Mapping of atrial fibrillation using fragmentation index

Abstract

The invention is entitled "Mapping atrial fibrillation using fragmentation index." A method for mapping atrial fibrillation (AF) in a heart includes receiving an electrogram (EGM) signal acquired at a given location in the heart and exhibiting the AF. Two or more primary peaks are identified in the EGM signal, and a cycle length (CL) is calculated between adjacent primary peaks. One or more secondary peaks are identified in the EGM signal within the CL. A local fragmentation index (FI) is calculated that indicates a number of the secondary peaks per CL. The local FI is visualized on a map of at least a portion of the heart.

Description

Technical Field

[0001] This invention relates generally to electrophysiological mapping, and more specifically to methods and systems for mapping atrial fibrillation using the fragmentation index. Background Technology

[0002] Atrial fibrillation is an irregular heart rhythm that occurs in the atria. Various techniques for mapping atrial fibrillation are known in the art.

[0003] For example, U.S. Patent Application Publication 2017 / 0367601 describes a method for identifying cardiac regions in patients who may be involved in the continuation of atrial fibrillation. This method considers a reference cycle of the arrhythmia and has two variants: a local variant in which each cardiac region is analyzed individually, and a regional variant in which several cardiac regions are analyzed together.

[0004] U.S. Patent Application Publication 2015 / 0359430 describes a method for medical image processing of images of body structures, the method comprising: receiving anatomical data to reconstruct an anatomical image of a patient's body region, said region including a portion of at least one internal body part adjacent to or spaced from target tissue; receiving functional data from a functional imaging modality that images at least said portion of said body region of the patient; processing the anatomical image to generate at least one image mask corresponding to the region outside said wall of said at least one internal body part; associating said at least one generated image mask with said functional data to guide the reconstruction of a functional image depicting said target tissue; and providing the reconstructed functional image.

[0005] U.S. Patent Application Publication 2012 / 0078129 describes a method for displaying an image showing the location of one or more low-voltage structures in a tissue. The method includes receiving electrical mapping data corresponding to a portion of the tissue, and generating an image using the electrical mapping data. Electrical mapping values ​​having two endpoints defining an upper and lower limit of at least one voltage range are distinguishable from electrical mapping values ​​outside at least one voltage range. The two endpoints are selected to distinguish one or more low-voltage structures in the tissue from other parts of the tissue. Summary of the Invention

[0006] The embodiments of the invention described herein provide a method for mapping atrial fibrillation (AF) in the heart, the method comprising receiving an electrogram (EGM) signal acquired at a given location in the heart and representing the AF. Two or more primary peaks are identified in the EGM signal, and a cycle length (CL) is calculated between adjacent primary peaks. One or more secondary peaks are identified in the EGM signal within the CL. A local fragmentation index (FI) indicating the number of said secondary peaks per CL is calculated. The local FI is visualized on a mapping of at least a portion of the heart.

[0007] In some embodiments, the heart has a region comprising (i) a given location at a given distance from a predefined location of the region, and (ii) at least an additional location having an additional FI and located at an additional distance from the predefined location, and the method comprising calculating and visualizing the regional FI of the region based on the given distance and the additional distance, and based on the local FI and the additional FI. In other embodiments, the predefined location comprises the geometric centroid (COG) of the region, and calculating and visualizing the regional FI comprises calculating a weighted average of at least the local FI and the additional FI based on the given distance and the additional distance. In other embodiments, identifying the one or more secondary peaks comprises merging two or more adjacent secondary peaks based on a predefined threshold.

[0008] In one embodiment, the method includes defining a window of interest (WOI) within the CL and identifying the one or more secondary peaks within the WOI. In another embodiment, the EGM signal includes a plurality of CLs, and calculating the local FI includes calculating an average CL based on the plurality of CLs and calculating the average number of the secondary peaks for each average CL.

[0009] According to an embodiment of the invention, a system for mapping atrial fibrillation (AF) in the heart is also provided, the system comprising a processor and a display. The processor is configured to: (a) receive an electrogram (EGM) signal exhibiting AF acquired at a given location in the heart, (b) identify two or more primary peaks in the EGM signal and calculate the cycle length (CL) between adjacent primary peaks, (c) identify one or more secondary peaks within the duration of the CL in the EGM signal, and (d) calculate a local fragmentation index (FI) indicating the number of said secondary peaks per CL. The display is configured to display the local FI on a mapping of at least a portion of the heart. Attached Figure Description

[0010] The invention will be more fully understood through the following detailed description of embodiments thereof, taken in conjunction with the accompanying drawings, wherein:

[0011] Figure 1 A schematic diagram of a system for annotating electrogram (EGM) signals according to an embodiment of the present invention;

[0012] Figure 2 A diagram illustrating the identification of activation points in an electrogram signal according to an embodiment of the present invention;

[0013] Figure 3 A flowchart illustrating a method for mapping atrial fibrillation (AF) in a patient's heart is shown in the form of an embodiment of the present invention.

[0014] Figure 4 This is a schematic illustration of a regional mapping map of the heart with a regional fragmentation index (FI) calculated based on multiple local FIs according to an embodiment of the present invention; and

[0015] Figure 5 A schematic diagram of a region of a patient's heart according to an embodiment of the present invention. Detailed Implementation

[0016] Overview

[0017] Some medical procedures are based on measuring electrogrammography (EGM) signals by placing multiple electrodes at different corresponding sites on the heart tissue. In some cardiac procedures, physicians use EGM signals to characterize the propagation of an electrically activated wavefront through the patient's heart tissue during cardiac circulation. For each EGM signal, the physician may attempt to identify an electrically activated point corresponding to an instance of the wavefront passing through the site where the signal was acquired.

[0018] In cases of atrial fibrillation (AF) or other arrhythmias, such activation points can be difficult to identify, even for experienced physicians, due to the irregularity and / or variability of the EGM signal. Regular EGMs typically contain a sharp, regularly spaced main peak that clearly indicates electrical activation. In contrast, irregular EGMs can exhibit a variety of different forms and may include multiple secondary peak bursts that do not typically indicate regular electrical activation.

[0019] The embodiments of the present invention described below provide methods and systems for mapping atrial fibrillation (AF) by calculating local and regional fragmentation indices (FI) and visualizing FI on a mapping map of a patient's heart. In some embodiments, the system for mapping AF in a patient's heart includes a processor and a display.

[0020] In some implementations, the processor is configured to receive an EGM signal from a catheter inserted into the patient's heart, the EGM signal exhibiting atrial fibrillation (AF) and acquired at a given location within the heart. The processor is further configured to identify two or more major activation peaks (also referred to herein as annotations) in the EGM signal and to calculate the cycle length (CL) between adjacent annotations. The processor is further configured to maintain a predefined criterion for the atrial fibrillation cycle length (AFCL) used to identify the rule, such as, but not limited to, a cycle length between 120 ms (referred to herein as short AFCL) and 250 ms (referred to herein as long AFCL) with a standard deviation of less than 30 ms.

[0021] In some implementations, the processor is configured to define a window of interest (WOI) within the AFCL and identify one or more secondary activation peaks, also referred to herein as fragmentation peaks. The processor computes a local fragmentation index (FI) at a given location, which indicates the average number of fragmentation peaks per WOI.

[0022] In some embodiments, the processor is configured to merge two or more adjacent fragmentation peaks based on predefined thresholds and criteria. In some embodiments, the EGM signal includes multiple AFCLs, and the processor is configured to calculate an average AFCL based on the multiple AFCLs and to calculate the average number of fragmentation peaks per average AFCL. In some embodiments, the display is configured to display the calculated and visualized local FI on a mapping of at least a portion of the heart.

[0023] In some implementations, a region of the patient's heart contains a given location at a given distance from the geometric centroid (COG) of that region. The region also includes multiple locations at corresponding distances from the COG. In such implementations, the processor is configured to acquire additional EGM signals at the additional corresponding locations using a catheter, and to calculate a corresponding additional FI for each of the additional locations.

[0024] In some embodiments, the processor is configured to calculate a regional FI based on a given distance and an additional distance, and based on the local FI and the additional FI. The processor is further configured to output a visualization of the regional FI and AFCL to a display. The display is configured to show the calculated and visualized regional FI on a cardiac mapping map showing at least the aforementioned regions, and to show short and long AFCLs superimposed on the regional FI. In such embodiments, the processor is configured to display important regions on the cardiac mapping map, such as regions with short AFCLs and large regional FIs.

[0025] The disclosed technology provides physicians with the ability to detect and characterize areas suspected of having irregular activation. Physicians can then apply ablation to one or more of these suspected areas to reduce cardiac arrhythmias in the patient's heart.

[0026] System Description

[0027] Figure 1 This is a schematic illustration of a system 21 for annotating an electrogram (EGM) signal 22 according to an embodiment of the present invention. Figure 1 As shown, during an electrophysiological (EP) procedure, physician 27 inserts catheter 29 and navigates the distal end 31 of catheter 29 to the desired location in the heart 23 of patient 25.

[0028] In some implementations, as the physician 27 moves the distal end 31 of the catheter 29 along the epicardial inner surface (also referred to herein as tissue) of the heart 23, one or more electrodes (not shown) positioned at the distal end 31 of the catheter 29 in contact with the heart tissue sense an EGM signal 22 generated by the tissue. Such a signal can be sensed, for example, when the heart 23 experiences atrial fibrillation (AF) or any other arrhythmia. It should be noted that in some cases, the arrhythmia may be induced by the physician as part of a protocol.

[0029] In some implementations, the processor 28 of system 21 receives EGM signals 22 from the remote end 31 via an electrical interface 35 (such as a socket or port) and processes these EGM signals, as will be described below. Figure 2 and Figure 3 As detailed in the document. In some implementations, in response to processing the EGM signal, processor 28 is configured to generate output, which typically includes visual output displayed on display 26 of system 21.

[0030] In some embodiments, processor 28 is configured to annotate at least one peak of EGM signal 22 to indicate the activation point of EGM signal, and the annotated signal is displayed on display 26. When annotating the signal, processor 28 may, for example, place a marker 24 on each activation point. In the context of the invention and the claims, the annotated signal indicated by marker 24 is also referred to herein as a “main peak” or “annotated activation signal”.

[0031] In some embodiments, the electrodes at the distal end 31 may be arranged in any suitable configuration, such as circular, linear, or multi-spline configurations. Typically, each EGM signal 22 is a bipolar signal, such that the signal represents the voltage between the corresponding electrode pair at the distal end 31. In an alternative embodiment, at least one of the acquired EGM signals may be a unipolar signal, such that the signal represents the voltage between one of the electrodes and a reference electrode externally coupled to the patient 25.

[0032] In some embodiments, processor 28 may include a single processor or a collaboratively networked or clustered group of processors. In some embodiments, as described herein, the functionality of processor 28 may be implemented solely in hardware, for example using one or more application-specific integrated circuits (ASICs) or field-programmable gate arrays (FPGAs). In other embodiments, the functionality of processor 28 may be implemented at least partially in software. For example, in some embodiments, processor 28 may include a programmable digital computing device including at least a central processing unit (CPU) and random access memory (RAM). In some embodiments, system 21 may include any suitable type of non-volatile memory device.

[0033] In other embodiments, processor 28 may include a general-purpose processor that is software-programmed to implement the functions described herein. For example, the software may be downloaded to the processor electronically via a network, or alternatively or otherwise, the software may be provided and / or stored on a non-transitory tangible medium such as magnetic, optical, or electronic memory.

[0034] This particular configuration of system 21 is illustrated by way of example to demonstrate certain problems solved by embodiments of the invention and to show the application of these embodiments in enhancing the performance of such systems. However, embodiments of the invention are by no means limited to this particular category of exemplary systems, and the principles described herein can be similarly applied to other categories of systems for mapping arrhythmias and other categories of systems for annotating and analyzing any suitable signals obtained from any human organ.

[0035] The local fragmentation index and cycle length are calculated based on the identified primary and secondary peaks.

[0036] Figure 2 A diagram illustrating the identification of activation points in an EGM signal 100 according to an embodiment of the present invention. The EGM signal 100 may be replaced, for example, as described above. Figure 1 EGM signal 22.

[0037] In some embodiments, the EGM signal 100 is acquired at a given location in the heart 23 using one or more electrodes at the distal end 31 and includes a complex, fragmented EGM signal measured in millivolts (mV) over time. In some embodiments, the processor 28 is configured to divide the total period spanned by the signal into consecutive smaller time intervals or any other suitable time intervals, each having a predetermined length or within a range of lengths, for example, between 100 milliseconds (ms) and 200 ms.

[0038] In some implementations, processor 28 is configured to select a set of candidate activation points, for each time period, said candidate activation points including the maximum value point (or "peak") within said time period, provided that the maximum value is greater than a threshold of said time period and also greater than Figure 2 The noise threshold is indicated by an upper noise threshold line 103 and a lower noise threshold line 105 (e.g., 0.05 mV). Note that line 103 is 0.05 mV above the center line of the EGM signal 100, and line 105 is 0.05 mV below the center line of the EGM signal 100. The processor 28 is then configured to remove any pair of candidate points from this set of candidate activation points that are within a predefined time interval (e.g., 80 ms) of each other. The remaining points in the set are then assumed to be the annotated activation signals, in this example, the main peaks 101, 111, and 121.

[0039] In some implementations, processor 28 is configured to compute adjacent annotation activation signals (in Figure 2 In the example, the atrial fibrillation cycle length (AFCL) is the AFCL between the main peaks 101 and 111 (102). In some embodiments, the processor 28 may maintain two thresholds for defining the AFCL range that is considered to be AF regular. For example, the EGM signal 100 may be acquired over a total period of 2500 ms and has seventeen (17) candidate main peaks, and therefore sixteen (16) calculated AFCLs. Each adjacent main peak may define an AFCL between 120 ms (referred to herein as short AFCL) and 250 ms (referred to herein as long AFCL), and has a standard deviation (SD) of less than 30 ms relative to the calculated 16 AFCLs of the EGM signal 100.

[0040] In some implementations, processor 28 is configured to calculate the average AFCL based on the calculated AFCL, and maintain two thresholds for the average AFCL and a threshold for the SD. According to the example above, processor 28 may maintain thresholds of 120ms and 250ms for the lower and upper limits of the average AFCL, respectively, and an additional threshold of 30ms for the AFCL SD.

[0041] In some implementations, processor 28 is configured to define a window of interest (WOI) 104 with a width less than AFCL and / or for a duration of width 102. Figure 2 In the example, the center of WOI 104 is aligned with the corresponding times of the main peaks 101, 111, and 121, and extends along the time axis by + / - 40% of the AFCL length. In other words, WOI 104 has 80% of the total length of AFCL 102. In other embodiments, processor 28 may maintain any other suitable threshold besides 80% to define the length (or time interval) of WOI 104 relative to AFCL 102.

[0042] In some implementations, processor 28 is configured to identify a set of candidate fragmentation peaks, also referred to herein as secondary peaks, within WOI 104. Processor 28 is further configured to filter out some of the candidate peaks using predefined thresholds and criteria, and obtain the final set of fragmentation peaks.

[0043] exist Figure 2 In the example, processor 28 identifies candidate peaks 106, 106A, 106B, 106C, 107, 108A, and 108B. In one embodiment, processor 28 filters out candidate peak 107, which is below a noise threshold of 0.05 mV (i.e., between lines 103 and 105). Note that processor 28 does not filter out peak 106C, which has an absolute value slightly larger than the noise threshold. In one embodiment, processor 28 checks whether all peaks greater than 0.05 mV (i.e., above line 103) trend upward over time before the peak and downward over time after the peak. Similarly, processor 28 checks whether all peaks less than -0.05 mV (i.e., below line 105) trend downward over time before the peak and upward over time after the peak.

[0044] In some implementations, processor 28 is configured to remove at least one set of candidate peaks from the set of candidate peaks that are within a predefined time interval (e.g., 20 ms) and have the same sign (positive or negative), and to merge one or more peaks removed from the set into the largest peak. Figure 2 In the example, peaks 106A and 108A are within a predefined time interval of 20ms, and peak 108A is greater than peak 106A. Therefore, processor 28 removes peak 106A, or in other words, merges peak 106A into peak 108A. Similarly, peaks 106B and 108B are within a predetermined time interval of 20ms, and peak 108B has an absolute electrode potential value greater than that of peak 106B. Therefore, processor 28 merges peak 106B into peak 108B. Subsequently, processor 28 generates a final list of subpeaks. Figure 2 In the example, the final list includes peaks 106, 106C, 108A, and 108B selected based on the thresholds and criteria described above. It should be noted that the predefined time intervals of 80ms and 20ms for the main and secondary peaks, the predefined time intervals between 120ms and 250ms for AFCL, and the predefined noise threshold of + / -0.05mV are all provided by way of example. In other embodiments, the processor 28 may maintain any other suitable one or more thresholds for any of the above time intervals and / or noise thresholds.

[0045] In some implementations, processor 28 is configured to calculate a local fragmentation index (FI) for an EGM signal 100 acquired at a given location on heart 23, the local fragmentation index indicating the average number of subpeaks per AFCL.

[0046] In some implementations, processor 28 is configured to calculate local FI using the formula (1) given below:

[0047]

[0048] in:

[0049] SP represents the cumulative number of secondary peaks counted within the actual WOI.

[0050] PP is the cumulative number of main peaks counted within the actual WOI.

[0051] AWOI is the cumulative actual duration of all actual WOIs within the EGM signal 100.

[0052] NWOI is the cumulative nominal duration of all nominal WOIs within the EGM signal 100.

[0053] For example, the total duration of EGM signal 100 is 2500 ms, and the cumulative nominal duration of all WOIs (NWOIs) is 80% of the total time, thus having a value of 2000 ms. The cumulative actual duration of all WOIs (AWOIs) within EGM signal 100 is 1984 ms. During the AWOI period, after applying the above threshold and filtering criteria, 17 main peaks and 40 secondary peaks remain.

[0054] In this example, processor 28 calculates the local FI using formula (1) and outputs the local FI as shown in formula (2):

[0055]

[0056] In some implementations, processor 28 is configured to output a local FI and a calculated average AFCL to display 26 on a mapping of at least a portion of the heart 23. Note that the local FI indicates the average number of fragmentation peaks per AFCL between two annotated activation signals. Figure 2 The main peaks 101, 111, and 121 are shown in the diagram. It should be noted that some segments of the EGM signal 100 may include signals within the noise threshold, such as the segment between WOI 104 of the main peaks 101 and 121, which has electrode potential values ​​between lines 103 and 105. In some embodiments, the processor 28 may exclude such segments from the calculation of AFCL and local FI using any suitable criteria and / or predefined or learned parameters.

[0057] In other embodiments, processor 28 is configured to exclude WOIs and identify a set of candidate secondary peaks along the entire AFCL between adjacent primary peaks. In such embodiments, the calculated local FI may consist only of the total number of identified secondary peaks divided by the total number of primary peaks.

[0058] A method for mapping atrial fibrillation by calculating the local fragmentation index

[0059] Figure 3 A flowchart illustrating a method for mapping atrial fibrillation (AF) in a heart 23 is shown schematically according to an embodiment of the present invention. The method begins with an EGM signal acquisition step 200, in which an EGM signal 100 is acquired using the distal end 31 of one or more electrodes positioned at a given location in the tissue of the heart 23. In some embodiments, a processor 28 receives the EGM signal 100 exhibiting AF and identifies annotated activation signals, such as peaks 101, 111, and 121, within the EGM signal 100.

[0060] At loop length calculation step 202, processor 28 calculates the AFCL between any pair of adjacent main peaks. In some embodiments, processor 28 calculates the average AFCL of the calculated AFCLs.

[0061] At WOI definition step 204, processor 28 defines a WOI, such as WOI 104, which has a length typically a fraction of the calculated AFCL (e.g., 70% or 80% of the AFCL length). In some embodiments, processor 28 calculates the WOI based on the aforementioned average AFCL length. In other embodiments, processor 28 uses a predefined fraction of the AFCL length of the corresponding segment to calculate the WOI for each segment of the EGM signal 100. Alternatively or additionally, processor 28 may calculate the average length of two adjacent AFCLs to define the WOI located between the two corresponding AFCLs, or use any other suitable method to define the WOI.

[0062] At fragmentation peak identification step 206, processor 28 identifies a set of candidate fragmentation peaks, such as those mentioned above. Figure 2 Peaks 106, 106A, 106B, 106C, 107, 108A, and 108B are shown. In some implementations, processor 28 may filter out at least some of the candidate fragmentation peaks, such as those located above. Figure 2 Peak 107 is shown within the noise threshold between noise threshold lines 103 and 105.

[0063] At the fragmentation peak merging step 208, the processor identifies a group of peaks comprising two or more sub-peaks that are within a predefined time interval (e.g., 20 ms) of each other and have the same sign (positive or negative). For example, one group of peaks 106A and 108A, and another group of peaks 106B and 108B. In some embodiments, the processor 28 may select the peak with the largest absolute electrode potential value within this group of sub-peaks and remove other peaks from this group. As described above. Figure 2 As depicted, peaks 108A and 108B have the maximum absolute electrode potential values, and therefore, the corresponding peaks 106A and 106B have been removed.

[0064] At step 210 of the Local Fragmentation Index (FI) calculation, the processor uses the above... Figure 2 The local FI is calculated using formula (1) as described above. Processor 28 is applied to formula (1) to determine the total number of primary and secondary peaks identified and verified after the filtering and merging processes described in steps 206 and 208 above, as well as the total actual duration and nominal WOI duration used to identify the primary and secondary peaks. In some embodiments, the calculated local FI indicates the average number of secondary peaks per AFCL duration. In the example shown in formula (2) above, the calculated value of the local FI is equal to 2.334, which indicates the average number of secondary peaks per average AFCL duration.

[0065] In other implementations, processor 28 may apply any other suitable formula to calculate the local FI. For example, the median of all AFCLs calculated within the EGM signal 100 may be used.

[0066] At mapping display step 212, the processor is configured to output a visualization mapping of at least a portion of the heart 23 to a display 26, the visualization mapping including at least a given point for acquiring the EGM signal 100 and the calculated FI and one or more AFCLs.

[0067] In some embodiments, display 26 displays localized fissures (FIs) on a mapping of the heart 23 received from processor 28. In some embodiments, display 26 is configured to use color coding or any other suitable visualization technique to display a visualization of the localized FIs. For example, processor 28 and / or display 26 may assign warm colors (e.g., red) to small values ​​of FIs and cold colors (e.g., blue) to large values ​​of FIs. As described above. Figure 2 As described above, display 26 may also display one or more AFCLs superimposed on the cardiac mapping and the displayed local FI. An exemplary embodiment of the mapping is shown below. Figure 5 middle.

[0068] Calculate and visualize the regional fragmentation index

[0069] Figure 4 This is a schematic diagram of region 300 of the heart 23 according to an embodiment of the present invention, which calculates the fragmentation index (FI) of a region based on multiple local FIs. In some embodiments, region 300 includes points 302, 304, 306, 308, and 310 located at corresponding distances of 3 mm, 1 mm, 2 mm, 4 mm, and 5 mm from the geometric centroid (COG) (referred to as COG 333 of region 300).

[0070] In some implementations, the distal end 31 of catheter 29 acquires one or more EGM signals from each of points 302, 304, 306, 308, and 310, and processor 28 uses, for example, the above-described... Figure 2 and Figure 3 The disclosed technique is used to calculate the local FI for each point in a region of 300. Figure 4 In the example, the local FI values ​​calculated for points 302, 304, 306, and 308 are 10, 5, 7, and 10, respectively.

[0071] In some implementations, processor 28 is configured to calculate the regional FI of region 300 by using a weighted average of the local FIs of points 302, 304, 306, 308, and 310 at corresponding distances from COG 333, in order to derive the average weight. For a given point, the corresponding local FI is weighted by a weight value that is proportional to the inverse of the distance between the given point and COG 333. For example, point 304, located 1 mm from COG 333, has a larger weight (e.g., 1) than point 310, located 5 mm from COG 333, and therefore has a smaller weight (e.g., 1 / 5).

[0072] In some implementations, processor 28 calculates a weighted average by computing a weighted sum and by using weights and a normalized weighted sum. Based on the exemplary values ​​provided above, the weights for points 302, 304, 306, 308, and 310 are 0.33, 1, 0.5, 0.25, and 0.2, respectively, and the normalization factor is the sum of weights of 2.283.

[0073] In some implementations, processor 28 calculates the normalized weights for points 302, 304, 306, 308, and 310, and outputs corresponding values ​​of 0.146, 0.438, 0.219, 0.109, and 0.087, and then multiplies the corresponding local FI by the corresponding normalized weight for each point and sums them up.

[0074] Based on the above exemplary values, the processor 28 multiplies the local FI of points 302, 304, 306, 308 and 310 by the corresponding normalized weight for each point, and outputs the corresponding values ​​of the weighted local FI: 1.46, 2.19, 1.53, 1.09 and 0.35.

[0075] In some implementations, after calculating the sum of the weighted local FIs, processor 28 outputs the region FI with a value of 6.628. Subsequently, processor 28 outputs the calculated region FI and the calculated AFCL of region 300 to display 26.

[0076] Regional fragmentation index and circulation length on visualized cardiac mapping

[0077] Figure 5 This is a schematic diagram of a mapping map 400 of region 410 of the heart 23 according to an embodiment of the present invention. In some embodiments, the mapping map 400 includes a location 420 identified by the processor 28 and displayed by the display 26 as a location with normal AF activity.

[0078] In some implementations, processor 28 analyzes, for example, multiple EGM signals 100 acquired by the distal end 31 at a corresponding location 420 in region 410. Figure 2 and Figure 3 As described above, processor 28 identifies the main peak, calculates the CL, and identifies the AFCL with a CL duration between 120ms and 250ms within the CL. Figure 2 and Figure 3 As further described, processor 28 defines a Word of Interest (WOI) and identifies fragmentation peaks within the corresponding WOI. Based on the identified and verified fragmentation peaks (after filtering out candidate fragmentation peaks that do not meet predefined thresholds and / or criteria), processor 28 calculates the local FI for each location 420, such as... Figure 2 and Figure 3 As described in [the text].

[0079] Subsequently, processor 28 uses the above... Figure 4 The technique described herein calculates region FI for each segment of region 410 and outputs a visual mapping map to display 26, which includes the calculated AFCL and / or one or more region FIs. In some embodiments, display 26 is configured to display the output mapping map using any suitable type of display, such as a gradient mapping map using color encoding.

[0080] exist Figure 5 In the example, the mapping map 400 of region 410 displayed on display 26 includes multiple segments with corresponding contours 402, 403, 404, 405, 406, and 408, each of which has a calculated region FI. In some embodiments, the mapping map 400 includes a legend 430 that provides any suitable type of encoding for visualizing the region FI values. For example, contours 403, 404, and 405 have “large” region FI values, contours 402 and 408 have medium region FI values, and contour 406 has a “small” region FI value. In this example, the term “large region FI value” refers to a typical value between 3 and 7, and the term “small region FI value” refers to a typical value less than 7.

[0081] In some implementations, the mapping diagram 400 also includes AFCL markers 422, 423, 424, 425, 426, and 428, which are processed by the processor 28 (e.g., using the above). Figure 2 The technique described herein is calculated in segments represented by corresponding contours 402, 403, 404, 405, 406, and 408. In one embodiment, the mapping map 400 includes a legend 440 that provides another set of codes for the visual value of the average AFCL marker. In this exemplary embodiment, 120ms represents a “short” AFCL value, and 250ms represents a “long” AFCL value.

[0082] In some implementations, local FI indicates the average number of fragmented peaks (also referred to herein as secondary peaks) per regular AFCL. Furthermore, short regular AFCLs indicate the short duration between two annotated activation signals (also referred herein as primary peaks). In some cases, a combination of large regional FI values, such as those shown in contour 403 and AFCL marker 423 respectively, and one or more short regular AFCLs, can indicate a segment with high AF activity in region 410.

[0083] In some implementations, the processor 28 may highlight a combination of, for example, high-area FI and short-regular AFCL on the display 26, in order to, for example, as described above Figure 1The EP procedure shown attracts the attention of physician 27. In such embodiments, processor 28 may maintain any set of thresholds for regional FI and routine AFCL, and for any combination thereof, to alert to any prominent AF activity identified in a specific segment or region of the heart 23.

[0084] In an alternative implementation, processor 28 may add any other suitable type of markers to be visualized on mapping map 400, or may reduce... Figure 5 At least one of AFCL or region FI shown on the mapping diagram 400.

[0085] This particular configuration of mapping figure 400 is illustrated by way of example to demonstrate certain problems addressed by embodiments of the invention and to show the application of these embodiments in enhancing the performance of systems (such as system 21) for analyzing arrhythmias. However, embodiments of the invention are by no means limited to this particular category of exemplary mapping and visualization configurations, and the principles described herein can be similarly applied to other categories of visualization in any medical system.

[0086] While the embodiments described herein primarily address arrhythmias, and particularly atrial fibrillation, the methods and systems described herein can also be used in other applications, such as in persistent atrial fibrillation or any other type of arrhythmia in the human heart.

[0087] Therefore, it should be understood that the embodiments described above are cited by way of example, and the invention is not limited to the specific contents shown and described above. Rather, the scope of the invention includes combinations and sub-combinations of the various features described above, as well as variations and modifications thereof, which will occur to those skilled in the art upon reading the above description and which are not disclosed in the prior art. Documents incorporated herein by reference are considered an integral part of this application, except that if any terminology defined in such incorporated documents conflicts with the definitions expressly or implicitly given in this specification, only the definitions in this specification shall be considered.

Claims

1. A method for mapping atrial fibrillation, AF, in a heart, the method comprising: receiving an electrogram, EGM, signal acquired at a given location in the heart that exhibits the AF; identifying two or more main activation peaks in the EGM signal and computing a cycle length, CL, between adjacent main activation peaks; identifying one or more fractionation peaks within the CL in the EGM signal; computing a local fractionation index, FI, indicative of a number of the fractionation peaks per CL; and visualizing the local fractionation index, FI, on a map of at least a portion of the heart, wherein identifying the one or more fractionation peaks comprises merging two or more adjacent fractionation peaks based on a predefined threshold.

2. The method of claim 1, wherein the heart has a region that includes (i) the given location located at a predefined location of the region a given distance away, and (ii) at least an additional location having an additional fractionation index, FI, and located at an additional distance away from the predefined location, and comprising computing and visualizing a regional fractionation index, FI, of the region based on the given distance and the additional distance, and based on the local fractionation index, FI, and the additional fractionation index, FI.

3. The method of claim 2, wherein the predefined location comprises a geometric center of gravity, COG, of the region, and wherein computing and visualizing the regional fractionation index, FI, comprises computing a weighted average of at least the local fractionation index, FI, and the additional fractionation index, FI, based on the given distance and the additional distance.

4. The method of claim 1, and comprising defining a window of interest, WOI, within the CL, and identifying the one or more fractionation peaks within the WOI.

5. The method of claim 1, wherein the EGM signal comprises a plurality of CLs, and wherein computing the local fractionation index, FI, comprises computing an average CL based on the plurality of CLs, and computing an average number of the fractionation peaks per the average CL.

6. A system for mapping atrial fibrillation, AF, in a heart, the system comprising: a processor configured to: (a) receive an electrogram, EGM, signal acquired at a given location in the heart that exhibits the AF, (b) identify two or more main activation peaks in the EGM signal and compute a cycle length, CL, between adjacent main activation peaks, (c) identify one or more fractionation peaks within a duration of the CL in the EGM signal, and (d) compute a local fractionation index, FI, indicative of a number of the fractionation peaks per CL; and a display configured to display the local fractionation index, FI, on a map of at least a portion of the heart, wherein the processor is configured to merge two or more adjacent fractionation peaks based on a predefined threshold. ​ 7. The system of claim 6, wherein the heart has a region that includes (i) the given location that is located at a given distance from a predefined location of the region, and (ii) at least an additional location that has an additional fragmentation index FI and is located at an additional distance from the predefined location, and wherein the processor is configured to compute and visualize a regional fragmentation index FI of the region based on the given distance and the additional distance, and based on the local fragmentation index FI and the additional fragmentation index FI.

8. The system of claim 7, wherein the predefined location comprises a geometric center of gravity COG of the region, and wherein the processor is configured to compute and visualize the regional fragmentation index FI by computing a weighted average of at least the local fragmentation index FI and the additional fragmentation index FI based on the given distance and the additional distance.

9. The system of claim 6, wherein the processor is configured to define a window of interest WOI within a duration of the CL, and to identify the one or more fragmentation peaks within the WOI.

10. The system of claim 6, wherein the EGM signal comprises a plurality of CLs, and wherein the processor is configured to compute an average CL based on the plurality of CLs, and to compute an average number of the fragmentation peaks per the average CL.

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