Patent Description:
Atrial fibrillation is an irregular heart rhythm generated in the atria. Various techniques for mapping atrial fibrillation are known in the art.

For example, <CIT> describes a method for identifying areas of the heart of a patient able to be involved in the perpetuation of atrial fibrillation. This method takes into account the reference cycle of the arrhythmia and has two variants: a local variant in which the areas of the heart are each analyzed separately and a regional variant in which several areas of the heart are analyzed together.

<CIT> describes a method of medical image processing for images of body structures, the method comprising: receiving anatomical data to reconstruct an anatomical image of a region of a body of a patient, said region comprises a portion of at least one internal body part which borders or is spaced apart from a target tissue; receiving functional data from a functional imaging modality which images at least said portion of the region of the body of the patient; processing said anatomical image to generate at least one image mask corresponding to the zone outside of the wall of said at least one internal body part; correlating the at least one generated image mask with the functional data for guiding a reconstruction of a functional image depicting said target tissue; and providing the reconstructed functional image.

<CIT> describes a method of displaying an image of the location of one or more low voltage structures in 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 within at least one voltage range having two endpoints that bound the upper and lower limits of the voltage range are distinguishable from electrical mapping values outside the at least one voltage range. The two endpoints are selected to distinguish the one or more low voltage structures of the tissue from other portions of the tissue.

<CIT> discloses a monitoring device for monitoring and evaluating electrogram signals representing electric activities of a heart chamber, said monitoring device comprising a signal input operatively connected to a mapping catheter and a signal processing and evaluation unit for processing and evaluating electrogram signals received at the signal input. The morphology of a detection complex is characterised by the evaluation device, including on the basis of the number of local peaks in the deflection complex.

<CIT> discloses a method for visualization of electrical activity on a surface of an organ over a time period, including morphological analysis of complex fractionated electrograms.

An embodiment of the present invention that is described herein provides a computer-implemented method for visualizing a map of atrial fibrillation (AF) in a heart, as specified in appended claim <NUM>. In some embodiments, the heart has a region that includes (i) the given position located at a given distance from a predefined location of the region, and (ii) at least an additional position having an additional FI and located at an additional distance from the predefined location, and the method includes calculating and visualizing, based on the given distance and the additional distance, and based on the local FI and the additional FI, a regional FI of the region. In other embodiments, the predefined location includes a geometrical center of gravity (COG) of the region, and calculating and visualizing the regional FI includes calculating, based on the given distance and the additional distance, a weighted average of at least the local FI and the additional FI.

In an 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 multiple CLs, and calculating the local FI includes calculating, based on the multiple CLs, an average CL, and calculating an average number of the secondary peaks per the average CL.

There is additionally provided, a system for mapping atrial fibrillation (AF) in a heart, as defined in appended claim <NUM>.

Some medical procedures are based on measuring electrogram (EGM) signals by disposing a plurality of electrodes at different respective sites on the cardiac tissue. In some cardiac procedures, a physician may use the EGM signals to characterize the propagation of the wavefront of electrical activation through the cardiac tissue of a patient during a cardiac cycle. For each of the EGM signals, the physician may attempt to identify electrical activation points that correspond to an instance at which the wavefront passed through the site at which the signal was acquired.

In cases of heart atrial fibrillation (AF) or other arrhythmias, it may be difficult, even for an experienced physician, to identify such activation points, due to irregularity and/or variability of the EGM signals. Regular electrograms typically contain regularly-spaced and sharp primary peaks that clearly indicate electrical activations. In contrast, irregular EGMs may exhibit a wide variety of different forms, and may comprise numerous bursts of secondary peaks that are typically not indicative of regular electrical activation.

Embodiments of the present invention that are described hereinbelow provide methods and systems for mapping AF by calculating local and regional fragmentation indexes (FIs), and visualizing the FIs on a map of a patient heart. In embodiments, a system for mapping AF in a patient heart comprises a processor and a display.

In some embodiments, the processor is configured to receive, from a catheter inserted into a patient heart, an EGM signal that exhibits the AF and acquired at a given position in the heart. The processor is further configured to identify, in the EGM signal, two or more primary activation peaks, also referred to herein as annotations, and to calculate a cycle length (CL) between adjacent annotations. The processor is further configured to hold predefined criteria for identifying a regular atrial fibrillation cycle length (AFCL), such as but not limited to, a cycle length between <NUM> (referred to herein as short AFCLs) and <NUM> (referred to herein as long AFCLs) having a standard deviation smaller than <NUM>.

The processor is configured to define a window of interest (WOI) within the AFCL, and to identify within the WOI, one or more secondary activation peaks, also referred to herein as fragmentation peaks. The processor calculates at the given position, a local fragmentation index (FI) that is indicative of an average number of the fragmentation peaks per WOI.

The processor is configured to merge, based on a predefined threshold and criteria, two or more adjacent fragmentation peaks. In some embodiments, the EGM signal comprises multiple AFCLs, and the processor is configured to calculate, based on the multiple AFCLs, an average AFCL, and to calculate an average number of the fragmentation peaks per the average AFCL. In some embodiments, the display is configured to display the calculated and visualized local FI on a map of at least part of the heart.

In some embodiments, a region of patient heart contains the given position, which is located at a given distance from a geometrical center-of-gravity (COG) of the region. The region additionally comprises multiple positions located at respective distances from the COG. In such embodiments, the processor is configured to acquire, using the catheter, additional EGM signals at the additional respective positions, and to calculate, for each of the additional positions, a respective additional FI.

In some embodiments, the processor is configured to calculate, based on the given distance and the additional distances, and based on the local FI and the additional FIs, a regional FI of the region. The processor is further configured to output, to the display, a visualization of the regional FI and the AFCLs. The display is configured to display the calculated and visualized regional FI on a map of the heart showing at least the aforementioned region, and to display the short and the long AFCLs, overlaid on the regional FIs. In such embodiments, the processor is configured to display, on the heart map, important regions, such as regions having short AFCLs and large regional FI.

The disclosed techniques provide the physician with the features of detection and display of regions suspected to have irregular activation. The physician may apply ablation in one or more of the suspected regions, in order to reduce the arrhythmia in the patient heart.

<FIG> is a schematic, pictorial illustration of a system <NUM> for annotating an electrogram (EGM) signal <NUM>. As shown in <FIG>, during an electrophysiological (EP) procedure, a physician <NUM> inserts a catheter <NUM> and navigates a distal end <NUM> of catheter <NUM> into a desired location in a heart <NUM> of a patient <NUM>.

When physician <NUM> moves distal end <NUM> of catheter <NUM> along the inner epicardial surface, also referred to herein as tissue, of heart <NUM>, one or more electrodes (not shown) disposed at distal end <NUM> of catheter <NUM>, which are in contact with tissue of the heart, sense EGM signals <NUM> generated by the tissue. Such signals may be sensed, for example, while heart <NUM> experiences atrial fibrillation (AF) or any other arrhythmia. Note that in some cases, the arrhythmia may be induced by the physician as part of the procedure.

In some embodiments, a processor <NUM> of system <NUM> receives EGM signals <NUM> from distal end <NUM>, via an electrical interface <NUM>, such as a socket or port, and processes these EGM signals as will be described in detail in <FIG> and <FIG> below. In some embodiments, in response to processing the EGM signals, processor <NUM> is configured to generate an output, which typically comprises a visual output displayed on a display <NUM> of system <NUM>.

In some embodiments, processor <NUM> is configured to annotate at least one peak of EGM signal <NUM> to show the activation points of the EGM signal, and to display the annotated signal on display <NUM>. In annotating the signal, processor <NUM> may, for example, place a marker <NUM> over each activation point. In the context of the present invention and in the claims, the annotated signal shown by marker <NUM> is also referred to herein as a "primary peak" or as an "annotated activation signal.

In some embodiments, the electrodes at distal end <NUM> may be arranged in any suitable configuration, such as a circular, linear, or multi-spline configuration. Typically, each EGM signal <NUM> may be a bipolar signal, so that the signal represents the voltage between a respective pair of the electrodes at distal end <NUM>. In alternative embodiments, at least one of the acquired EGM signals may be a unipolar signal, so that the EGM signal represents the voltage between one of the electrodes and a reference electrode that is coupled externally to patient <NUM>.

In some embodiments, processor <NUM> may comprise a single processor, or a cooperatively networked or clustered set of processors. In some embodiments, the functionality of processor <NUM>, as described herein, may be implemented solely in hardware, e.g., using one or more Application-Specific Integrated Circuits (ASICs) or Field-Programmable Gate Arrays (FPGAs). In other embodiments, the functionality of processor <NUM> may be implemented at least partly in software. For example, in some embodiments, processor <NUM> may comprise a programmed digital computing device comprising at least a central processing unit (CPU) and random access memory (RAM). In some embodiments, system <NUM> may comprise any suitable types of non-volatile memory devices.

In other embodiments, processor <NUM> may comprise a general-purpose processor, which is programmed in software to carry out the functions described herein. The software may be downloaded to the processor in electronic form, over a network, for example, or it may, alternatively or additionally, be provided and/or stored on non-transitory tangible media, such as magnetic, optical, or electronic memory.

This particular configuration of system <NUM> is shown by way of example, in order to illustrate certain problems that are addressed by embodiments of the present invention and to demonstrate the application of these embodiments in enhancing the performance of such a system. Embodiments of the present invention, however, are by no means limited to this specific sort of example system, and the principles described herein may similarly be applied to other sorts of systems for mapping arrhythmia and to other sorts of systems for annotating and analyzing any suitable signals acquired from any human organ.

<FIG> is a graph showing an identification of activation points in an EGM signal <NUM>. EGM signal <NUM> may replace, for example, EGM signal <NUM> of <FIG> above.

In some embodiments, EGM signal <NUM> is acquired at a given position in heart <NUM> using one or more electrodes of distal end <NUM>, and comprises a complex fractionated EGM signal measured in millivolts (mV) over time. In some embodiments, processor <NUM> is configured to divide the total period that is spanned by the signal into successive smaller time periods, each being of a predefined length or within a range of lengths, e.g., between <NUM> milliseconds (ms) and <NUM>, or any other suitable period of time.

In some embodiments, processor <NUM> is configured to select a set of candidate activation points that comprises, for each of the time periods, the point (or "peak") of greatest magnitude within the time period, provided that the greatest magnitude is greater than the threshold for the time period and is also greater than a predefined noise threshold (e.g., <NUM> mV), indicated in <FIG> by upper and lower noise-threshold lines <NUM> and <NUM>. Note that line <NUM> is located <NUM> mV above the center line of EGM signal <NUM>, and line <NUM> is located <NUM> mV below the center line of EGM signal <NUM>. Subsequently, processor <NUM> is configured to remove, from the set of candidate activation points, one of any pair of the candidate points that are within a predefined time interval (e.g., <NUM>) from one another. The points remaining in the set are then assumed to be the annotated activation signals, in the present example, primary peaks <NUM>, <NUM> and <NUM>.

The processor <NUM> is configured to calculate an atrial fibrillation cycle length (AFCL) between adjacent annotated activation signals, in the example of <FIG>, an AFCL <NUM> between primary peaks <NUM> and <NUM>. In some embodiments, processor <NUM> may hold two thresholds used for defining a range of AFCLs that are considered regular for AF. For example, EGM signal <NUM> may be acquired over a total period of <NUM> and having seventeen (<NUM>) candidate primary peaks and therefore sixteen (<NUM>) calculated AFCLs. Each adjacent primary peaks may delimit an AFCL between <NUM> (referred to herein as a short AFCL) and <NUM> (referred to herein as a long AFCL) and having a standard deviation (SD) smaller than <NUM> relative to the calculated <NUM> AFCLs of EGM signal <NUM>.

In some embodiments, processor <NUM> is configured to calculate, based on the calculated AFCLs, an average AFCL and to hold two thresholds for the average AFCL and a threshold for the SD. In accordance with the above example, processor <NUM> may hold thresholds of <NUM> and <NUM> for the lower and higher values of the average AFCL, respectively, and to hold an additional threshold of <NUM> for the AFCL SD.

In some embodiments, processor <NUM> is configured to define a window of interest (WOI) <NUM> having a width smaller than the AFCL and/or within the duration of width <NUM>. In the example of <FIG>, the centers of WOIs <NUM> are aligned with the respective time of primary peaks <NUM>, <NUM> and <NUM> and extend along the time axis +/-<NUM>% of the AFCL length. In other words, WOI <NUM> has an <NUM>% length of the total length of AFCL <NUM>. In other embodiments, processor <NUM> may hold any other suitable threshold, other than <NUM>%, for defining the length (or time interval) of WOI <NUM> relative to AFCL <NUM>.

The processor <NUM> is configured to identify, within WOI <NUM>, a set of candidate fragmentation peaks, also referred to herein as secondary peaks. Processor <NUM> is further configured to filter out some of the candidate peaks, using a predefined threshold and criteria, and to obtain a final set of fragmentation peaks.

In the example of <FIG>, processor <NUM> identifies candidate peaks <NUM>, 106A, 106B, 106C, <NUM>, 108A and 108B. In an embodiment, processor <NUM> filters out candidate peak <NUM>, which is below the <NUM> mV noise threshold (i.e., between lines <NUM> and <NUM>. Note that processor <NUM> does not filter out peak 106C, which has an absolute value slightly larger than the noise threshold. In an embodiment, processor <NUM> checks whether all peaks larger than <NUM> mV, i.e., located above line <NUM>, are trending-up over time before the peak and are trending-down over time after the peak. Similarly, processor <NUM> checks whether all peaks smaller than -<NUM> mV, i.e., located below line <NUM>, are trending-down over time before the peak and are trending-up over time after the peak.

In some embodiments, processor <NUM> is configured to remove, from the set of candidate peaks, at least one of any group of the candidate peaks that are within a predefined time interval (e.g., <NUM>) from one another and having the same sign (positive or negative), and to consolidate the removed one or more peak into the largest peak from among the group. In the example of <FIG>, peaks 106A and 108A are within the predefined time interval of <NUM> and peak 108A is larger than peak 106A. Therefore, processor <NUM> removes peak 106A, or in other words, merges peak 106A into peak 108A. Similarly, peaks 106B and 108B are within the predefined time interval of <NUM> and peak 108B has an absolute electro-potential value larger than that of peak 106B. Therefore, processor <NUM> merges peak 106B into peak 108B. Subsequently, processor <NUM> produces a final list of the secondary peaks, in the example of <FIG> the final list comprises peaks <NUM>, 106C, 108A and 108B selected based on the threshold and criteria described above. Note that the time intervals of <NUM> and <NUM> predefined, respectively, for the primary and secondary peaks, the predefined time interval between <NUM> and <NUM> for the AFCLs, and the predefined noise threshold of +/-<NUM> mV, are all provided by way of example. In other embodiments, processor <NUM> may hold any other suitable one or more thresholds value for any of the above time intervals and/or noise threshold.

In some embodiments, processor <NUM> is configured to calculate for EGM signal <NUM> that was acquired at the given location of heart <NUM>, a local fragmentation index (FI) that is indicative of an average number of the secondary peaks per AFCL.

In some embodiments, processor <NUM> is configured to calculate the local FI using equation (<NUM>) given by: <MAT> where:.

For example, the total time duration of EGM signal <NUM> is <NUM>, the accumulated nominal time durations of all WOIs (NWOI) is <NUM>% of the total time, and therefore has a value of <NUM>. The accumulated actual time durations of all WOIs (AWOI) within EGM signal <NUM> is <NUM>. During the AWOI, a number <NUM> of primary peaks and a number <NUM> of secondary peaks remained after applying the threshold and filtering criteria described above.

In this example, processor <NUM> calculates the local FI using equation (<NUM>), and outputs the local FI as shown in equation (<NUM>): <MAT>.

In some embodiments, processor <NUM> is configured to output to display <NUM>, the local FI and the calculated average AFCL, on a map of at least part of heart <NUM>. Note that the local FI is indicative of an average number of the fragmentation peaks per AFCL between two annotated activation signals, shown in <FIG> as primary peaks <NUM>, <NUM> and <NUM>. Note that some sections of EGM signal <NUM> may comprise signals within the noise threshold, e.g., the section between WOIs <NUM> of primary peaks <NUM> and <NUM>, which has electro-potential values between lines <NUM> and <NUM>. In some embodiments, processor <NUM> may exclude such sections from the calculation of AFCLs and local FI, using any suitable criteria and/or predefined or learned parameters.

In other embodiments, processor <NUM> is configured to exclude the WOI and to identify the candidate set of secondary peaks along the entire AFCL between adjacent primary peaks. In such embodiments, the calculated local FI may comprise only the total number of identified secondary peaks, divided by the total number of primary peaks.

<FIG> is a flow chart that schematically illustrates a method for mapping AF in heart <NUM>.

The method begins at an EGM signal acquisition step <NUM>, with acquiring EGM signal <NUM> using distal end <NUM> having one or more electrodes positioned at the given position in tissue of heart <NUM>. In some embodiments, processor <NUM> receives EGM signal <NUM> that exhibits the AF, and identifies within EGM signal <NUM>, the annotated activation signals, such as primary peaks <NUM>, <NUM> and <NUM>.

At a cycle length calculation step <NUM>, processor <NUM> calculates the AFCL between any pair of adjacent primary peaks. In some embodiments, processor <NUM> calculates an average AFCL of the calculated AFCLs.

At a WOI definition step <NUM>, processor <NUM> defines a WOI, such as WOI <NUM>, having a length that is typically a fraction of the calculated AFCL, e.g., <NUM>% or <NUM>% of the AFCL length. In some embodiments, processor <NUM> calculates the WOI based on the aforementioned average AFCL length. In other embodiments, processor <NUM> calculates a WOI for each section of EGM signal <NUM>, using a predefined fraction of the AFCL length of the respective section. Additionally or alternatively, processor <NUM> may calculate an average length of two adjacent AFCLs for the definition of a WOI located between the two respective AFCLs, or using any other suitable method for defining the WOI.

At a fragmentation peaks identification step <NUM>, processor <NUM> identifies a set of candidate fragmentation peaks, such as peaks <NUM>, 106A, 106B, 106C, <NUM>, 108A and 108B shown in <FIG> above. In some embodiments, processor <NUM> may filter out at least some of the candidate fragmentation peaks, such as peak <NUM> which is within the noise threshold located between noise-threshold lines <NUM> and <NUM> shown in <FIG> above.

At a fragmentation peaks merging step <NUM>, processor identifies a group of peaks comprising two or more secondary peaks that are within a predefined time interval (e.g., <NUM>) from one another and having the same sign (positive or negative). For example, a group of peaks 106A and 108A, and another group of peaks 106B and 108B. In some embodiments, processor <NUM> may select, within the group of peaks, the peak having the largest absolute electro-potential value and removes the other peaks from the set of secondary peaks. As depicted in <FIG> above, peaks 108A and 108B had the largest absolute electro-potential value and therefore, respective peaks 106A and 106B have been removed.

At a local fragmentation index (FI) calculation step <NUM>, processor calculates the local FI using equation (<NUM>), which is described in <FIG> above. Processor <NUM> applies to equation (<NUM>), the total number of primary and secondary peaks identified and verified after the filtering and merging processes described in steps <NUM> and <NUM> above, and the total actual and nominal WOI time durations used for identifying the primary and secondary peaks. In some embodiments, the calculated local FI is indicative of the average number of secondary peaks per AFCL time duration. In the example shown in equation (<NUM>) above, the calculated value of the local FI equals <NUM>, which is indicative of the average number of secondary peaks per average AFCL time duration.

In other embodiments, processor <NUM> may apply any other suitable equation for calculating the local FI. For example, using a median value of all the AFCLs calculated within EGM signal <NUM>.

At a map displaying step <NUM>, processor is configured to output, to display <NUM>, the visualized map of at least part of heart <NUM>, which comprises at least the given point used for acquiring EGM signal <NUM> and the calculated FI and one or more AFCLs.

In some embodiments, display <NUM> displays the local FI on the map of heart <NUM> received from processor <NUM>. In some embodiments, display <NUM> is configured to display a visualization of the local FI using color coding or any other suitable visualization technique. For example, processor <NUM> and/or display <NUM> may assign warm colors, e.g., red, to small values of FI, and cold colors, e.g., blue, to large values of FI. As described in <FIG> above, display <NUM> may also display the one or more AFCLs overlaid on the heart map and on the displayed local FI. An exemplary embodiment of the map is shown in <FIG> below.

<FIG> is a schematic, pictorial illustration of a region <NUM> of heart <NUM> for which a regional fragmentation index (FI) is calculated based on multiple local FIs. In some embodiments, region <NUM> comprises points <NUM>, <NUM>, <NUM>, <NUM> and <NUM>, located at respective distances of <NUM>, <NUM>, <NUM>, <NUM>, and <NUM>, from a geometrical center-of-gravity (COG), referred to as COG <NUM> of region <NUM>.

In some embodiments, distal end <NUM> of catheter <NUM> acquired one or more EGM signals from each of points <NUM>, <NUM>, <NUM>, <NUM> and <NUM> and processor <NUM> calculates, e.g., using the techniques disclosed at <FIG> and <FIG> above, a local FI for each of the points of region <NUM>. In the example of <FIG>, the values calculated for the local FIs of points <NUM>, <NUM>, <NUM> and <NUM> are <NUM>, <NUM>, <NUM> and <NUM>, respectively.

In some embodiments, processor <NUM> is configured to calculate the regional FI of region <NUM> by calculating a weighted average over the local FIs of points <NUM>, <NUM>, <NUM>, <NUM> and <NUM>, using the respective distances from COG <NUM> for deriving the averaging weights. For a given point, the respective local FI is weighted by a weight value that is proportional to the inverse value of the distance between the given point and COG <NUM>. For example, point <NUM>, which is located <NUM> from COG <NUM>, has larger weight (e.g., <NUM>) relative to that of point <NUM> located <NUM> from COG <NUM>, and therefore having a 5X smaller weight (e.g., <NUM>/<NUM>).

In some embodiments, processor <NUM> calculates the weighted average by calculating a weighted sum and normalizing the weighted sum by the sum of weights. Based on the exemplary values provided above, the weights of points <NUM>, <NUM>, <NUM>, <NUM> and <NUM> are <NUM>, <NUM>, <NUM>, <NUM> and <NUM>, respectively, and the normalization factor is the sum of weights <NUM>.

In some embodiments, processor <NUM> calculates normalized weights for points <NUM>, <NUM>, <NUM>, <NUM> and <NUM> and outputs the respective values of <NUM>, <NUM>, <NUM>, <NUM> and <NUM>, and then multiplies, for each point, the respective local FI by the respective normalized weight, and sums.

Based on the exemplary values described above, processor <NUM> multiplies, for each point, the local FI of points <NUM>, <NUM>, <NUM>, <NUM> and <NUM> by the respective normalized weight, and outputs the respective values of weighted local FIs, <NUM>, <NUM>, <NUM>, <NUM> and <NUM>.

In some embodiments, after calculating a sum of the weighted local FIs, processor <NUM> outputs the regional FI having a value of <NUM>. Subsequently, processor <NUM> outputs the calculated regional FI and the calculated AFCLs of region <NUM>, to display <NUM>.

<FIG> is a schematic pictorial illustration of a map <NUM> of a region <NUM> of heart <NUM>. In some embodiments, map <NUM> comprises locations <NUM> identified by processor <NUM> and displayed by display <NUM> as locations having regular AF activity.

In some embodiments, processor <NUM> analyzes multiple EGM signals <NUM> acquired, e.g., by distal end <NUM>, at respective locations <NUM> of region <NUM>. As described in <FIG> and <FIG> above, processor <NUM> identifies the primary peaks, calculates CLs and identifies, within the CLs, the AFCLs having a CL duration between <NUM> and <NUM>. As further described in <FIG> and <FIG> above, processor <NUM> defines the WOIs, and identifies the fragmentation peaks within the respective WOIs. Based on the identified and verified fragmentation peaks (after filtering out the candidate fragmentation peaks not meeting the predefined threshold and/or criteria), processor <NUM> calculated local FI for each location <NUM>, as described in <FIG> and <FIG> above.

Subsequently, processor <NUM> calculates for each section of region <NUM>, a regional FI, using the technique described in <FIG> above, and outputs to display <NUM>, a visual map comprising the calculated AFCLs and/or one or more regional FIs. In some embodiments, display <NUM> is configured to display the output map using any suitable type of display, such as a gradient map using color coding.

In the example of <FIG>, map <NUM> of region <NUM>, which is displayed on display <NUM>, comprises multiple sections having respective contours <NUM>, <NUM>, <NUM>, <NUM>, <NUM> and <NUM>, each of which having a calculated regional FI. In some embodiments, map <NUM> comprises a legend <NUM>, which provides any suitable type of coding of the visualized regional FI values. For example, contours <NUM>, <NUM> and <NUM> have a "large" value of regional FI, contours <NUM> and <NUM> have a medium value of regional FI, and contour <NUM> has a "small" value of regional FI. In the present example, the term "large value of regional FI" refers to typical values between <NUM> and <NUM>, and the term "small value of regional FI" refers to a typical value smaller than <NUM>.

In some embodiments, map <NUM> further comprises AFCL markers <NUM>, <NUM>, <NUM>, <NUM>, <NUM> and <NUM>, which are calculated by processor <NUM> (e.g., using the technique described in <FIG> above) in the sections represented by respective contours <NUM>, <NUM>, <NUM>, <NUM>, <NUM> and <NUM>. In an embodiment, map <NUM> comprises a legend <NUM> that provides another set of coding of the visualized values of the average AFCL markers. In this example embodiment, <NUM> represents "short" AFCL values and <NUM> represents "long" AFCL values.

In some embodiments, the local FI is indicative of the average number of fragmentation peaks (also referred to herein as secondary peaks) per regular AFCL. Moreover, a short regular AFCL is indicative of a short time duration between two annotated activation signals (also referred to herein as primary peaks). In some cases, a combination of a large value of a regional FI and one or more short regular AFCLs, as shown for example in contour <NUM> and AFCL marker <NUM>, respectively, may be indicative of a section having high AF activity in region <NUM>.

In some embodiments, processor <NUM> may highlight on display <NUM> outstanding combinations, e.g., of high regional FI and short regular AFCLs, so as to draw the attention of physician <NUM> during the EP procedure shown for example in <FIG> above. In such embodiments, processor <NUM> may hold any set of thresholds for the regional FI and regular AFCLs, and for any combination thereof, so as to alert of any outstanding AF activities identified at specific sections or regions of heart <NUM>.

In alternative embodiments, processor <NUM> may add any other suitable types of markers to be visualized on map <NUM>, or may reduce at least one of the AFCLs or regional FIs shown on map <NUM> of <FIG>.

This particular configuration of map <NUM> is shown by way of example, in order to illustrate certain problems that are addressed by embodiments of the present invention and to demonstrate the application of these embodiments in enhancing the performance of a system for analyzing arrhythmia, such as system <NUM>. Embodiments of the present invention, however, are by no means limited to this specific sort of example mapping and visualizing configuration, and the principles described herein may similarly be applied to other sorts of visualizations of any medical systems.

Although the embodiments described herein mainly address arrhythmia and particularly atrial fibrillation, the methods and systems described herein can also be used in other applications, such as in persistent atrial fibrillation or in any other type arrhythmia in a human heart.

Claim 1:
A computer-implemented method for visualizing a map of atrial fibrillation,AF, in a heart, the method comprising:
receiving an electrogram, EGM, signal exhibiting the AF, acquired at a given position in the heart;
identifying (<NUM>), in the EGM signal, two or more primary activation peaks and calculating (<NUM>) a cycle length, CL, between adjacent primary activation peaks;
identifying (<NUM>), in the EGM signal, one or more fragmentation peaks within the CL, wherein identifying the one or more fragmentation peaks comprises identifying one of more candidate peaks, wherein each of the one or more candidate peaks has an absolute value larger than a noise threshold, and wherein identifying the one or more fragmentation peaks comprises merging, based on being within a predefined time interval and on having the same sign, two or more adjacent candidate peaks in the EGM signal;
calculating (<NUM>) a local fragmentation index, FI, that is indicative of a number of the fragmentation peaks per CL; and
visualizing (<NUM>), on a display, the local FI on a map of at least part of the heart.