Patent Publication Number: US-2022211315-A1

Title: Local activation driver classification mapping in atrial fibrillation

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Patent Application 63/133,723, filed 4 Jan. 2021, which is incorporated herein by reference. 
    
    
     FIELD OF THE INVENTION 
     This invention relates generally to cardiology, and specifically to cardiac arrythmia. 
     BACKGROUND OF THE INVENTION 
     Atrial fibrillation (AF) is the most common arrythmia, projected to affect 6-12 million people in the United States by 2050 and 17.9 million people in Europe by 2060. Radiofrequency (RF) or irreversible electroporation (IRE) or pulsed field (PF) ablation is a treatment option for AF which acts to change the path of an electric wave in the heart. However, in order to apply the ablation effectively, it is important to locate the source or driver of the AF, and methods for implementing this are known. 
     SUMMARY OF THE INVENTION 
     An embodiment of the present invention provides a method, consisting of: 
     acquiring, from a plurality of electrodes in contact with heart tissue undergoing atrial fibrillation, respective signals; 
     calculating from the signals respective mutual information metrics between multiple pairs of the electrodes; 
     generating a graph with the electrodes as nodes, and edges as connections therebetween for which the respective mutual information metrics exceed a selected mutual information metric threshold; 
     calculating a respective local efficiency metric for each node, indicating an efficiency of information exchange between the node and other nodes connected to the node, based on path lengths between the connected nodes; 
     averaging respective local efficiency metrics of the nodes to formulate a resultant local efficiency for the selected mutual information metric threshold; and 
     analyzing the resultant local efficiency and the selected mutual information metric threshold to classify the atrial fibrillation. 
     The signals may be unipolar or bipolar voltage or action potential voltage vs. time signals. 
     Typically, calculating from the signals includes estimating local activation times (LATs) from the signals. 
     In a disclosed embodiment the heart tissue is part of an atrium, and classifying the atrial fibrillation includes estimating a percentage of remodeling of the atrium. 
     In a further disclosed embodiment the method includes presenting to a user of the method a classification of the atrial fibrillation. 
     The plurality of electrodes may be located on a catheter having a multiplicity of spines. 
     Typically, the nearest-neighbor electrodes in the plurality of electrodes are separated by less than 3 mm. 
     In another embodiment, averaging respective local efficiency metrics of the nodes consists of generating subgraphs of nodes connected directly to a given node, calculating local efficiency metrics for each of the subgraphs, and averaging the calculated local efficiency metrics. 
     In yet another embodiment, the method includes reiterating the steps of generating the graph, and averaging the respective local efficiency metrics while incrementing the selected mutual information metric threshold, so as to produce a set of ordered pairs of resultant local efficiency and mutual information threshold. 
     The set of ordered pairs may be analyzed to classify the atrial fibrillation. Typically, analyzing the set of ordered pairs consists of fitting a polynomial to the set, and classifying the atrial fibrillation in response to a first derivative of the polynomial. 
     In a yet further embodiment, calculating from the signals includes calculating from the signals respective mutual information metrics between all pairs of the electrodes. 
     There is also provided, according to an embodiment of the invention, apparatus, including: 
     a probe, having a plurality of electrodes configured to contact heart tissue undergoing atrial fibrillation; and 
     a processor, configured to: 
     receive signals from the electrodes and calculate from the signals mutual information metrics between multiple pairs of the electrodes; 
     generate a graph with the electrodes as nodes, and edges as connections therebetween for which the respective mutual information metrics exceed a selected mutual information metric threshold; 
     calculate a respective local efficiency metric for each node, indicating an efficiency of information exchange between the node and other nodes connected to the node, based on path lengths between the connected nodes; 
     average respective local efficiency metrics of the nodes to formulate a resultant local efficiency for the selected mutual information metric threshold; and 
     analyze the resultant local efficiency and the selected mutual information metric threshold to classify the atrial fibrillation. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present disclosure will be more fully understood from the following detailed description of the embodiments thereof, taken together with the drawings, in which: 
         FIG. 1  is a schematic, pictorial illustration of an atrial fibrillation classification system, according to an embodiment of the present invention; 
         FIG. 2  is a flowchart of steps of an algorithm performed by a processor of the system, according to an embodiment of the present invention; 
         FIG. 3  illustrates two histograms, according to an embodiment of the present invention; 
         FIG. 4  is an exemplary schematic regional information graph, according to an embodiment of the present invention; 
         FIGS. 5A and 5B  are schematic diagrams illustrating subgraphs, according to an embodiment of the present invention; 
         FIGS. 6A and 6B  are schematic graphs of resultant local efficiency vs. mutual information thresholds, according to an embodiment of the present invention; 
         FIGS. 7A, 7B, and 7C  are further schematic graphs of resultant local efficiency vs. mutual information thresholds, according to an embodiment of the present invention; and 
         FIG. 8  shows schematic graphs of local efficiency first derivatives vs. mutual information thresholds, according to an embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION OF EMBODIMENTS 
     Overview 
     While, for atrial fibrillation (AF), it is important to locate the driver of the AF, it is also important to classify the atrial fibrillation to identify an optimal strategy for RF (radiofrequency) or IRE (irreversible electroporation)/pulsed field (PF) ablation. Areas with significant remodeling, i.e., electrophysiological and/or structural changes in the atrium, may be important in atrial fibrillation maintenance mechanisms. Consequently, knowing whether or not an atrium has been remodeled leads to improvement in the mapping and ablation strategy. 
     The present disclosure develops a novel strategy for estimating how much remodeling is present in a fibrillating atrium. 
     In an embodiment of the present invention, a probe, comprising a plurality of electrodes, is inserted into a human patient so that the electrodes contact heart tissue that is undergoing atrial fibrillation. A processor acquires signals from the electrodes, and calculates a mutual information metric between each pair of electrodes of the probe. For a given pair of electrodes, the mutual information metric provides a numerical value of the mutual dependence of the signals of the pair. (e.g., if the signals are independent of each other, the metric is close to zero.) 
     The processor generates a graph with the electrodes as nodes, and with edges, as connections between the nodes, that exceed a selected mutual information metric threshold. In one embodiment the threshold is selected so that at least 50% of the electrodes have connections. 
     The processor calculates a local efficiency metric for each of the nodes, the local efficiency metric for a given node being a measure of how efficiently nodes connected to the given node exchange information. The processor then averages the local efficiency metric for the graph to generate a resultant local efficiency metric for the selected mutual information metric threshold. 
     The processor reiterates the steps of generating the graph, and averaging the respective local efficiency metrics while incrementing the selected mutual information metric threshold, so as to produce a set of ordered pairs of resultant local efficiency and mutual information threshold. The processor then analyzes the set of ordered pairs so as to classify the atrial fibrillation. 
     The analysis typically comprises estimating a percentage of remodeling of the heart tissue. 
     DETAILED DESCRIPTION 
     In the following description, like elements in the drawings are identified by like numerals, and like elements are differentiated as necessary by appending a letter to the identifying numeral. 
     Reference is now made to  FIG. 1 , which is a schematic, pictorial illustration of an atrial fibrillation (AF) classification system  20 , according to an embodiment of the present invention.  FIG. 1  depicts a physician  22  using a catheter  24 , also referred to herein as a probe  24 , to acquire unipolar or bipolar intra-cardiac ECG (electrocardiograph) signals from tissue of a heart  26  of a patient  28 . Catheter  24  comprises, at its distal end  31 , a plurality of spines  30 , which may be mechanically flexible, and on each of the spines there are two or more electrodes  32 . The electrodes  32  are located on spines  30  so that the separation between nearest-neighbor electrodes is less than 3 mm. While  FIG. 1  depicts a catheter with five spines, embodiments of the present invention comprise catheters with other numbers of spines, as well as other catheters having a plurality of electrodes, where nearest-neighbor electrodes are separated by less than 3 mm. 
     Electrodes  32  are coupled, via conductors in catheter  24  and an interface  34 , to a processor  36 . Processor  36  comprises a processing unit  42 , typically a central processing unit (CPU) and also referred to herein as CPU  42 , which is coupled to a memory  46 . Memory  46  comprises a number of modules: an ECG module  50 , a tracking module  58 , and an AF analysis module  54 . The functions of the modules are described below. 
     CPU  42  typically comprises a general-purpose processor with software programmed to carry out the functions described herein. The software may be downloaded 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. 
     Physician  22  communicates with processor  36  via an input device  70 , such as a keypad or a pointing unit, as well as a screen  74 , and the processor  36  may present results of procedures performed by the processor on the screen. 
     In system  20  CPU  42  may track the locations of electrodes  32  using tracking module  58 . In one embodiment the CPU and the module are configured to implement an Advanced Current Location (ACL) system in system  20 . The ACL system is described in U.S. Pat. No. 8,456,182. In the ACL system, a processor estimates the respective locations of the distal electrodes based on impedances or currents measured between each of distal electrodes  32  and a plurality of surface electrodes  66  that are coupled to the skin of patient  28 . For ease of illustration, only one surface-electrode  66  is shown in  FIG. 1 . The processor may then associate any electrophysiological signal received from distal electrodes  32  with the location at which the signal was acquired. 
     Alternatively or additionally, the CPU and module  58  are configured to track the locations of electrodes  32  using an electromagnetic tracking system, such as is used in the Carto® System produced by Biosense Webster of Irvine Calif., and as is described in U.S. Pat. Nos. 5,391,199 5,433,489 and 6,198,963. In this case, one or more magnetic sensors  38 , typically single, double, or triple axis coils, are attached to distal end  31 . In addition, a set  60  of alternating magnetic field radiators are located in proximity to, typically beneath, patient  28 . Currents generated in sensors  38 , in response to the fields from set  60 , are registered by module  58 , and the module and the CPU analyze the currents to determine the location of the distal end as well as the locations of electrodes  32 , since the geometry of the distal end is known or may be estimated. 
       FIG. 2  is a flowchart of steps of an algorithm performed by processor  36 , under overall control of physician  22 , according to an embodiment of the present invention. In an initial step  100  of the flowchart, physician  22  inserts probe  24  into patient  28  when the patient has atrial fibrillation. Processor  36  uses module  58  to track distal end  31  of the probe, and the physician observes the tracking of the probe on screen  74 , and maneuvers the distal end until electrodes  32  of the probe contact a desired section of tissue within an atrium, herein assumed to comprise the left atrium, of heart  26 . 
     Typically the tracking within the atrium is presented to the physician by processor  36  overlaying an icon of the probe on a pre-acquired map  78  of the atrium that is presented on screen  74 . In addition to using the tracking provided by module  58 , the physician may use processor  36  to measure impedances between electrodes  32  and a return electrode  80  attached to the skin of patient  28 , and from the impedances confirm the contact of the electrodes with atrium tissue. 
     Once in position, physician  22  operates probe  24  to acquire and record sets of unipolar signals, i.e., sets of voltages measured with respect to electrode  80 , for each electrode  32 . Typically the sets of signals are acquired over a time period of approximately 2 minutes, i.e., for approximately 150 heartbeats, but embodiments of the invention may acquire signals for shorter or longer periods. Typically, a minimum of 30 seconds of AF should be recorded. CPU  42  stores the acquired signals in memory  46 , and for each signal the CPU uses ECG module  50  to calculate the local activations times (LATs). It will be understood that since patient  28  has atrial fibrillation, then, unlike a heart in sinus rhythm, the LAT values for one electrode, at a fixed position in the heart, have changing values, i.e., for the 150 heartbeats there may be 150 different LAT values. 
     In a first analysis step  104 , CPU  42  uses AF analysis module  54  to sort the set of LAT values for each electrode into bins of a histogram. Typically, to simplify the calculations described hereinbelow, the histograms have equal-width bins. Embodiments of the invention may use the histograms to represent a distribution of the LAT values In one embodiment the number of bins is approximately 
     
       
         
           
             
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     where L is the number of LAT values in the set. 
       FIG. 3  illustrates two equal-width bin histograms, according to an embodiment of the present invention. for electrodes  32 X and  32 Y, herein also referred to as electrodes X and Y. In the calculations performed by the CPU, the number of values in a given bin is assumed to give an estimate of the probability {circumflex over (p)} of the occurrence of the mean LAT of the bin. 
     Returning to step  104  of the flowchart of  FIG. 2 , module  54  estimates a mutual information metric for multiple pairs of electrodes  32  according to equation (1): 
     
       
         
           
             
               
                 
                   
                     
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     where N represents the number of bins in the histograms, 
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     L is the total number of values in each histogram, 
     
       
         
           
             
               
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     In the description hereinbelow, except where otherwise stated, the mutual information metric is estimated, using equation (1), for all pairs of electrodes  32 . However, those having ordinary skill in the art will be able to adapt the description, mutatis mutandis, for embodiments where the metric is estimated for fewer than all pairs of electrodes  32 , for example if some of the electrodes are not coupled to processor  36 , and all such embodiments are assumed to be comprised within the scope of the present invention. 
     The mutual information metric is known in the art, and is a measure of dependence between two signals: i.e., the amount of information obtained about one signal based on the observation of another. Thus, if one signal is a deterministic function of another their mutual information is maximized; but if two signals are completely independent of each other, their mutual information is close to 0. 
     In a graph preparation step  108 , CPU  42  uses module  54  to set a mutual information threshold such that a set percentage of electrode pairs have mutual information values above that value, and then prepares a graph, herein also termed a regional information graph, where nodes of the graph represent electrodes  32  and edges between the nodes represent connections that equal or exceed the threshold. Typically, the threshold is initially set to include 50% of electrode pairs. 
       FIG. 4  is an exemplary schematic regional information graph  82  prepared by CPU  42 , according to an embodiment of the present invention. By way of example in graph  82  there are 48 nodes  84 , corresponding to the number of electrodes  32  in distal end  31  of the catheter. Edges  86  connect the nodes, corresponding to connections that are equal to or greater than a mutual information threshold. 
     In a subgraph step  112 , for each given node in the regional information graph module  54  generates a subgraph, comprising a set of nodes that are connected to the given node being considered. CPU  42  calculates the shortest path between each pair of nodes in the set as the least number of connections between the pair, and for the calculation the CPU considers any given connection to have a unit length. The shortest path lengths are stored in memory  46 . 
       FIGS. 5A and 5B  are schematic diagrams of regional information graph  82 , illustrating respectively a subgraph  88  for a node  84 A, and a subgraph  90  for a node  84 B, according to an embodiment of the present invention. 
     As shown in  FIG. 5A , node  84 A has four connections or edges  86 A,  86 B,  86 C, and  86 D, respectively to nodes  84 B,  84 C,  84 D, and  84 E. Thus subgraph  88  for node  84 A comprises the nodes  84 B,  84 C,  84 D, and  84 E. The shortest path lengths between pairs of these nodes may be determined from inspection of  FIG. 5A , and is given in Table I: 
     
       
         
           
               
               
               
             
               
                   
                 TABLE I 
               
               
                   
                   
               
               
                   
                 Node Pair 
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     As shown in  FIG. 5B , node  84 H has three connections or edges  86 J,  86 K, and  86 L, respectively to nodes  84 J,  84 K, and  84 L. Thus subgraph  90  for node  84 H comprises the nodes  84 J,  84 K, and  84 L. In contrast to node  84 A, there are no paths between nodes of the subgraph of node  84 H. 
     In step  112  the inverse of the shortest path lengths is used to calculate a local efficiency metric, E local , for each node, according to equation (2): 
     
       
         
           
             
               
                 
                   
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     where N Gi  is the number of nodes in a subgraph Gi, and 
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     Local efficiency has been used in evaluating brains. It signifies fault tolerance, indicating how well each subgraph exchanges information when the central node is eliminated. 
     CPU  42  uses equation (2) to calculate a local efficiency metric for each node in the regional information graph. Thus, for node  84 A equation (2), using the values for the path lengths of Table I, gives E local  as 0.0972. 
     For node  84 H, since there are no paths between nodes of the subgraph E local  is 0. 
     In an averaging step  116  the calculated local efficiencies are averaged to provide a resultant local efficiency for the graph at the set mutual information threshold. CPU  42  stores the resultant local efficiency and the corresponding mutual information threshold as an ordered pair in memory  46 . 
     As shown in a decision step  120  and an increment step  124 , CPU  42  reiterates steps  108 ,  112 , and  116 , incrementing the mutual information metric threshold at each iteration, until a maximum value of the metric, typically approximately 95%, is reached. At each iteration the CPU stores the resultant local efficiency and the corresponding mutual information threshold as an ordered pair, so that when the iteration terminates, there is a set of ordered pairs available to CPU  42 . 
     In a results step  128 , from the set of ordered pairs produced in step  116 , CPU  42  generates a graph of the resultant local efficiency vs. mutual information thresholds. 
       FIGS. 6A and 6B  are schematic graphs of resultant local efficiency vs. mutual information thresholds for catheters having different electrode spacings, according to an embodiment of the present invention. In  FIG. 6A  a graph  200  illustrates simulated results for rotational signals acquired by a catheter with minimum spacing of electrodes 1 mm. In  FIG. 6B  a graph  204  illustrates simulated results for rotational signals acquired by a catheter with minimum spacing of electrodes 3 mm. 
     As is illustrated in graph  200 , there is a plateau  202  where the slope of the graph decreases, compared to the slopes on either side of the plateau. There is no such plateau in graph  204 . Graphs  200  and  204  illustrate that when electrodes are spaced by less than 3 mm, there is a plateau region in the graph when signals are rotational. This plateau is not present when the electrode spacing is 3 mm or greater. 
       FIGS. 7A, 7B, and 7C  are schematic graphs of resultant local efficiency vs. mutual information thresholds for a catheter having minimum electrode spacings of 2 mm, according to an embodiment of the present invention. The graphs are of simulated results for different percentages of remodeling of tissue of heart  26 . A graph  210  is for 10% remodeling, a graph  214  is for 50% remodeling, and a graph  218  is for 90% remodeling. As is apparent from the graphs, 10% remodeling does not generate a plateau, whereas 50% remodeling and 90% remodeling respectively generate plateaus  216  and  220 . 
     To illustrate the existence of a plateau more clearly, in step  128  CPU  42  fits a polynomial, typically a third degree polynomial to the generated graph, calculates the first derivative of the polynomial, and plots a graph of the first derivative vs. the information threshold. 
       FIG. 8  shows schematic graphs of local efficiency first derivatives vs. mutual information thresholds, generated from graphs  210 ,  214 , and  218 , according to an embodiment of the present invention. To produce the graph of  FIG. 8 , CPU  42  fits a third degree polynomial to each of graphs  210 ,  214 , and  218 . The processing unit then calculates first derivatives of the local efficiency for each of the fitted polynomials, and plots the first derivatives vs. mutual information thresholds. 
     A graph  224  is the first derivative local efficiency vs. mutual information thresholds for the 10% remodeling graph  210 ; 
     A graph  228  is the first derivative local efficiency vs. mutual information thresholds for the 50% remodeling graph  214 ; and 
     A graph  232  is the first derivative local efficiency vs. mutual information thresholds for the 90% remodeling graph  218 . 
     As seen in the graphs, the presence of a minimum in the graph indicates large or very large remodeling, whereas there is no minimum in the graph for small amounts, e.g., 10% remodeling. Furthermore, the “sharpness” of the minimum, e.g., the radius of curvature at the minimum, is indicative of the degree of remodeling, and embodiments of the invention may measure the radius of curvature, or some other metric of sharpness, to evaluate the degree of modeling. 
     In step  128  the first derivative graph may be presented to physician  22  on screen  74 . Alternatively or additionally, CPU  42  may determine whether or not there is a minimum in the first derivative graph, and if there is the CPU may measure its sharpness. From the determination the CPU may present a conclusion such as “no significant remodeling,” “intermediate remodeling,” or “high remodeling” on screen  74 . 
     It will be appreciated that in step  128 , there may be no requirement for CPU  42  to generate all the physical graphs described, and that the CPU may generate data corresponding to some of the graphs. In other words, the CPU may just use the ordered pairs from step  116 , and from the ordered pairs may present the first derivative graph and/or the conclusion described above. 
     The description above has, for simplicity, assumed that the signals acquired by the electrodes are used to form LATs. Those having ordinary skill in the art will be able to adapt the description, mutatis mutandis, using voltages, typically unipolar or bipolar voltages, or action potential voltages, of the signals, rather than LATs. 
     It will be appreciated that the embodiments described above are cited by way of example, and that the present invention is not limited to what has been particularly shown and described hereinabove. Rather, the scope of the present invention includes both combinations and subcombinations of the various features described hereinabove, as well as variations and modifications thereof which would occur to persons skilled in the art upon reading the foregoing description and which are not disclosed in the prior art.