probe icon, Each marker, oriented in the direction of electrical propagation, is placed on the icon corresponding to the electrode located at the sampling position. The system according to Example 14, configured as shown.

[0140] (Example 16) The processor is Calculate the respective rates of electrical propagation at the sampling location. Change at least one property of the marker according to this speed. The system described in Example 15 is further configured as follows.

[0141] (Example 17) The method involves obtaining multiple local excitation times (LATs) at different measurement locations on the anatomical surface of the heart. The method further includes calculating the direction of electrical propagation at one or more sampling locations on the anatomical surface by: selecting a subset of measurement locations relative to each sampling location; constructing a set of vectors, each of which at least some vectors includes, for each different measurement location in the subset, three position values ​​derived from the position coordinates of each measurement location and a LAT value derived from the LAT at the measurement location; and calculating the direction of electrical propagation at the sampling location based on principal component analysis (PCA) of a 4x4 covariance matrix of the set of vectors. The method further includes displaying the direction of electrical propagation on a display.

[0142] (Example 18) Constructing a set of vectors is Calculating the scaling factor based on the variance of LAT across the entire subset of measurement locations, For each measurement location within the subset, The scaling factor scales a parameter selected from a group of parameters consisting of the position coordinates of the measurement location and the LAT at the measurement location. From scaled parameters, construct a vector corresponding to the measurement position, The method according to Example 17, including the method described in Example 17.

[0143] (Example 19) The method according to Example 17 or 18, wherein calculating the direction of electrical propagation at the sampling location involves calculating the direction of electrical propagation by projecting the first principal component of the covariance matrix onto each dimension of the position coordinates.

[0144] (Example 20) The rate of electrical propagation at each sampling location is For a virtual line that passes through the sampling position and is oriented in the direction of electrical propagation at the sampling position, calculate the respective distances along the virtual line where each projection of a subset of measurement positions exists onto the virtual line, The calculation involves determining the velocity as the gradient of a regression function fitted to a group of regression points, wherein each regression point, for each different measurement location belonging to the subset, includes (i) the distance along the line on which the projection of the measurement location exists, and (ii) the LAT at the measurement location. The method according to any one of Examples 17 to 19, further configured to calculate by

[0145] (Example 21) The measurement locations correspond to different measurement points on the digital model surface representing the anatomical surface, and these measurement points are each associated with LAT. This method further includes specifying multiple sampling points on the surface of the digital model, The sampling positions correspond to each sampling point. The method according to any one of Examples 17 to 20.

[0146] (Example 22) The system (20) includes an electrical interface (34) and a processor (32). The processor (32) is configured to receive respective signals acquired by a plurality of electrodes (28) on the anatomical surface (42) of the heart (24) via the electrical interface (34). Based on these signals, the processor (32) is further configured to calculate the respective local excitation time (LAT) at each location of the electrodes (28). Based on the LAT, the processor (32) is further configured to calculate the respective direction of electrical propagation at these locations. The processor (32) is further configured to select pairs (139) of adjacent electrodes (28) such that, for each pair (139), the vector (140) connecting the pairs (139) is aligned within a predetermined alignment threshold with the direction of electrical propagation at one location of the electrodes (28) belonging to the pair (139). The processor (32) is further configured to associate each bipolar voltage measured by the pair of electrodes (28) (139) with a digital model (38) of the anatomical surface (42).

[0147] (Example 23) The processor (32) determines the LAT at the position of each first electrode of the electrode (28), This involves obtaining multiple candidate sets of LAT for that position, and for each second electrode among the electrodes (28) adjacent to the first electrode, The input to the function is to provide (i) a unipolar voltage signal representing the unipolar voltage between the first electrode and the reference electrode, and (ii) a bipolar voltage signal representing the bipolar voltage between the first electrode and the second electrode. The function will receive one of each candidate from the set as output, To obtain by, Select LAT from the candidate set, The system (20) described in Example 22 is configured to perform calculations by [the specified method].

[0148] (Example 24) The method includes calculating the respective local excitation time (LAT) at each location of the electrodes (28) based on the respective signals acquired by the electrodes (28) on the anatomical surface (42) of the heart (24). The method further includes calculating the respective direction of electrical propagation at these locations based on the LAT. The method further includes selecting pairs (139) of adjacent electrodes (28) such that, for each pair (139), the vector (140) joining the pair aligns within a predetermined alignment threshold with the direction of electrical propagation at one location of the electrodes (28) belonging to the pair (139). The method further includes relating the respective bipolar voltages measured by the pairs (139) of electrodes (28) to a digital model (38) of the anatomical surface (42).

[0149] (Example 25) Calculating LAT means determining the LAT at the position of each first electrode among the electrodes (28). This involves obtaining multiple candidate sets of LAT for that position, and for each second electrode among the electrodes (28) adjacent to the first electrode, The input to the function is to provide (i) a unipolar voltage signal representing the unipolar voltage between the first electrode and the reference electrode, and (ii) a bipolar voltage signal representing the bipolar voltage between the first electrode and the second electrode. The function will receive one of each candidate from the set as output, To obtain by, Select LAT from the candidate set, The method according to Example 24, which includes calculating by

[0150] (Example 26) The computer software product includes a tangible, non-temporary computer-readable medium on which program instructions are stored. When the instructions are read by the processor (32), the processor (32) causes the processor (32) to receive the respective signals acquired by a plurality of electrodes (28) on the anatomical surface (42) of the heart (24), to calculate the respective local excitation time (LAT) at each location of the electrodes (28) based on these signals, to calculate the respective direction of electrical propagation at each location based on those LATs, to select adjacent pairs (139) of the electrodes (28) such that for each pair (139), the vector (140) connecting the pairs (139) aligns with the direction of electrical propagation at one location of the electrodes (28) belonging to the pair (139) within a predetermined alignment threshold, and to associate the respective bipolar voltages measured by the pairs (139) of electrodes (28) with a digital model (38) of the anatomical surface (42).

[0151] (Example 27) The system (20) includes a display (36) and a processor (32). The processor (32) is configured to calculate the respective local excitation time (LAT) at each location of the electrodes (28) based on the respective signals acquired by a plurality of electrodes (28) belonging to an internal probe (26) on the anatomical surface (42) of the heart (24). The processor (32) is further configured to calculate the respective direction and velocity of electrical propagation at these locations based on the LAT. The processor is further configured to display icons (26') of the probes (26) on the display (36) while each electrode (28) is in that location. The processor (32) is further configured to place respective markers (117) that are oriented in the direction of electrical propagation and have at least one characteristic that changes with velocity on the portion of the icon (26') corresponding to the electrode (28).

[0152] (Example 28) The processor (32) determines the LAT at the position of each first electrode of the electrode (28), This involves obtaining multiple candidate sets of LAT for that position, for each second electrode among the electrodes adjacent to the first electrode, The input to the function is to provide (i) a unipolar voltage signal representing the unipolar voltage between the first electrode and the reference electrode, and (ii) a bipolar voltage signal representing the bipolar voltage between the first electrode and the second electrode. The function will receive one of each candidate from the set as output, To obtain by, Select LAT from the candidate set, The system (20) described in Example 27 is configured to perform calculations by [the specified method].

[0153] (Example 29) The characteristic is that the marker (117) is For those with a velocity belonging to the first range, having a first shape and a first thickness, For those with velocities in a second range lower than the first range, having a first shape and a second thickness, For those with velocities in a third range lower than the second range, having a second shape, For this reason, the system (20) described in Example 27 or 28 changes according to the speed.

[0154] (Example 30) The marker (117) has at least one other characteristic that changes according to LAT, the system (20) according to Example 29.

[0155] (Example 31) The marker (117) is colored according to a color scale based on LAT, as described in the system (20) of Example 30.

[0156] (Example 32) The method includes calculating the respective local excitation time (LAT) at each position of the electrodes (28) based on the respective signals acquired by a plurality of electrodes (28) belonging to an internal probe (26) on the anatomical surface (42) of the heart (24). The method further includes calculating the respective direction and velocity of electrical propagation at these positions based on the LAT. The method further includes displaying icons (26') of the probe (26) while each electrode (28) is in that position. The method further includes placing each marker (117), each oriented in the direction of electrical propagation and having at least one characteristic that changes with velocity, on the portion of the icon (26') corresponding to the electrode (28).

[0157] (Example 33) Calculating LAT means determining the LAT at the position of each first electrode among the electrodes (28). This involves obtaining multiple candidate sets of LAT for that position, for each second electrode among the electrodes adjacent to the first electrode, The input to the function is to provide (i) a unipolar voltage signal representing the unipolar voltage between the first electrode and the reference electrode, and (ii) a bipolar voltage signal representing the bipolar voltage between the first electrode and the second electrode. The function will receive one of each candidate from the set as output, To obtain by, Select LAT from the candidate set, The method according to Example 32, which includes calculating by

[0158] (Example 34) The characteristic is that the marker (117) is For those with a velocity belonging to the first range, having a first shape and a first thickness, For those with velocities in a second range lower than the first range, having a first shape and a second thickness, For those with velocities in a third range lower than the second range, having a second shape, For this reason, the method according to Example 32 or 33, which changes according to speed.

[0159] (Example 35) The method according to Example 34, wherein the marker (117) has at least one other characteristic that changes according to LAT.

[0160] (Example 36) The marker (117) is colored according to a color scale based on LAT, as described in Example 35.

[0161] (Example 37) The computer software product includes a tangible, non-temporary computer-readable medium in which program instructions are stored. When the instructions are read by the processor (32), the processor (32) calculates the respective local excitation time (LAT) at each location of the electrodes (28) based on the signals acquired by the electrodes (28) belonging to the internal probe (26) on the anatomical surface (42) of the heart (24). The instructions further cause the processor (32) to calculate the respective direction and velocity of electrical propagation at these locations based on the LATs. The instructions further cause the processor (32) to display icons (26') of the probe (26) while the electrodes (28) are at their respective locations. The instructions further cause the processor (32) to place markers (117) on the portion of the icons (26') corresponding to the electrodes (28), each oriented in the direction of electrical propagation and having at least one characteristic that changes with velocity.

[0162] Those skilled in the art will understand that this disclosure is not limited to those specifically shown herein and described above. Rather, the scope of this disclosure includes the various combinations and partial combinations of the features described herein, as well as variations and modifications of features not found in the prior art, which those skilled in the art may conceive by reading the above description. Documents incorporated into this patent application by reference shall be considered integral parts of this application, except that, in such incorporated documents, only the definitions herein shall be considered to the extent that any term is defined in a manner that contradicts the definitions expressed herein, either explicitly or implicitly.

[0163] [Implementation Method] (1) A system, Electrical interface and It is a processor, The electrical interface receives the respective signals acquired by multiple electrodes on the anatomical surface of the heart. Based on the aforementioned signal, the local excitation time (LAT) at each of the electrode positions is calculated. Based on the aforementioned LAT, the respective directions of electrical propagation at the aforementioned location are calculated. For each of the electrodes, an adjacent pair of electrodes is selected such that the vector connecting the pair is aligned with the direction of electrical propagation at one of the electrodes belonging to the pair, within a predetermined threshold alignment degree. The bipolar voltages measured by the pair of electrodes are associated with the digital model of the anatomical surface. A processor configured in such a way, A system that includes this. (2) The processor controls the LAT at the position of each first electrode among the electrodes, Obtaining multiple candidate sets of LAT for the aforementioned position, wherein for each second electrode among the electrodes adjacent to the first electrode, The input to the function is to provide (i) a unipolar voltage signal representing the unipolar voltage between the first electrode and the reference electrode, and (ii) a bipolar voltage signal representing the bipolar voltage between the first electrode and the second electrode. The output from the aforementioned function is to receive one of each of the candidate sets, To obtain by, Select the LAT from the aforementioned candidate set, The system according to Embodiment 1, configured to calculate by (3) Based on the signals obtained by multiple electrodes on the anatomical surface of the heart, calculate the local excitation time (LAT) at each of the electrode locations, Based on the aforementioned LAT, the respective directions of electrical propagation at the aforementioned location are calculated, Selecting adjacent pairs of electrodes from the aforementioned electrodes such that, for each pair, the vector connecting the pair is aligned with the direction of electrical propagation at one of the electrodes belonging to the pair within a predetermined threshold alignment degree, The bipolar voltages measured by the pair of electrodes are associated with the digital model of the anatomical surface. A method that includes this. (4) Calculating the LAT means that the LAT at the position of each first electrode among the electrodes is Obtaining multiple candidate sets of LAT for the aforementioned position, wherein for each second electrode among the electrodes adjacent to the first electrode, The input to the function is to provide (i) a unipolar voltage signal representing the unipolar voltage between the first electrode and the reference electrode, and (ii) a bipolar voltage signal representing the bipolar voltage between the first electrode and the second electrode. The output from the aforementioned function is to receive one of each of the candidate sets, To obtain by, Select the LAT from the aforementioned candidate set, The method according to Embodiment 3, which includes calculating by (5) A computer software product comprising a tangible, non-temporary computer-readable medium in which program instructions are stored, wherein when the instructions are read by a processor, the processor The signals acquired by multiple electrodes on the anatomical surface of the heart are received. Based on the aforementioned signal, the local excitation time (LAT) at each of the electrode positions is calculated. Based on the aforementioned LAT, the respective directions of electrical propagation at the aforementioned location are calculated. For each of the electrodes, an adjacent pair of electrodes is selected such that the vector connecting the pair is aligned with the direction of electrical propagation at one of the electrodes belonging to the pair, within a predetermined threshold alignment degree. The bipolar voltages measured by the pair of electrodes are associated with the digital model of the anatomical surface. Computer software products.

[0164] (6) The instruction provides the processor with the LAT at the position of each first electrode among the electrodes, Obtaining multiple candidate sets of LAT for the aforementioned position, wherein for each second electrode among the electrodes adjacent to the first electrode, The input to the function is to provide (i) a unipolar voltage signal representing the unipolar voltage between the first electrode and the reference electrode, and (ii) a bipolar voltage signal representing the bipolar voltage between the first electrode and the second electrode. The output from the aforementioned function is to receive one of each of the candidate sets, To obtain by, Selecting the LAT from the aforementioned candidate set, A computer software product according to Embodiment 5, which is used to perform calculations. (7) A system, The display and It is a processor, Based on the signals acquired by multiple electrodes belonging to an in vivo probe on the anatomical surface of the heart, the local excitation time (LAT) at each of the electrode locations is calculated. Based on the aforementioned LAT, the respective directions and velocities of electrical propagation at the aforementioned location are calculated. While each of the electrodes is in the aforementioned position, the icon of the probe is displayed on the display. Each marker, oriented in the direction of electrical propagation and having at least one characteristic that changes according to the speed, is placed in the portion of the icon corresponding to the electrode. A processor configured in such a way, A system that includes this. (8) The processor controls the LAT at the position of each first electrode among the electrodes, Obtaining multiple candidate sets of LAT for the aforementioned position, wherein for each second electrode among the electrodes adjacent to the first electrode, The input to the function is to provide (i) a unipolar voltage signal representing the unipolar voltage between the first electrode and the reference electrode, and (ii) a bipolar voltage signal representing the bipolar voltage between the first electrode and the second electrode. The output from the aforementioned function is to receive one of each of the candidate sets, To obtain by, Selecting the LAT from the aforementioned candidate set, The system according to embodiment 7, configured to calculate by (9) The above characteristics are that the marker is For those with a velocity belonging to the first range, having a first shape and a first thickness, For speeds belonging to a second range lower than the first range, having the first shape and the second thickness, For those with velocities in a third range lower than the second range, having a second shape, For this reason, the system according to embodiment 7 changes according to the speed. (10) The system according to Embodiment 9, wherein the marker has at least one other characteristic that changes in accordance with the LAT.

[0165] (11) The system according to embodiment 10, wherein the marker is colored according to a color scale based on the LAT. (12) Based on the signals obtained by multiple electrodes belonging to an intracellular probe on the anatomical surface of the heart, calculate the local excitation time (LAT) at each of the electrode locations, Based on the aforementioned LAT, the respective directions and velocities of electrical propagation at the aforementioned location are calculated, The probe icon is displayed while each of the electrodes is in the aforementioned position, Each marker, oriented in the direction of electrical propagation and having at least one characteristic that changes according to the speed, is placed on the portion of the icon corresponding to the electrode. A method that includes this. (13) Calculating the LAT means that the LAT at the position of each first electrode among the electrodes is Obtaining multiple candidate sets of LAT for the aforementioned position, wherein for each second electrode among the electrodes adjacent to the first electrode, The input to the function is to provide (i) a unipolar voltage signal representing the unipolar voltage between the first electrode and the reference electrode, and (ii) a bipolar voltage signal representing the bipolar voltage between the first electrode and the second electrode. The output from the aforementioned function is to receive one of each of the candidate sets, To obtain by, Selecting the LAT from the aforementioned candidate set, The method according to embodiment 12, which includes calculating by (14) The above characteristics are that the marker is For those with a velocity belonging to the first range, having a first shape and a first thickness, For speeds belonging to a second range lower than the first range, having the first shape and the second thickness, For those with velocities in a third range lower than the second range, having a second shape, For this reason, the method according to Embodiment 12, which changes according to the speed. (15) The method according to Embodiment 14, wherein the marker has at least one other characteristic that changes in accordance with the LAT.

[0166] (16) The method according to embodiment 15, wherein the marker is colored according to a color scale based on the LAT. (17) A computer software product comprising a tangible, non-temporary computer-readable medium in which program instructions are stored, wherein, when the instructions are read by a processor, the processor Based on the signals acquired by multiple electrodes belonging to an internal probe on the anatomical surface of the heart, the local excitation time (LAT) at each of the electrode locations is calculated. Based on the aforementioned LAT, the respective directions and velocities of electrical propagation at the aforementioned location are calculated. While each of the electrodes is in the aforementioned position, the icon of the probe is displayed. Each marker, oriented in the direction of electrical propagation and having at least one characteristic that changes according to the speed, is placed on the portion of the icon corresponding to the electrode. Computer software products. (18) The instruction provides the processor with the LAT at the position of each first electrode among the electrodes, Obtaining multiple candidate sets of LAT for the aforementioned position, wherein for each second electrode among the electrodes adjacent to the first electrode, The input to the function is to provide (i) a unipolar voltage signal representing the unipolar voltage between the first electrode and the reference electrode, and (ii) a bipolar voltage signal representing the bipolar voltage between the first electrode and the second electrode. The output from the aforementioned function is to receive one of each of the candidate sets, To obtain by, Selecting the LAT from the aforementioned candidate set, A computer software product according to embodiment 17, which is used to perform calculations. (19) The above characteristics are that the marker is For those with a velocity belonging to the first range, having a first shape and a first thickness, For speeds belonging to a second range lower than the first range, having the first shape and the second thickness, For those with velocities in a third range lower than the second range, having a second shape, For this reason, the computer software product according to Embodiment 17 changes according to the speed. (20) The computer software product according to Embodiment 19, wherein the marker has at least one other characteristic that changes in accordance with the LAT.

Claims

1. It is a system, Electrical interface and It is a processor, The electrical interface receives the respective signals acquired by each of the multiple electrodes on the anatomical surface of the heart. Based on the respective signals, the local excitation time (LAT) at each of the multiple electrodes is calculated. Based on each of the LATs, the respective directions of electrical propagation at each of the locations are calculated. When the vector connecting an adjacent pair of electrodes among the plurality of electrodes is within a predetermined angular range with respect to the direction of electrical propagation at the position of one of the pair of electrodes, the pair of electrodes is selected as a pair of electrodes to be paired. The respective bipolar voltages measured by each electrode in the electrode pair are associated with the digital model of the anatomical surface. A processor configured in such a way, A system that includes this.

2. The processor calculates the LAT at the position of the first electrode among the plurality of electrodes. This is done by obtaining multiple candidate sets of LAT for the position of the first electrode, and by selecting one LAT from the multiple candidate sets of LAT. Obtaining multiple candidate sets of the aforementioned LAT is, Regarding the second electrode adjacent to the first electrode, The input to the function is to provide (i) a unipolar voltage signal representing the unipolar voltage between the first electrode and the reference electrode, and (ii) a bipolar voltage signal representing the bipolar voltage between the first electrode and the second electrode. The output from the aforementioned function is to receive multiple candidate sets of the aforementioned LAT, The system according to claim 1, performed by the method described above.

3. A computer software product comprising a tangible, non-temporary computer-readable medium in which program instructions are stored, wherein, when the instructions are read by a processor, the processor... The system receives the signals acquired by each of the multiple electrodes on the anatomical surface of the heart. Based on each of the aforementioned signals, the local excitation time (LAT) at each of the multiple electrodes is calculated. Based on each of the aforementioned LATs, the respective directions of electrical propagation at each of the aforementioned locations are calculated. When the vector connecting an adjacent pair of electrodes among the plurality of electrodes is within a predetermined angular range with respect to the direction of electrical propagation at the position of one of the pair of electrodes, the pair of electrodes is selected as a pair of electrodes to be paired. The bipolar voltages measured by each electrode in the pair of electrodes are associated with the digital model of the anatomical surface. Computer software products.

4. The instruction instructs the processor to calculate the LAT at the position of the first electrode among the plurality of electrodes. The process involves the processor obtaining a plurality of candidate sets of LATs for the position of the first electrode, and the processor selecting one LAT from the plurality of candidate sets of LATs. The processor acquires multiple candidate sets of the LAT, The processor, with respect to the second electrode adjacent to the first electrode, The input to the function is to provide (i) a unipolar voltage signal representing the unipolar voltage between the first electrode and the reference electrode, and (ii) a bipolar voltage signal representing the bipolar voltage between the first electrode and the second electrode. The processor receives a set of multiple candidate LATs as output from the function, This is done by The computer software product according to claim 3.

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