Computer-implemented methods and systems for assisting in mapping cardiac rhythm abnormalities

Abstract

A computer-implemented method and system are described that identify one or more regions of the heart responsible for supporting or initiating an abnormal heart rhythm. Electrogram data recorded from multiple electrodes on a multipolar cardiac catheter is used, the electrogram data being obtained from a corresponding series of sensing locations on the heart during a recording time period. The method includes the steps of: identifying, based on the electrogram, a region within a chamber of the heart having an electrical activation sequence that characterizes the region as a potential driver of an abnormal heart rhythm; for each sensing location at or substantially surrounding the region, determining an earliest activation electrode site based on the predominant activation; for each determined earliest activation electrode site: calculating a value for each of a plurality of modifiers associated with the electrode site, the modifier being determined based on the electrogram data for the site, a tissue feature of the site, or an anatomical feature of the site; determining a ranking factor calculated based on the plurality of modifiers; ranking each of the earliest activation electrode sites based on the ranking factor; and outputting data identifying the region, the data varying the significance of each of the determined earliest activation electrode sites depending on the ranking.

Description

Technical Field

[0001] The present invention relates to computer-implemented methods and systems for assisting in mapping abnormal heart rhythms, and in particular to methods and systems for identifying regions of the heart that are statistically likely to drive abnormal heart rhythms. Background Art

[0002] Atrial fibrillation (AF) is the most common sustained abnormal heart rhythm. The incidence of AF is increasing, in part due to an aging population, and it has been described as a growing epidemic. AF causes irregular contractions of the heart, which can cause uncomfortable symptoms of palpitations and increase the risk of stroke, heart failure (HF), and death. Percutaneous catheter ablation (CA) is a safe treatment option for patients with symptomatic AF. The success rates of these procedures have improved over time due to a better understanding of AF, the development of new techniques and technologies, and greater physician experience. However, the success rates of these procedures remain at only between 50% and 70%. A major reason for the difficulty in identifying the specific sites responsible for the persistence and maintenance of AF (hereafter referred to as AF drivers) is the irregular and chaotic nature of the activation wavefront in AF. The constant tortuosity and instability of individual rotations, reentry, or focal activations make the interpretation of the activation sequence very complicated.

[0003] Recently, a number of computational and electroanatomical methods have been developed that allow electrical data (i.e., electrograms) to be recorded from within the atria and presented to the physician in a manner that identifies specific "driven" areas. These drives can also be easier or more difficult to identify, depending on the relationship between the frequency of the drives and the activation frequency of non-driven random and chaotic activity. Panoramic mapping techniques attempt to address this problem. Here, a multipolar catheter is inserted into the cardiac chamber of interest, and signals are acquired simultaneously across the chamber. Examples of this include non-contact mapping (Ensite, Abbott Medical; alternatively Acutus Medical), as well as specific 2D and 3D contact mapping methods (e.g., Cartofinder, Biosense Webster, J&J; Topera, Abbott Medical; Rhythmia, Boston Scientific).

[0004] Evidence is conflicting as to whether electrogram features can be used as surrogate markers of local drive in persistent AF in humans. These can be termed atrial fibrillation drive (AFD). Understandably, a single AFD may induce regular, non-fibrillatory arrhythmias, and thus an AFD can be considered atrial fibrillation / arrhythmia drive. While tissue features of the electrogram may better identify sites that play a mechanical role in AF, rapid markers are less reliable. Optical mapping studies in animals have shown that AF is sustained by sites that exhibit the fastest cycle length (CL) and highest dominant frequency (DF), however, in humans this has been shown to be a poor predictor of sites that support AF. The poor correlation may be due to a lack of spatiotemporal stability of drive in AF, which could explain the apparent inconsistency of rapid sites. Furthermore, optical mapping studies in animals have demonstrated intra-atrial high-to-low-frequency gradients at rotor sites. While frequency gradients have also been demonstrated in humans with AF, these gradients are limited to intra-atrial gradients.

[0005] STAR Mapping method

[0006] The STAR mapping method has been described in detail in the above-mentioned patent application and has been validated in vitro and in vivo by mapping atrial tachycardia (AT) before being used to map AF. In short, the principle of the STAR mapping method is to use temporal comparisons of multiple electrograms to establish individual wavefront trajectories associated with AF. This is then used to identify atrial regions that are most often activated before adjacent regions. By collecting data from many activations, a statistical model can be formed. This allows regions of the atrium to be ranked according to the amount of time they are activated before adjacent regions. The timing of unipolar activation is usually taken as the maximum negative deflection (peak negative dv / dt), but activation timing can be derived from other methods, such as dipole density or peak bipolar or resolved omnipolar voltage. By utilizing a predefined refractory period, the mapping method avoids allocating activation from individual wavefronts or graded electrograms. The mapping method also excludes unreasonable electrode timing relationships due to conduction velocity limitations.

[0007] One form of the STAR map display consists of color-coded electrode locations projected onto a replica of the patient's atrial geometry created in a standard 3D mapping system. Each color represents the proportion of time that an electrode spends leading relative to the other paired electrodes, as highlighted by the color scale to the right of the STAR map.

[0008] Problems with Current Approaches

[0009] A mapping system that seeks to identify the proportion of time that an electrode is "leading" relative to its neighbors can be used to generate a global distribution of ranked sites of potential AF drive when using a basket to map the atria. However, with sequential high-density mapping techniques, no information is provided about the relative importance of early sites relative to each other. For example, many sites may all be considered leading.

[0010] Another problem with the analysis of sequential acquisition data relates to the complexity of the interpretation of the leading designated as the edge of the acquisition region. This is illustrated schematically in Figure 1. A rule-based approach to the interpretation of such data is advantageous because it allows for automated interpretation and the removal or reduction of the significance of the display of passive activation sites at the edge of the acquisition reduces the areas that can be highlighted as potential driver sites. Summary of the invention

[0011] According to one aspect of the present invention, there is provided a computer-implemented method for identifying one or more regions of a heart that are responsible for supporting or initiating an abnormal heart rhythm, the computer-implemented method using electrogram data recorded from a plurality of electrodes on a multipolar cardiac catheter, the electrogram data being obtained from a corresponding series of sensing locations on the heart over a recording period, the method comprising the steps of:

[0012] identifying, based on the electrogram, regions within chambers of the heart having sequences of electrical activation that characterize the regions as potential drivers of abnormal heart rhythms,

[0013] for each sensing location at or substantially surrounding the region, determining an earliest activated electrode site based on the predominant activation;

[0014] For each earliest activated electrode site identified:

[0015] calculating a value for each of a plurality of modifiers associated with the electrode site, the modifiers being determined based on electrogram data of the site, tissue characteristics of the site, or anatomical characteristics of the site;

[0016] determining a ranking factor calculated based on the plurality of modifiers;

[0017] ranking each of the earliest activated electrode sites according to a ranking factor; and

[0018] Data identifying the regions is output, the data varying the significance of each of the determined earliest activated electrode sites depending on the ranking.

[0019] Data that changes the significance of each identified earliest activated electrode site according to the ranking may suppress display of the sites, highlight them in reports / spreadsheets, change the visual significance of the sites in graphical displays (which may overlap on a view of the heart, etc.), serve as a trigger for a scanning system to consider / ignore the area, etc.

[0020] Modifiers determined from electrogram data may include:

[0021] The minimum cycle length of the electrogram recorded at the electrode, the activation frequency gradient between the electrogram recorded at the electrode or within the region and the electrogram obtained within a predetermined geodesic distance, and the average voltage of the local electrogram recorded within the region.

[0022] Modifiers regarding tissue characteristics may include measurements of the presence and density of scar tissue as determined by imaging.

[0023] Modifiers regarding tissue characteristics may include a measure of tissue impedance at the site.

[0024] The modifier may be determined based on the treatment outcome at another earliest activated electrode site.

[0025] Modifiers regarding tissue properties can be calculated by referring to the resection results of similar sites in previous cases.

[0026] Modifiers regarding anatomical features may include predetermined weighting factors that depend on the location of the electrode sites.

[0027] The output data may include said data displayed with a visual indication that varies the significance of each of the determined earliest activated electrode sites depending on said ranking.

[0028] The method may further comprise:

[0029] The main electrogram wavefront trajectory was determined for each electrode site;

[0030] Determine the vector of electrographic activation across multiple electrodes and the main derivative of activation;

[0031] If the direction of activation progresses from an electrode site to one or more electrode sites that are close to a potential driver site, classifying the electrode site as an earliest activated electrode site and a potential driver site; and

[0032] Reference conduction velocities and pathways are within a reasonable activation sequence, and the order and timing of activation are within a predetermined biologically reasonable pattern.

[0033] In one embodiment, if activation occurs across at least two electrodes, the angle from the vertex subtended by the potential driving site to the two electrodes is less than a predetermined angle, then the site is classified as an earliest activated electrode site, and

[0034] At least one electrographic activation was determined to be later than the potential driver site within the defined arc of excitatory tissue.

[0035] The predetermined angle may be less than 180 degrees.

[0036] The method may further include outputting the data to a display or medical scanning device to cause display or navigation of chamber geometry and guide placement of catheters or electrodes for subsequent electrogram data acquisition.

[0037] The method may also include identifying sites for subsequent placement of the catheter or electrode to eliminate or confirm a previously determined first activated electrode, or to extend the electrogram data to a previously unscanned or incompletely scanned area.

[0038] The method may further comprise identifying the site based on the position of an already identified first activation site or based on a gradient in the activation vector and the signal lead score.

[0039] The method may further include receiving electrogram data in substantially real time and providing an indication as to when sufficient acquisition timing has occurred for the scanned site.

[0040] The step of determining whether sufficient acquisition timing has been performed may include determining that the activation pattern of the site being scanned has reached a predetermined level of statistical certainty, for example, that there is a 95% probability that the pattern is not random (which may be achieved within a few seconds if the rhythm is very regular).

[0041] The step of determining whether sufficient acquisition timing has occurred may include one or more of the following:

[0042] Count down a predetermined time period (e.g., 30 seconds with reference to the previous case), collect a preset number of activation cycles (e.g., 50 activation cycles) on at least a preset number of electrodes on the flow multipolar mapping catheter, and collect a predetermined time or a predetermined number of activation cycles, wherein the activation pattern across multiple electrodes remains within a pattern matching order.

[0043] The method also includes deriving a wavefront direction by determining a leading electrode in each electrode pair, and processing the wavefront direction to determine whether the earliest activated electrode is a true AFD or represents a passive activation site that is activated outside of the measurement range.

[0044] Advantageously, in embodiments of the present invention, additional information is utilized so that a ranking of the importance of the earliest activated (leading) sites can be performed. In irregular, complex rhythms, sites acquired at different time points can be compared and used to verify whether they are truly leading. Other characteristics of the electrical activity of atrial tissue regions can also be used to help indicate areas where resection will interrupt or slow AF. The combination of other measured factors improves the ability to indicate areas of the heart where treatment will provide the best effect. Embodiments incorporate factors to modify, weight, or further rank the signal lead scores produced by such mapping to improve the selection of resection areas and indicate the importance of areas for the persistence of arrhythmias. In addition, the combination of these factors can also be used to provide negative weights, indicating that these areas are less likely to cause arrhythmic mechanisms.

[0045] According to another aspect of the present invention, there is provided a computer-implemented method for identifying one or more regions of the heart responsible for supporting or initiating an abnormal heart rhythm by analyzing electrograms acquired from the heart, the computer-implemented method using electrogram data recorded from a plurality of electrodes on a multipolar cardiac catheter, the electrogram data being obtained from a corresponding series of sensing locations on the heart over a recording time period, the method comprising the steps of:

[0046] By analyzing the sequence of electrical activation, specific regions within the heart's chambers are defined as potential drivers of abnormal heart rhythms;

[0047] Classification of areas as potential drivers is weighted according to factors including:

[0048] During the same acquisition period, the direction of the wavefront generated by the activation travels from the potential actuation site to one or more nearby electrodes relative to the actuation site,

[0049] The sequence and timing of activation are within a biologically plausible manner, with reference to conduction velocities and pathways within a plausible activation sequence,

[0050] Activation occurs across at least two electrodes, the angle between the vertex subtended by the potential actuation site and the two electrodes is less than a predetermined angle, such as less than 180 degrees, and

[0051] At least one electrographic activation was determined to be later than the potential driver site within a defined arc of excitable tissue, and

[0052] Further acquisitions performed at adjacent locations opposite the missing arc of the first potential driver site do not disconfirm potential driver sites at similar locations; and

[0053] Based on the weighting, the refined potential drivers are displayed in a highlighted manner, the display being correlated to a computer representation of the heart chambers.

[0054] According to another aspect of the present invention, there is provided a computer system for identifying one or more regions of myocardium responsible for supporting or initiating an abnormal heart rhythm, the computer system using electrogram data recorded from a plurality of electrodes on a multipolar cardiac catheter, the electrogram data being obtained from a corresponding series of sensing locations on the heart over a recording period, the system comprising:

[0055] processor;

[0056] a first memory for storing received electrogram data; and

[0057] a second memory storing program code, which, when executed by the processor, causes the system to:

[0058] identifying, based on the electrogram, regions within chambers of the heart having sequences of electrical activation that characterize the regions as potential drivers of abnormal heart rhythms,

[0059] for each sensing location at or substantially surrounding the region, determining an earliest activated electrode site based on the predominant activation;

[0060] For each earliest activated electrode site identified:

[0061] calculating a value for each of a plurality of modifiers associated with the electrode site, the modifiers being determined based on electrogram data of the site, tissue characteristics of the site, or anatomical characteristics of the site;

[0062] determining a ranking factor calculated based on the plurality of modifiers;

[0063] ranking each of the earliest activated electrode sites according to a ranking factor; and

[0064] Data identifying the regions is output, the data varying the significance of each of the determined earliest activated electrode sites depending on the ranking.

[0065] When the program code is executed by a processor, it can cause the system to determine modifiers determined from the electrogram data, including one or more of the following:

[0066] The minimum cycle length of the electrogram recorded at the electrode, the activation frequency gradient between the electrogram recorded at the electrode or within the region and the electrogram obtained within a predetermined geodesic distance, and the average voltage of the local electrogram recorded within the region.

[0067] The program code, when executed by the processor, may cause the system to determine a modifier regarding a tissue characteristic by obtaining a measurement of the presence and density of scar tissue determined by imaging.

[0068] The program code, when executed by the processor, may cause the system to determine a modifier regarding a tissue characteristic by obtaining a measurement of tissue impedance at the site.

[0069] The program code, when executed by the processor, may cause the system to determine a modifier regarding a tissue characteristic by obtaining a treatment result at another earliest activated electrode site.

[0070] The program code, when executed by the processor, may cause the system to determine modifiers regarding tissue characteristics by accessing data regarding resection results of similar sites in previous cases.

[0071] The program code, when executed by the processor, causes the system to determine a modifier by accessing data to obtain a predetermined weighting factor, the predetermined weighting factor being dependent on the location of the electrode site.

[0072] The program code, when executed by the processor, may cause the system to:

[0073] The main electrogram wavefront trajectory was determined for each electrode site;

[0074] Identify vectors of electrographic activation across multiple electrodes and major derivatives of activation;

[0075] If the direction of activation progresses from an electrode site to one or more electrode sites that are close to a potential driver site, classifying the electrode site as an earliest activated electrode site and a potential driver site; and

[0076] Reference conduction velocities and pathways are within a reasonable activation sequence, and the order and timing of activation are within a predetermined biologically reasonable pattern.

[0077] , the program code, when executed by the processor, may cause the system to output a visual indication that changes the significance of each of the determined earliest activated electrode sites depending on the ranking.

[0078] , the program code, when executed by the processor, can cause the system to output data to a display or medical scanning device to cause display or navigation of chamber geometry and guide placement of catheters or electrodes for subsequent electrogram data acquisition.

[0079] Embodiments of the present invention seek to provide a system and method that can be used to refine the STAR method and other similar mapping methods based on human body data, typically electrographic data as well as imaging data (e.g., tissue features or anatomical data acquired from CT or MRI scans) acquired during electroanatomical mapping, and prioritize statistically identified potential AFDs in an order that may include the following factors:

[0080] Cycle length measurements and comparisons of identified potential AFDs, in particular the shortest cycle length, the lowest cycle length variation compared to other identified AFDs, and the presence of a steep cycle length (CL) gradient around the AFD.

[0081] The steepest CL gradient or the presence of a CL gradient between the potential AFD and surrounding electrodes can be recorded in the same acquisition, but only if the direction of the wavefront generated by the STAR mapping travels from the AFD to the paired electrode (to avoid high cycle lengths associated with wavefront collisions.

[0082] If a wavefront propagates at at least two electrodes with an angle less than 108° (with the AFD opposite as the vertex), the wavefront is considered to originate from the AFD site.

[0083] Other standards that may apply include:

[0084] If the AFD is not paired with an electrogram timing later than it within the 180° arc, it is assumed to be a peripheral point whose activation is likely to come from directions within the 180° arc rather than from the AFD, unless there is a scar within the 180° arc.

[0085] An AFD can only be marked as such if it has more than one electrode pair behind it (and the spacing of these electrode pairs is large enough that they do not allow the AFD to produce a null arc of 180° arc in which case the AFD is considered a peripheral device activation rather than a true AFD).

[0086] An example of 180 degrees is given because the activation site can be represented as coming from a direction within the defined arc, rather than from a potential drive site.

[0087] In a preferred embodiment, potential AFDs can be further refined in their relative importance by reference to modifying characteristics from electrographic signatures or tissue characteristics of the underlying tissue.

[0088] One embodiment is directed to a system for analyzing electrograms acquired from a heart.

[0089] Defining specific areas within the chambers of the heart that have sequences of electrical activation that identify specific areas as potential drivers of abnormal heart rhythms, and

[0090] The importance of each defined region is classified by the electrographic characteristics of the region, rather than activation sequence, anatomical or imaging properties, surface electrograms, and / or patient characteristics, which may include:

[0091] The shortest or smallest loop length compared to all other early activation sites identified, and / or

[0092] If the direction of the calculated activation order is from the potential drive to the electrode to which it is compared, this can be further refined by considering only the gradients between the potential drive and nearby electrodes recorded in the same acquisition, and / or

[0093] The lowest period length variability compared to all other early activation sites identified, and / or

[0094] The steepest cycle length gradient or the presence of a cycle length gradient between an early activation site and one or more electrodes in the same acquisition within a defined geodesic distance of the potential site of interest (e.g., <3 cm for irregular rhythms); and / or

[0095] Assessment of geodesic distance to sites of interest in regions of unipolar and / or bipolar electrogram voltage abnormalities, and / or

[0096] Evaluation of frequency analysis or frequency gradient analysis, such as the dominant frequency at the site of interest and other sites within a defined geodesic distance, and / or

[0097] Assessment of anatomic and structural features identified by imaging methods (eg, intracardiac echocardiography, cardiac magnetic resonance imaging, cardiac computed tomography), such as:

[0098] Close to pulmonary veins or atrial appendage tissue

[0099] Junction of appendage and vein

[0100] Marshall Vein

[0101] Mitral annulus

[0102] Temperance

[0103] Aneurysm

[0104] Identify scars

[0105] Changes in tissue thickness

[0106] and / or

[0107] Assessment of tissue electrical impedance, and / or

[0108] Assessment of tissue motion or thickening, either directly by imaging or by reference to the motion of a cardiac catheter in contact with cardiac tissue, and / or

[0109] Evaluation of correlation between identified sites of interest and sites identified as important by other cardiac rhythm mapping systems, such as giving higher importance to regions of interest identified by two or more methods, which may be on the same cardiac geometry or on the same anatomical region, which may be on two or more geometries created by different cardiac mapping systems; and

[0110] applying weights to further categorize the likelihood of a beneficial effect of the intervention at those sites, which may be determined by reference to previously acquired data and responses to resection, wherein machine learning techniques may be used to calculate and optimize the weighting factors to be used, and,

[0111] Categorical data are displayed with a visual indication of the calculated importance of these sites.

[0112] In one embodiment, a system for analyzing electrograms acquired from the heart is disclosed. The system can define specific regions where electrode activations typically precede electrode activations within a specific geodesic distance and calculate a time proportion or signal lead score for each identified location, and

[0113] The lead score is modified by applying adjustment factors calculated from other features of the electrogram, anatomical or imaging properties, surface electrograms, and / or patient characteristics.

[0114] The properties of the average wavefront direction or vector (from determining the leading electrode in each pair, i.e., STAR mapping) can be used to confirm whether the electrodes identified as potential AFD are indeed true AFDs. Electrodes on the edge of the region are mapped and identified as potential AFDs in one acquisition, which may represent passive activation sites that themselves activate from outside the measurement boundaries. If this potential AFD site can be passively activated, its significance may be reduced.

[0115] Embodiments may be used to record data (highlighting potentially problematic or confirmed AFD sites). Alternatively, embodiments may operate in substantially real time to highlight or instruct a user to move an electrode mapping catheter to an area most likely to cover an AFD, or to instruct them to move away from an area less likely to cover an AFD (i.e., an introduction to a system that uses these features to guide mapping catheter movement).

[0116] In one embodiment, a system for recording and analyzing electrograms acquired from a heart is disclosed. The system can define specific regions within a chamber of the heart with an electrical activation sequence that identifies the specific regions as potential drivers of abnormal heart rhythms, wherein the apparent potential abnormal heart rhythm driver sites acquired by continuous multipolar mapping can be further classified by separate rules to determine whether the driver site identified in a single mapping acquisition is the true source of the arrhythmia:

[0117] wherein an electrode site is classified as potentially driven only if the direction of the wavefront produced by activation travels from the potential driven site to one or more electrodes compared to a driven site during the same acquisition period; and

[0118] Electrode sites were classified as potentially driving only if the order and timing of activation were within a biologically plausible pattern, the reference conduction velocity and path were within a plausible activation sequence, and at least 2 electrodes were at an angle less than the defined angle (e.g., the angle for the potential driving site to be the vertex was less than 180 degrees);

[0119] and wherein an electrode site may be excluded as a potential driver, i.e., classified as a passive activation site, if the electrode site does not have an electrogram timing pairing later than it within an arc of defined excitatory tissue, e.g., 180 degrees (because such an activation site can be represented as coming from a direction within the defined arc rather than from a potential driver site), and further acquisitions performed at adjacent locations opposite the missing arc of the first potential driver site fail to confirm a potential driver site at a similar location, and wherein identified scar tissue is classified as non-excitable and is considered not to contribute to the activation arc.

[0120] Further modification and adaptation of the weighting factors may depend on the electrographic characteristics of the region (besides the activation order), the anatomy of the potential driver site, the imaging characteristics surrounding the potential driver site, the characteristics of the surface electrographic characteristics, and / or patient characteristics. Typical electrical characteristics that may be considered as weighting factors may include:

[0121] If the direction of the calculated activation order travels from the potential drive toward the electrode to which it is compared, the shortest or minimum cycle length compared to all other early activation sites identified can be further refined by considering only the gradient between the potential drive and nearby electrodes recorded in the same acquisition.

[0122] Additionally, other electrical properties of the electrograms recorded at the potential driver site compared to all other identified early activation sites may be considered relevant and their quantification used as input to the modification factors (including minimum cycle length variability), and / or

[0123] The steepest cycle length gradient or the presence of a cycle length gradient between an early activation site and one or more electrodes in the same acquisition within a defined geodesic distance of the potential site of interest; and / or

[0124] Assessment of geodesic distance to sites of interest in regions of unipolar and / or bipolar electrogram voltage abnormalities, and / or

[0125] Evaluation of frequency analysis or frequency gradient analysis, such as the dominant frequency at the site of interest and other sites within a defined geodesic distance, and / or

[0126] Measurement of tissue electrical impedance at the actuation site.

[0127] Any one or combination of the above can be used to further determine the likelihood that a particular site contributes to the occurrence of an arrhythmia, and direct quantification of such sites can be used as a modifying factor.

[0128] Additionally, assessment of anatomical and structural features may be performed using imaging methods such as intracardiac echocardiography, cardiac magnetic resonance imaging, or cardiac computed tomography, and one or more resulting quantitative metrics used as input to the modification factors.

[0129] The anatomical location itself may also be used along with factors based on proximity to the anatomical structure, such as the calculated geodesic distance to any one or more defined anatomical points, e.g.:

[0130] Close to pulmonary veins or atrial appendage tissue

[0131] Junction of appendage and vein

[0132] Marshall Vein

[0133] Mitral annulus

[0134] Temperance

[0135] Aneurysm

[0136] Identify scars

[0137] Changes in tissue thickness

[0138] Dynamic assessment of tissue motion or thickening, either directly from imaging or with reference to the motion of a cardiac catheter in contact with cardiac tissue, can be used as a modifying factor to further refine site importance.

[0139] In one embodiment, a computer-implemented method for analyzing recorded electrograms acquired from a heart is operated to identify, from electrogram regions within a chamber of the heart, regions having a sequence of electrical activation that characterizes the region as a potential driver of an abnormal heart rhythm, and

[0140] The importance of each of the defined regions is ranked by applying a ranking factor to each identified region, wherein the ranking factor is a product of a plurality of normalization factors and is subject to predetermined weighting factors, including

[0141] The minimum cycle length of the electrogram recorded by this electrode, once extreme outliers have been excluded, and

[0142] the activation frequency gradient between the electrogram recorded at that electrode or within that region and the electrogram obtained within a predetermined geodesic distance, and

[0143] the average voltage of the local electrogram recorded in that area, and

[0144] The data are displayed with a visual indication of the calculated importance classification of these sites.

[0145] In another embodiment, a system for analyzing an electrogram acquired from a human or animal heart includes a computer processor configured to execute computer program code to:

[0146] identifying activation sequences of electrical activity from the electrogram to define specific regions within the chambers of the heart having sequences of electrical activation that characterize the region as a potential driver of an abnormal heart rhythm, and

[0147] ranking the importance of each of said defined regions by applying a ranking factor to each identified region,

[0148] The ranking factor is the product of a plurality of normalization factors and is subject to a predetermined weighting factor, wherein the ranking factor is calculated based on a factor selected from a set of factors, the set of factors comprising:

[0149] The minimum cycle length of the electrogram recorded by this electrode, once extreme outliers have been excluded, and

[0150] the activation frequency gradient between the electrogram recorded at that electrode or within that region and the electrogram obtained within a predetermined geodesic distance, and

[0151] the average voltage of the local electrogram recorded in that area, and

[0152] The data is displayed along with a visual indication of the classification of these sites representing the heart chambers.

[0153] Multiple cardiac mapping systems may be used at one time, and the correlation between the identified sites of interest and sites identified as important by other cardiac mapping systems may be used as a modifying factor to give greater importance to areas of interest identified by two or more methods that may be located on the same cardiac geometry or within the same anatomical region on two or more geometries created by different cardiac mapping systems.

[0154] Each defined factor can be influenced by a weighting factor designed to provide a measure of the likelihood of a beneficial effect of the intervention at those sites. These weighting factors can be determined by reference to previously acquired data, the resection response of previous patients, and / or by reference to the resection response of the same patient in the study. Machine learning techniques can be used to calculate and optimize the weighting factors and final sites to be used, which are displayed along with a visual indication of the final calculated importance classification for each site, such as by color coding, percentage of likelihood of arrhythmia termination, or beneficial effect or importance ranking.

[0155] Embodiments of the present invention provide systems and methods for improving CL analysis, and sites that exhibit faster speed and organization can improve the sensitivity and specificity of identifying driver sites in AF, such as by identifying cardiac regions that activate before adjacent regions. In one embodiment, the STAR mapping method is used, but there are other methods that can identify potential driver sites. Potential AFD can be identified by statistical methods or other commercial methods (e.g., Cartofinder [Biosense Webster, Haifa, Israel], Acutus, ECGi [e.g., Cardioinsight, Medtronic, Ireland], Ablacon, Topera mapping [Abbott Ltd., Mn, USA]) that identify the earliest activated local areas. It should be understood that electrogram features applicable to the atria (e.g., AFD) can also be applicable to other rhythms and chambers, such as ventricular fibrillation or tachycardia.

[0156] In experimental trials, the STAR method has been used to identify potential AF drivers (AFDs) and confirm that the AFDs are those that respond to ablation of those sites with either a slowing of AF cycle length >30 ms or AF termination. However, it should be understood that other factors can also be used to more accurately identify AF drivers.

[0157] Furthermore, testing showed that driver sites identified using the STAR approach showed greater speed and organization in maintaining AF, which was mechanistically more important, as evidenced by a greater likelihood that resection would terminate AF.

[0158] There are several novel methods that can be used as weighting factors that can be used to modify the signal lead scores (ranking factors) that can be used in embodiments of the present invention. These can be subdivided into modifiers related to individual electrodes, modifiers related to the relationship between individual electrodes and surrounding electrodes, modifiers related to specific locations and individual patient-specific data that are not derived from electroanatomy or physiology, location-specific modifiers of results characteristics from patient groups, and general modifiers that can include demographic data.

[0159] Embodiments of the invention include an apparatus and associated computer-implemented methods that seek to improve the effectiveness of identifying potential arrhythmia driver sites from electroanatomical mapping, thereby allowing them to be better classified and their importance determined. The data generated can be displayed to a physician or otherwise output or communicated to other systems. It can be used during a cardiac catheterization procedure, such as a catheter ablation procedure (immediately or at a later time) to highlight locations where ablation will have the most beneficial effect. Such data can be used to achieve targeting of non-invasive therapies, e.g., radiation therapy, gamma knife, proton beam therapy.

[0160] One potential use of embodiments of the present invention is to treat patients who have been diagnosed with persistent atrial fibrillation and for whom resection therapy consisting of pulmonary vein isolation has not resulted in complete cessation of their arrhythmia. In these patients, other mapping procedures such as STAR mapping may be used to better target further resection, and this procedure will be improved by embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0161] Embodiments of the present invention will now be described, by way of example only, with reference to the accompanying drawings, in which:

[0162] Figure 1a -d is a schematic diagram showing aspects of determining actuation sites in an embodiment of the present invention;

[0163] Figure 2a -e is a schematic diagram showing aspects of further determining the driver site in an embodiment of the present invention;

[0164] Figure 3a-3f is a diagram illustrating determination and use of a maximum expansion angle in an embodiment of the present invention;

[0165] Figure 4a - b is a schematic diagram illustrating the operation of aspects of an embodiment of the present invention;

[0166] Figure 5 is the cycle length (CL) histogram obtained at the basket catheter electrode;

[0167] Figure 6 is a flow chart showing the study of identified potential AFDs;

[0168] Figure 7 The AF is obtained from images and electrograms of the subject;

[0169] Figure 8 The AC is an image and electrogram obtained from the subject; and

[0170] Fig. 9 A-Cii are images and electrograms obtained from the subject. DETAILED DESCRIPTION

[0171] In the embodiments described below, the system can be used in conjunction with a full chamber basket catheter (Conste llation catheter, Boston Scientific, ltd, US and FIRMap catheter, Abbott, US) to allow simultaneous panoramic left atrium (LA) mapping. However, other suitable catheters / electrodes can also be used to obtain electrogram data without having to collect data from the entire region of interest at the same time. For example, the region of interest can be divided into smaller areas and electrogram data collected sequentially before analysis. The analysis results can then be combined and displayed in a single STAR mapping.

[0172] A system for acquiring and processing electrogram data typically includes one or more multipolar electrographic catheters inserted into a patient's heart chamber (such as the basket catheter mentioned above, as well as other intracardiac catheters, such as a decapolar catheter placed in the coronary sinus), an amplifier and an analog-to-digital (AD) converter, a console including a signal analyzer, a processor and a GPU, a display unit, a control computer unit, and a system for determining and integrating 3D position information of the electrodes.

[0173] The system can use known hardware and software, such as Carto for catheters, 3D electroanatomy integration, and processing units. TM System (Biosense Webster, J&J), NavX Precision TM (Abbott Medical) or Rhythmia TM (Boston Scientific). Catheters, such as "basket" catheters, circular or multi-strip mapping catheters (e.g., Lasso catheters, Biosense Webster, J&J, HD-mapping catheters, Abbott Medical SJM, Pentarray Biosense Webster, J&J), depolarizing catheters or catheters can be used. These systems and catheters are used to collect electrogram signals and corresponding position and time data related to electrical activity at different locations in the heart chambers. These data are transmitted to a processing unit, which performs algorithmic calculations on these data and is intended to convert these data to provide doctors with location information of the areas in the heart that are most likely to cause abnormal heart rhythms to be maintained and continued. Alternatively, the system can use customized catheters, tracking systems, signal amplifiers, control units, computing systems and displays.

[0174] In the above-mentioned patent application, a mapping system is described, hereinafter referred to as the "Stochastic Trajectory Analysis of Sequential Signals (STAR) Mapping" system. Its purpose is to identify the driving sites of arrhythmias, which can be displayed, for example, in the form of a 3D map. A map created using the STAR mapping system is called a "STAR map".

[0175] When performing STAR mapping (and collecting data for embodiments of the present invention), a physician may: place a multipolar panoramic mapping catheter outside the left atrium, collect data for a period of time (e.g., 5 seconds to 5 minutes), then reposition the catheter to ensure close contact with the left atrial septum or anterior wall, and perform another recording. It should be understood that this may be performed in advance and the pre-recorded data processed.

[0176] The ratio of the "leading" electrode will be correlated between maps, so the data and ratio will be able to be displayed on the same map without any problems. In this way, multiple related statistical maps can be built up sequentially by moving the catheter within the chamber and taking further recordings.

[0177] The following are the major steps in the process used to identify "leading" signals (which represent regions / sites that are statistically likely to drive abnormal rhythms in the heart) after acquiring electrogram data (and corresponding spatial and temporal data).

[0178] First, interference and far-field signal components are removed from the input electrogram signal. In one embodiment, the system decomposes the signal into relevant components, such as by spectral analysis, far-field signal blanking, far-field signal subtraction, filtering, or by another method known in the art. Signal components from the chamber of interest in the heart (e.g., atrial signals) are identified. The relative timing of the atrial signals can be established in an explicit, random, or probabilistic manner. In some embodiments, the phase of each signal can be determined and the relative timing can be established based on the relative phase shift between different electrodes.

[0179] Secondly, the timing of signals from adjacent electrodes is paired. Signals are paired with each other only if the electrodes are located within a specified geodesic distance of each other, i.e. only electrodes that are close to each other are paired with each other. This can be further improved by only pairing electrodes that are located on the same side of the chamber wall; i.e. adjacent electrodes on the posterior wall of the heart will be considered adjacent, but not if the electrodes are located on opposite sides of a discontinuity, such as the pulmonary veins, even though the absolute distance between these electrodes may be small. The relative timing of activation at paired electrodes is thus established, and a value is assigned to the "leading" electrode. This pairing can be performed for discrete analysis time periods, typically lasting between 10 milliseconds and 200 milliseconds. The length of the analysis time period does not need to remain constant across all data being analyzed. The aim is to compare the timing between paired electrode activations resulting from the same activation sequence, and the analysis time period can be determined accordingly. For example, each analysis time period can be selected to contain electrode activations that may result from the same activation sequence. Thus, the analysis time periods can overlap with each other.

[0180] Third, this process is repeated multiple times within a given time period (i.e., for a number of analysis time periods, for each pairing). Advantageously, the analysis time periods overlap and are offset relative to the initial analysis time period, e.g., by 10 to 120 seconds. As described above, within a given time period, the analysis time periods may overlap. For example, if the analysis time period is 200 ms, the analysis time periods may overlap by 100 ms, or 50%. In other words, the lead electrode is determined within the first 200 ms time period, and then for the second 200 ms time period, the second time period begins 100 ms after the start of the first time period, and so on, for the given time period. As with the analysis time periods themselves, the degree of overlap may vary from data set to data set. Through this repeated analysis, activation sequences that are repeated less frequently or not repeated at all can be discarded, and activation sequences that occur more frequently can be ranked with higher importance and priority.

[0181] In atrial fibrillation, the activation pattern appears chaotic with frequent changes in wavefront propagation. The relative proportion of the "time" that each site in the activation map preceded each of its neighbors was calculated, thus creating a proportional map of the more frequently "leading" electrode sites.

[0182] Fourth, the proportion of "time" spent "leading" activation in each recording area is calculated. This calculation can be based on determining the actual duration that each electrode leads its paired electrode. Alternatively, it may be the proportion of the total analysis time period (whether the duration of these time periods is the same or different) that the site leads in the mapping activation. Therefore, in some examples, the relative proportion is actually determined by looking at the total number of atrial activation signals seen by a given electrode and determining the proportion of those activation signals that the electrode leads relative to multiple other paired electrodes. Although only mapping adjacent electrodes may cause errors, the system maps all electrodes relative to all other electrodes for each activation cycle to establish the activation direction within the mapping field. The order is analyzed to identify the main activation sequence during the recording and the sites that cause these activations (i.e., the points where the activation is emitted). The activation sequence with trajectories representing local sources and AFD is considered to be mechanically important. The STAR mapping system calculates the ratio of the activation sequence to a given vector to establish a dominant vector (if any) and calculates the time proportion of activation that follows the vector. For all sites in the mapping field, the proportion of mapping activations from the site is calculated to determine its relative importance. These ratios can be referred to as “leading signal scores” and allow for further modification, comparison, and calculations.

[0183] The leading signal scores are normalized so that the ratios can be compared across the heart. Many embodiments of statistical processing, normalization, and subsequent display of this data can be envisioned.

[0184] Electrodes that completely cover passive areas of cardiac activation will tend to have areas that emit little, if any, activation. Only activation sites that frequently "lead" in the activation sequence are given a value that indicates a high probability of being the source of activation. Similarly, when considering overlapping electrode sample locations A and B, and the earliest activation is repeated at the edge of sample A, activation can be seen to travel from B to A, so the early activation sites of A can be considered passive, and the electrodes from sample B are considered to be leading, so more emphasis is placed on the leading electrodes from sample B.

[0185] This process can be repeated over multiple recording sessions and locations to further refine and define repetitive and discard non-repetitive activation patterns, thereby establishing a broader mapping area than would be obtained from a single activation recorded from a multipolar electrode catheter.

[0186] The STAR mapping method can be used in embodiments to identify the predominantly earliest activated electrode sites for a region within a chamber of the heart that has an electrical activation sequence that characterizes the region as a potential driver of an abnormal heart rhythm. TM CARTOFINDER running on the Biosense Webster system (J&J) TM The information can be normalized to form a signal lead score, which can be used in the described methods to provide improved information about the importance of these sites.

[0187] In one embodiment, a computer-implemented method is used to identify one or more regions in the heart that are responsible for supporting or initiating an abnormal heart rhythm. The computer-implemented method uses electrogram data recorded by multiple electrodes of a multi-electrode array on a multi-polar cardiac catheter, the electrogram data being obtained from a corresponding series of sensing locations on the heart over a recording period. The method comprises the following steps:

[0188] Identifying from the electrogram an area within a chamber of the heart that has a sequence of electrical activation that characterizes the area as a potential driver of an abnormal heart rhythm,

[0189] For each sensing location at or substantially surrounding the area,

[0190] For each identified earliest activated electrode site

[0191] For each earliest activated electrode site identified:

[0192] calculating a value for each of a plurality of modifiers associated with the electrode site, the modifiers being determined based on electrogram data of the site, tissue characteristics of the site, or anatomical characteristics of the site;

[0193] Determines the sorting factor calculated by multiple modifiers

[0194] ranking each of the earliest activated electrode sites according to a ranking factor; and

[0195] Data identifying the regions is output, the data varying the significance of each of the determined earliest activated electrode sites depending on the ranking. This is discussed in the example below.

[0196] Figure 1a -d is a schematic diagram showing aspects of the determination of the driver site (the earliest activated electrode site corresponding to potential driver of an abnormal heart rhythm).

[0197] Figure 1a A schematic diagram of the atria of a human heart is shown. The figure shows 3 activation sites that drive atrial fibrillation, represented by circles A, B, and C. These can be identified as driver sites by a variety of methods, such as random trajectory analysis of sequenced signals (STAR ​​mapping). In sequential mapping of arrhythmia wavefronts, the electrogram wavefront trajectory is determined by using a multi-electrode array, represented by the large circle MEA, which surrounds many individual electrode sites including electrode e1. The derived electrogram activation vectors are derived across the MEA, represented here by the arrow lines. In this example, the earliest activation site was determined to be e1. When the MEA moves to a new position, such as Figure 1c As shown in the screen, the electrode site covering the original site e1 is no longer the leading site representing the characteristics of the driving site, and the electrode site e1' is actually generally leading in activation.

[0198] In an embodiment of the invention, an electrode site on the edge of an electrode array acquisition is classified as driven when the site appears to be leading in one acquisition but not leading in another acquisition that overlaps with the leading site in the first acquisition.

[0199] Now consider site e1', which is Figure 1c The picture shows the leading position and actually covers the real driving position ( Figure 1a Here, at Figure 1d The screen shows the execution of the third acquisition, but in this case the electrode (e2') covering site e1' continues to have the characteristics of the signal of the leading electrode, while the edge electrode site e1" has now moved to the following area. The system confirms that the site driving site at e1' (and e2') is now definitely the driving site.

[0200] Continuous acquisition of electrograms performed around the heart chamber in this manner can be used to distinguish true driver sites from apparent driver sites that are passively activated due to conduction from remote driver sites.

[0201] Those skilled in the art will appreciate that it is possible to dynamically perform such sequential mapping and determination of actuation sites during mapping or at any intermediate point once all sites have been mapped. For example, a map can be generated from the acquired data, the locations of the determined potential actuation sites, and potential actuation sites in areas that need to be definitively classified as potential actuation (or not) by further mapping or electrogram acquisition can be highlighted in the map for the user or physician. It is conceivable that such a system could be used to guide a robotically controlled catheter toward and around potential actuation sites for mapping.

[0202] Figure 2a is a diagram of a cardiac chamber (here the left atrium) with two independent actuation sites represented by stars A1 and A2. The general vector of the cardiac activation wavefront is represented by the shaded arrow.

[0203] In an embodiment of the present invention, Figure 2b As shown, a multipolar mapping catheter with electrodes e1 to e10 is placed above AF driver A1. Electrode e2 is activated first, and the activation vector is generally away from the site. The resulting vectors are all toward the e2 electrode with the highest leading signal score (for clarity, only arrows from e2, e3, e5, and e9 are shown). As a result, e2 is highlighted to the user.

[0204] like Figure 2c As shown, the multipolar mapping catheter is then moved to a site away from both activation sources. The "leading signal score" will now be highest at peripheral electrode e9'. Activation is generally in one direction, and generally peripheral electrode e9' leads all other electrodes.

[0205] Figure 2d A simplified tandem mapping, as shown in STAR, is shown, where the electrode with the highest signal lead score in each consecutive acquisition is highlighted. The two sites representing AFD are now displayed as outlines rather than solid shapes. In this example, two electrodes, e2 and e9', are highlighted. It is clear that although electrode e9' is attributed with a high lead signal score, this does not indicate that this electrode indicates the true location of the AFD, unlike e2. The described invention modifies the display of sites by identifying such sites based on synthetic vectors toward the periphery of the acquisition, where the activation order of adjacent acquisitions is consistent (i.e., in approximately the same direction / away from site e9'). Experimental observations also indicate that these sites exhibit differences in other metrics compared to true AFD, such as activation frequency, electrogram voltage, and dominant frequency.

[0206] exist Figure 2e, the mapping obtained by the embodiment is shown, and although the signal lead score is obviously high, e9' has been identified as being unlikely to be an AFD site. Therefore, the visualization of the electrode is modified by reducing the projection size, reducing the color highlight, or similar methods. Site e2 can be further highlighted by increasing the color intensity, size, or other visual modifications.

[0207] In a preferred embodiment, a graphical representation of the mapping of the drive sites is displayed on a display unit.

[0208] In one embodiment, the relative importance of the identified potential AFDs may be further refined by reference to specific features from electrographic characteristics, tissue characteristics, or other criteria (eg, anatomy) of the underlying tissue.

[0209] In this embodiment, electrogram acquisition at different electrode sites throughout the cardiac chamber is performed in groups (g 1- Wait until g x For example, the electrodes on the pentagonal array mapping catheter (e 1 To e 20 ) can be moved to different sites within the left atrium of a patient with atrial fibrillation and perform different sequential electrogram acquisitions at each site.

[0210] From each sequentially acquired electrogram data set, a signal lead score is calculated for each electrode within the group. This can be done, for example, using the STAR method or another statistically based approach to show the proportion of time that activation seen at that electrode precedes activation detected at other electrode sites acquired simultaneously.

[0211] Alternatively, other methods may provide the proportion of activation that activated before other sites, or the proportion of electrodes in the acquisition where that particular electrode was considered the lead, or a mixed index.

[0212] Consider the case where there are 5 electrodes (e1-e5) on a single group acquisition (g1):

[0213]

[0214] For each of these sites, one or more other factors may be calculated, such as the change in cycle length at each electrode or the minimum average cycle length.

[0215] Cycle length metrics were calculated by initially identifying all activations on each electrode in the acquisition.

[0216] To calculate the minimum stable cycle length (Min-CL), the initial activation-to-activation coupling intervals are filtered to remove any activations that are faster or slower than a set point near the average cycle length. For example, activations that are 30% faster or slower than the average CL are ignored. The shortest of the remaining cycle lengths may represent unstable activations, so the shortest 10% of activations are excluded. The value assigned to Min-CL is the minimum of the remaining values, equivalent to the shortest cycle length in the 9th decile of the sorted activation intervals, with the longest cycle length in the 1st decile, and so on.

[0217] For example, each continuous acquisition (here g 1 -g 3 ) has a calculated Min-CL value (in ms) for each electrode site.

[0218]

[0219] Normalization of the min-max feature scaling can be performed across all sites, allowing comparison of min-CL between separate acquisitions. In the case of a modified variable Min-CL, this normalization is performed on the range of values ​​acquired. In this example, the absolute value of Min-CL subtracted from the maximum value of all Min-CLs across all electrodes and acquisitions is normalized to the change in that value.

[0220] For example, for g2e2,

[0221] abs(187ms-201ms)=14ms;

[0222] as well as

[0223] Change of CL = 36ms;

[0224] so

[0225] Normalized min-CL of g2e2 = 14 / 36

[0226] =0.39

[0227] This value can be multiplied by the original signal lead score for that electrode (here 0.2) to give a modified signal lead score, i.e.

[0228] 0.2*0.39=0.05

[0229] At this point, all modified signal lead scores can be normalized again.

[0230]

[0231] In this example, sites g1e3 and g3e5 would be the two sites with the highest scores. In contrast, site g2e5 has a lower leading score because the min-CL value here is much higher than the other sites.

[0232] Weighting can be used for each modified variable before standardization.

[0233] It will be appreciated that this method of providing weights for electrode lead signal scores derived from STAR mapping or other electroanatomical mapping methods can be applied to other electrophysiological phenomena, such as cycle length variations, bipolar or unipolar voltage signals at the site, electrogram duration, or stability of other electrode activation (i.e., low CL variability on other electrodes).

[0234] Further modified variables may be applied from a lookup table, for example relating to anatomical sites which themselves may be manually or automatically marked. Alternatively, they may be calculated from anatomical or tissue measurements derived from, for example, MRI or CT scans.

[0235] Preferably, the geometric sites corresponding to the locations where the highest ranked, normalized modified signal lead score sites were acquired are highlighted, for example by increasing or changing the size, opacity, color or marking of the areas or sites on the cardiac chamber surface presented in the data output.

[0236] In a preferred embodiment, which may be used in addition to or alternative to those listed above, whether a potential AFD is actually a true AFD is determined by reference to activation vectors determined by, for example, STAR mapping.

[0237] As previously described, preferably by grouping (g 1 Wait until g x ) to acquire electrograms sequentially. For each simultaneous set of electrogram acquisitions, an approximate vector of activation is derived by reference to the signal lead score (unmodified) or according to other methods (such as wavefront direction calculations). Electrodes with high signal lead scores that are surrounded by electrodes with lower signal lead scores are likely to represent sites of true AFD. However, electrodes with high signal lead scores that are at the periphery of the acquisition geometry may represent true AFD, or simply passive activation sites that are closest to the AFD in that particular acquisition. Advantageously, it is automatically indicated whether an acquisition is likely to be a passive activation site.

[0238] To determine whether the leading electrode site in the acquisition (e.g., e1) is likely to be a true AFD, the electrode with the highest signal leading score in the set of acquisitions is identified. A tangent plane to the geometric surface of the chamber is created at the midpoint of the electrogram acquisition site. It is advantageous to use a highly smoothed representation of the chamber geometry to prevent plane internalization. The in-plane angle distribution from any bisection of e1 is then calculated, and the in-plane angle distribution (expansion angle) of the electrodes surrounding this point is determined. The absence of an angular separation greater than a predetermined angle (e.g., 120°) between the surrounding electrodes indicates that e1 is likely to be a true AFD.

[0239] Therefore, a modification factor, which may be determined by a lookup table or as a preselected value, is applied to the leading electrode site identified as AFD. Alternatively, the maximum angular separation between electrodes at the time of acquisition may be inversely rescaled (1-(min-max normalization)) and used as the modified variable.

[0240] For example, consider the case where e2 is surrounded by 4 electrodes when acquiring g1, and the consecutive angular separations of these electrodes are 93°, 65°, 119°, and 83°. Therefore, the maximum angle acquired in this example is 119°. The maximum spread angle acquired, such as >170°, means that the potential AFD site is at the edge of its electrode group.

[0241] In this example, three additional acquired sites (g2 to g4) provide maximum angular separations of 174°, 120°, and 240° for each acquisition.

[0242] These angles can be normalized to provide a modification factor that can be applied to each of the most likely AFDs seen at each acquisition by weighting. In this example, 1, 0.55, 0.99, and 0 can come from a standard max-min normalization. Using the maximum possible separation (360°) rather than the measured maximum angular separation provides non-zero normalized modification factors, in this example: 1, 0.77, 0.99, and 0.49.

[0243] In the event that the maximum spread angle of a potential AFD site is greater than 170°, then it must be determined whether the site is passively active. This can be performed by referencing other acquisitions that occurred within a predetermined geodesic distance of the potential AFD and within the maximum spread angle arc.

[0244] Determine passive activation

[0245] Electrodes located at the periphery of simultaneously acquired potential AFD sites can be classified as possible true AFD or passive activation sites by reference to the activation order or average wavefront vector of the individual acquisitions close to the site covered within the arc of maximum expansion angle. Preferably, these are within a certain geodesic distance of the AFD site in question, typically within a pre-set distance, such as 3 cm.

[0246] For example, electrode acquisition g1e1 is located at the periphery of the acquisition and is identified as a potential AFD. A further acquisition g2 is performed within 3 cm and within the maximum extension angle arc and the calculation is applied. If g1e1 is passively activated, the signal lead score of the nearest electrode on g2 is expected to be low. This can define a signal lead score on one (or more) of the nearest electrodes of g2 (e.g. g2e1) that is 50% lower than all signal lead scores of g2. In addition, the activation vectors of these electrodes will be oriented away from g1e1, i.e., located on a plane tangent, e.g., between 90° and 270°, where the vector from g2e1 to g1e1 is taken as 0°.

[0247] If both of these conditions are met, G1e1 can be defined as an absolutely passively activated site. A modification factor can be applied to reduce its signal lead score, thereby modifying G1el by reducing its significance in the calculations. This modification factor can be arbitrary based on a lookup table or prior investigations, and can be applied to a single electrode site (G1e1) or to all electrodes across sequential acquisitions (e.g. G1).

[0248] An iterative process may then be performed in which the signal lead scores are recalculated after redistribution and normalization of the signal lead scores.

[0249] However, if g2e1 and / or its neighboring signal lead scores are high (e.g., within the top 75% of lead signal scores that activate g2), and the activation vector is oriented toward site g1e1 (e.g., between -90° and +90° on the plane tangent inter-electrode vector), true AFD drive may be located at or very close to both electrode sites. A positive modification factor can be applied, and the region between g2e1 and g1e1 can be visually highlighted.

[0250] There may be a situation where an electrode with a high signal lead score is at the edge of the acquisition and no acquisition is made within the maximum spread angular arc and the defined geodesic distance. In this case, the area may be highlighted on the display to indicate that further acquisitions should be made in this area. This may be done by highlighting with a different color or by indicating with an arrow, pointer, or other animation the area where further acquisitions should be performed.

[0251] During such an acquisition, an indicator may be displayed to indicate whether sufficient electrographic data has been acquired. At its simplest, this may be a simple timer, but other indicators may count the number of electrographic activations on each electrode and preset a target number by reference to a predetermined value.

[0252] Figure 3a-3e is a diagram illustrating the determination and use of a maximum expansion angle in an embodiment of the present invention.

[0253] Figure 3a A multipolar mapping catheter with 6 electrodes labeled e1 to e6 is shown. In this example, electrode e6 has a higher signal lead score than the other electrodes. The activation vectors between this electrode and the surrounding electrodes are drawn by solid arrows, which are projected onto a cut plane representing the smoothed cardiac chamber surface at e6. There are angles between these vectors, such as the two vectors from e6 to e1 and from e6 to e5, where the angle is given by In this example, e6 is surrounded by electrodes, which can be called the maximum angle of maximum expansion angle. is less than a preset maximum, e.g. 120°. This confirms the e6 as an AFD, so a modification factor can be applied.

[0254] Figure 3b Now the same electrode arrangement is shown moved to another position. Here the potential AFD, i.e. the electrode with the highest signal lead score, is e1. However, in this example the maximum inter-electrode angle is now It is greater than a set amount (eg 170°), thereby confirming this as an activated peripheral point.

[0255] Figure 3c The diagram shows a set diameter (e.g. 3 cm) and a maximum expansion angle (in this case ) can be considered as the shaded area. This may completely cover the angle Or possibly smaller and centered with it. The closest two or more electrode activations from separate grouping acquisitions located within that arc are then considered.

[0256] like Figure 3d As shown, the closest two or more electrode activations from the separate grouped acquisitions (g2e1-g2e6) located within the arc are then considered. In this example, the two closest acquisitions, g2e4 and g2e3, both have low signal leading scores and are not potential AFDs. In addition, the average activation vector represented by the thick solid arrow is toward e1 (rather than away from e1, as would be expected if e1 were a true AFD). This confirms that e1 in this example is not an AFD and a modification factor can be applied to it to reduce its visual significance.

[0257] Figure 3e Another case is shown where one of the nearest electrodes in a further acquisition (g3e4) has a high signal lead score and the vector from this electrode is in the opposite direction to the vector from e1. This indicates that there is indeed an AFD near these two electrodes and the area between them can be highlighted (e.g., here with a striped circle).

[0258] Figure 4aa and b show how data from other electrogram analyses or from imaging techniques can be used in conjunction with the regions identified by electrogram vector mapping to better classify and rank the importance of the identified regions. In this example, regions A, B, and C are first identified using a mapping technique, such as STAR mapping; data sets that may indicate the likelihood of regions being important for maintaining an arrhythmia are further combined and used to rank the identified driver sites. One way of doing this is to use the regions to provide normalized modification factors that are then applied to the corresponding driver sites. Several modification factors may be applied to each identified driver site to give a final possible ranking or display of the respective importance of all driver sites identified by the original mapping method. Data such as from historical patient data may be further applied, and machine learning algorithms may be used to determine the exact modification factors to use and the ranking in which to apply them.

[0259] like Figure 4a As shown, data collected from electroanatomical mapping can be used to identify potential driver sites through activation sequence mapping (such as STAR mapping), and can also be used to analyze other electrical properties of these sites (i.e., analyzing the electrogram characteristics of these areas). If mapping has occurred sequentially, another rule-based approach as detailed in Figure 1 can be used to remove driver sites that are actually passively activated. These resulting data can be further combined with other information about the patient's heart under study, such as imaging-derived indicators, from which imaging data will produce modification factors that can be normalized, weighted and used to modify the importance attributed to each identified driver site. Such data can be cardiac MRI data including measurements such as percent scar, wall thickness or tissue edema, which itself can be composed of enhancement measures with contrast agents, such as late gadolinium enhancement. Another source of modification information can refer to historical data, which can be coupled with machine learning techniques to further derive modification factors for each identified potential driver site.

[0260] Figure 4b The flow of data through the proposed device is shown. The electrogram data is acquired via a multi-polar mapping catheter connected to a cardiac mapping system, which can be incorporated into or separated from a 3D electroanatomical mapping system. It should be understood that instead of using an intracardiac catheter to collect data regarding the cardiac electrogram, the surface electrogram can be used by inverse solution to obtain a "virtual" electrogram on a representation of the surface of the cardiac chambers. The data is then transmitted to an analysis module, which can exist as a separate computer or be integrated into the electroanatomical mapping system. Here, the analysis is performed before being integrated with the 3D anatomical representation of the heart and displayed to a user, such as a physician. Figure 4a The data integration and processing described.

[0261] Figure 5A histogram of cycle lengths (CL) obtained at one of the basket catheter electrodes is shown, with the percentage recorded consisting of each CL on the y-axis and the CL on the x-axis. Each bar represents a defined CL. For illustrative purposes, CLs with lower frequencies along the mean are excluded. Ai shows the Min-CL at 131ms, which also represents the main CL. According to part of the method, the CLs at 102ms and 105ms are excluded because they represent CLs that are greater than 30% of the average CL recorded at the electrode and only represent less than 10% of the total recorded CL. Aii shows the Min-CL at 135ms, while the main CL is 142ms. Similarly, according to part of the above method, the CLs at 105ms and 109ms are excluded.

[0262] Figure 6 is a flow chart of the study showing the potential AFDs identified and those associated with resection response, and how many of them co-localized with sites of Min-CL, lowest CLV, regional DF gradient, and LVZ.

[0263] Figure 7 AF is an image and electrogram obtained from a subject (patient ID 6). Figure 7 A) STAR mapping of the LA in the captioned top view showing potential AFD mapped to the mid-apical region. Figure 7 B), the measured CL graph in the top view in the figure title shows the overall fastest Min-CL at the measured potential AFD. Figure 7 C) CLV mapping in the captioned top view shows the lowest CLV at the mapped potential AFD. Figure 7 D) shows the potential AFD (highlighted by asterisks) specified by the STAR mapping method and the electrograms obtained at the adjacent electrodes. The electrograms obtained at the potential AFD indicate that this site is in the lead and has a faster CL than its adjacent paired site. Figure 7 E) CARTO mapping of the LA in top view, demonstrating resection at the potential AFD guided by STAR mapping. Figure 7 F) Electrocardiogram shows that AF terminates to sinus rhythm when the underlying AFD is resected.

[0264] LUPV - left superior pulmonary vein

[0265] RUPV - right superior pulmonary vein

[0266] LAA - Left Atrial Appendage

[0267] MVA - Mitral Valve Annulus

[0268] Figure 8AC are images from a different subject (patient 16). In FIG. 8A ), a potential AFD is shown in the STAR mapping of the LA in the captioned lateral view. Figure 8 B) As shown in the CARTO LA mapping in the captioned lateral view, resection performed at this site results in AF to AT termination. Figure 8 C) Potential AFD co-localizes with the site of the fastest regional CL that receives reduced CL from neighboring electrodes.

[0269] LUPV - left superior pulmonary vein

[0270] LAA - Left Atrial Appendage

[0271] MVA - Mitral Valve Annulus

[0272] Fig. 9 AC is an image from yet another subject (patient ID 18). Fig. 9 In A), STAR mapping of the LA in lateral view shows potential AFD. Fig. 9 B) Potential AFD co-localizes with the site of regionally highest DF, with a reduction in DF obtained from neighboring electrodes. Fig. 9 Ci-ii) As shown in the electrograms obtained from BARD, ablation at the potential AFD resulted in a slowing of the CL from 164 ms to 229 ms.

[0273] LUPV - left superior pulmonary vein

[0274] LAA - Left Atrial Appendage

[0275] MVA - Mitral Valve Annulus

[0276] Experimental Results

[0277] The inventors performed experiments as described below to further refine and develop the STAR mapping and to establish a series of parameters that can be used as modifying factors to determine other factors by which potential driver sites can be classified according to their importance or likelihood of being true driver sites.

[0278] Parameter Development Method

[0279] All patients had high-density bipolar voltage mapping created using a PentaRay Nay catheter with a 2-6-2 mm interelectrode spacing. Points ≥3 mm from the geometric surface were filtered out because of the lack of contact with the myocardium, and the acquired points were respiratory-gated to optimize the accuracy of anatomical localization.

[0280] Parameter Check

[0281] Bipolar Voltage and AFD

[0282] At least 800 bipolar voltage points were collected per patient to ensure adequate atrial coverage. The interpolation threshold for surface color projection was set to 5 mm, and points were collected to achieve complete LA coverage (i.e., an area no larger than 5 mm from a data point).

[0283] Areas with bipolar voltage <0.5 mV were defined as low-voltage zones (LVZs). Voltage maps were divided into non-LVZ and low-voltage zones. Identified potential AF drivers were then classified as present in the non-LVZ or LVZ. The relationship between potential AFD mapped to the LVZ and the achievement of AF termination during resection was assessed. The relationship between the proportion of the LVZ and the number of AFDs identified was also assessed.

[0284] Spectral Analysis and AFD

[0285] CL was determined at each electrode pole of the contact in each patient's 5-minute recording using unipolar signals recorded from a basket catheter. CL was taken as the time difference between two consecutive atrial signals using an automated custom-written Matlab measurement script. A custom algorithm was used to model reasonable biological behavior based on a well-described refractory period. The algorithm was used to avoid double counting of segmented electrograms. Unipolar activation timing was considered as the maximum negative deflection (peak negative dv / dt).

[0286] Histograms of all CLs identified on the x-axis (rounded to the nearest integer millisecond) and the percentage of the recording formed by each CL on the y-axis were plotted for each 5-minute recording for each individual electrode in each patient. Different approaches have been used previously to identify sites of fast activity from atrial CL measurements. One approach uses a histogram of CLs and takes the center of the narrowest range of CLs in the histogram that contains 50% of the cycles and defines this as the “dominant CL”. The dominant CL for each electrode was then projected onto a replicate of the anatomical geometry to identify the site of the fastest dominant CL, as defined as values ​​in the top decile, and their spatial relationship to the AFD is assessed.

[0287] For the new method of determining the site of the fastest CL defined as the minimum CL (Min-CL), the initial extreme CL outliers were ignored, which were defined as CLs that were 30% slower or faster than the average CL at the electrode and those that contained <10% of the cycles. From the remaining CLs, the Min-CL for each electrode was identified (Figure 1). Therefore, after removing the outliers, the Min-CL is the shortest CL that constitutes >10% of the cycles. The Min-CL was compared between all electrodes to identify the site of the overall fastest Min-CL in the LA. Two definitions of the overall fastest Min-CL were tested: i) the Min-CL within the top decile of all electrodes and / or ii) the shortest one or "absolute Min-CL" among the overall Min-CLs and any other point with a Min-CL within 5% of the overall fastest Min-CL. The location of the electrode with the fastest overall Min-CL was then identified on the same STAR map that displays the ESA to ensure accurate anatomical correlation.

[0288] CL variability (CLV) was used as a marker of the tissue. The CLV of each electrode was determined by taking the standard deviation (SD) of the CL. Therefore, a smaller CLV indicates a smaller CL variation. The CLV was compared between all electrodes to identify the site with the overall lowest CLV in the LA. The site with the lowest CLV was defined as i) the CLV in the lowest decile and / or ii) the CLV was within 5% of the overall lowest CLV, referred to as the absolute lowest CLV. The location of the electrode with the lowest CLV was then identified for STAR mapping and again correlated with the AFD.

[0289] Regional CL and frequency gradient at potential AFD

[0290] The Min-CL at the potential AFD determined using the STAR mapping method was compared with the Min-CL obtained at the adjacent electrodes to determine whether there was a CL gradient from the potential AFD to the surrounding area. This was repeated for all potential AFDs identified by each STAR mapping in each patient. Adjacent electrodes were defined as electrodes within 3 cm of the potential AFD.

[0291] To determine the DF, a Butterworth second-order filter was applied to the unipolar signal after filtering the far-field ventricular signal. After applying a low-pass filter to the signal, it was rectified to obtain the absolute value of the signal. A Hamming window was then applied for Fourier transformation. The DF for each four-second window was then determined. The median of all these values ​​within 5 minutes of obtaining the DF at the recording electrode was determined. The DF at the electrode identified as a potential AFD was compared with the DF obtained at the adjacent electrode to determine whether there was a high- and low-frequency gradient from the potential AFD to the surrounding area. This was repeated for all potential AFDs identified by each STAR mapping for each patient. The relationship between the LA sites with the overall highest DF (based on those values ​​in the highest decile) and the potential AFD was also evaluated.

[0292] Summarize the results

[0293] Thirty-two patients were included, and 83 potential AFDs identified by STAR mapping were resected. Resection responses were observed in 73 sites (24 AF terminations and 49 CL slowing ≥30ms). Of the 73 sites, 54 (74.0%) were located at the same site as the overall fastest CL, and 55 (75.3%) were located at the same site as the lowest CLV. However, when conventional markers were used, sites with the fastest main CL and the highest DF were rarely co-located with potential AFDs with resection responses (39, 53.4% ​​CL and 41, 56.2% DF). At potential AFDs, PVI did not affect CL (131.0±12.1ms before PVI vs. 131.2±15.5ms; P=0.96 after PVI) or CLV (10.3±3.9ms before PVI vs. 11.0±5.4ms after PVI; p=0.80). These potential AFDs also frequently demonstrated regional CL (61 / 73, 83.6%) and frequency gradients (58 / 73, 80.8%). Potential AFDs were more commonly localized to the LVZ, and the proportion of the LVZ correlated with the number of potential AFDs identified (rs = 0.91; p < 0.001). The use of these novel markers of rapidity and organization in conjunction with STAR mapping allowed for high sensitivity and specificity in predicting which potential AFDs would respond to resection. Potential AFDs co-localized with sites with the lowest CLV (24 / 24 (100%) vs. 31 / 49 (63.2%); p < 0.001) and fastest CL (24 / 24 (100%) vs. 30 / 49 (61.2%); p < 0.001) were consistently associated with AF termination at resection, rather than with slowing CL.

[0294] Detailed results

[0295] Thirty-two patients underwent STAR mapping-guided resection. The mean AF duration was 15.4±4.3 months, and 21 of the 32 patients (65.6%) received antiarrhythmic drugs pre-resection.

[0296] Potential AFD Resection response

[0297] In brief, in 32 patients, 92 potential AFDs were identified on STAR mapping after PVI (2.8 ± 0.8 per patient), of which 83 (90.2%, 2.6 ± 0.7 per patient) were resected (Figure 3). The 9 potential AFDs that were not resected occurred in patients in whom resection of the previous site resulted in AF termination.

[0298] 73 potential AFDs (2.3 ± 0.6 per patient) achieved an ablation response, including at least one response in all 32 patients. On a per potential AFD basis, AF termination was achieved by ablation at 24 sites (18 tissue to AT, and 6 termination to sinus rhythm), and 49 sites achieved a CL slowing of ≥ 30 ms. On a per patient basis, AF termination was achieved in 24 patients, and a CL slowing of ≥ 30 ms was achieved in the remaining 8 patients.

[0299] Bipolar voltage and potential AFD

[0300] On a per-patient basis, an average of 3.4 ± 0.7 well-defined LVZs were identified. The majority of identified potential AFDs were mapped to the LVZ (62 / 92, 67.4%). Of the 83 potential AFDs that were resected, 60 were mapped to the LVZ (72.3%), of which 59 (98.3%) were associated with resection response.

[0301] Patients with more than 50% of the LA consisting of the LVZ (59.2 ± 6.5 mV) were more likely to have more than 2 potential AFDs identified. There was a strong positive correlation between the proportion of the LVZ present and the number of potential AFDs identified (r s =0.91; p<0.001). However, LVZ alone had little predictive value in predicting the site of AFD.

[0302] The association of latent AFD with the LVZ was highly predictive of resection response. Latent AFD mapping to the LVZ was more frequently associated with AF termination at resection than latent AFD mapping to the non-LVZ (odds ratio = 20.0, 95% CI 1.1-351.5; p = 0.04).

[0303] Spectral analysis and Potential AFD

[0304] For the spectral analysis, a total of 170 5-min monopolar recordings were used. Of these 170 recordings, 84 were created before PVI and 86 were created after PVI. The mean Main-CL and mean Min-CL obtained at the potential AFD after PVI were 137.9 ± 64.2 ms and 131.2 ± 15.5 ms, respectively.

[0305] Co-localization of sites identified in spectral analysis and potential AFDs

[0306] i) Fastest major CL

[0307] Of the 92 potential AFDs identified after PVI, 47 (51.1%) co-localized with the site of the fastest major CL. The major CL showed a sensitivity of 51.1% (95% CI 40.4-61.7%) and a specificity of 18.6% (95% CI 8.4-33.4%) in predicting AFD. The forward and reverse predictive values ​​were 57.3% (95% CI 51.2-63.2%) and 15.1% (95% CI 8.4-25.6%), respectively.

[0308] ii) Min-CL sites within the highest decile and absolute Min-CL

[0309] On a per-patient basis, an average of 4.1 ± 1.1 Min-CL sites were identified in the top decile. Of the 92 potential AFDs identified after PVI, 58 (63.0%) co-localized with one of these sites. Based on the Min-CL sites in the top decile, it showed a sensitivity of 63.0% (95% CI 52.3-72.9) and a specificity of 20.4% (95% CI 10.2-34.3) in identifying potential AFDs.

[0310] On a per-patient basis, an average of 2.5 ± 0.9 absolute Min-CL sites were identified that were within 5% of the fastest Min-CL sites. Of the 92 potential AFDs identified after PVI, 56 (60.9%) co-localized with one of these sites.

[0311] iii) The lowest CLV point within the lowest decile and the absolute lowest CLV

[0312] On a per-patient basis, an average of 3.8 ± 1.0 lowest CLV sites were identified in the lowest decile. Of the 92 potential AFDs identified after PVI, 61 (66.3%) co-localized with one of these sites. Based on the lowest CLV site in the lowest decile, the lowest CLV showed a sensitivity of 66.3% (95% CI 55.7-75.8) and a specificity of 24.4% (95% CI 12.4-40.3) in identifying potential AFDs.

[0313] On a per-patient basis, an average of 2.3 ± 0.8 lowest CLV sites were identified that were within 5% of the overall lowest CLV sites. Of the 92 potential AFDs identified after PVI, 60 (65.2%) co-localized with one of these sites.

[0314] Using spectral analysis to predict ablation response at potential AFD

[0315] i) Fastest major CL

[0316] Of the 73 potential AFDs associated with response, only 39 (53.4%) co-localized with the site of the fastest major CL resection. The fastest major CL showed a sensitivity of 53.4% ​​(95% CI 41.4-65.2%) and a specificity of 26.3% (95% CI 13.4-43.1%) in predicting potential AFD. The predictive values ​​of the forward and reverse directions were 58.2% (95% CI 51.1-65.0%) and 22.7% (95% CI 14.1-34.6%), respectively.

[0317] ii) Min-CL sites within the highest decile and absolute Min-CL

[0318] Of the 73 potential AFDs with study-defined excision responses, 54 (74.0%) co-localized with one of the Min-CL sites ( FIGS. 3 and 4A-F ).

[0319] Co-localization with these sites was again shown to demonstrate high diagnostic accuracy in predicting potential AFD that would respond to resection.

[0320] iii) The lowest CLV point within the lowest decile and the absolute lowest CLV

[0321] Of the 73 potential AFDs with study-defined resection responses, 56 (76.7%) co-localized with one of the lowest CLV sites defined within the lowest decile ( Figure 5 and Figure 6 AF).

[0322] Of the 73 potential AFDs associated with excision response, 55 (75.3%) co-localized with one of the absolute lowest CLV sites (Figs. Figure 6 AF). Again, co-localization with these sites was shown to demonstrate high diagnostic accuracy in predicting potential AFD that would respond to resection.

[0323] iv) Predicting AF termination

[0324] Potential AFD co-localized with Min-CL sites according to either definition were more frequently associated with AF termination at resection than CL slowing (24 / 24 (100%) vs. 29 / 49 (59.2%); p<0.001). The marker showed an odds ratio of 34.1 (95% CI 2.0-592.3; P=0.02) for predicting AF termination at resection at potential AFD.

[0325] Compared with CL slowing, potential AFD co-localized with the lowest CLV site according to either definition was more frequently associated with AF termination at the time of resection (24 / 24 (100%) vs. 31 / 49 (63.2%); p<0.001). The marker showed an odds ratio of 28.8 (95% CI 1.7-501.6; p=0.02) for predicting AF termination at the time of resection at the potential AFD.

[0326] Regional CL and frequency gradients and Potential AFD

[0327] Among the 92 potential AFDs identified, 56 potential AFDs (60.9%) had a clear gradient in min-CL from the potential AFD to the surrounding pole ( Figure 5 and Figure 8 AD). Of the 73 potential AFDs that responded to resection, 61 (83.6%) showed a fastest-slowest Min-CL gradient from the potential AFD to the adjacent pole. The average reduction in Min-CL from the potential AFD to the adjacent pole within 3 cm was 10.5±4.2ms (p=0.01). The presence of a regional CL gradient at the potential AFD showed an odds ratio of 5.1 (95% CI 1.3-20.3, p=0.02) in predicting a potential AFD that responded to resection.

[0328] The mean DF of potential AFDs was 6.2 ± 0.7 Hz. In 56.5% (52 / 92) of cases, potential AFDs were co-localized with the site of highest DF in LA only, of which 41 of 73 (56.2%) with ablation responses were co-localized with the site of highest DF. In 59 of the 92 potential AFDs identified (64.1%), there was a clear frequency gradient from the potential AFD to the adjacent pole ( Figure 5 and Figure 7AD). The average reduction in DF from the potential AFD to the adjacent pole was 1.8±0.7 Hz (p=0.01). When only the 73 potential AFDs that responded to resection were examined, 58 (80.8%) exhibited a frequency gradient. The presence of a regional frequency gradient at the potential AFD showed an odds ratio of 5.8 (95% CI 1.4-23.2, p=0.01) in predicting potential AFDs that responded to resection. There was a non-significant difference in the frequency gradient in the case of AF termination versus the frequency in the case of CL slowing during resection (18 / 24, 75.0% vs. 39 / 49, 79.6%; p=0.78).

[0329] Effect of PVI on Spectral Analysis Data

[0330] Of the 92 potential AFDs identified post-PVI, 42 were also detected on pre-PVI maps, of which 39 (92.9%) were associated with study-defined resection responses. Mean CLV pre-PVI (obtained by taking the mean CLV of all electrodes) was lower in patients with potential AFD identified pre-PVI compared with patients with potential AFD identified only post-PVI (31.4±4.8ms vs. 49.5±7.3ms; p=0.01).

[0331] At potential AFD, PVI did not affect CL (131.0±12.1 ms pre-PVI vs. 131.2±15.5 ms; P=0.96 post-PVI) and CLV (10.3±3.9 ms pre-PVI vs. 11.0±5.4 ms post-PVI; p=0.80).

[0332] Potential AFD also identified before PVI showed 53.4% ​​(95% CI 41.4-65.2) sensitivity and 90.0% (95% CI 55.5-99.8) specificity in predicting potential AFD with resection response. The forward and reverse predictive values ​​were 97.5% (95% CI 85.7-99.6) and 20.9% (95% CI 16.1-26.7), respectively.

[0333] It should be understood that some embodiments of the present invention as described below can be incorporated as code (e.g., software algorithm or program), which resides on firmware and / or computer-usable media with control logic to enable execution on a computer system with a computer processor. Such a computer system generally includes a memory storage unit, which is configured to provide output from executing the code, and the code configures the processor according to execution. Code can be arranged as firmware or software, and can be organized into a group of modules, such as discrete code modules, function calls, procedure calls, or objects in an object-oriented programming environment. If implemented using modules, the code can include a single module or multiple modules that operate in collaboration with each other.

[0334] Optional embodiments of the present invention may be understood to include the parts, elements and features mentioned or indicated herein, individually or collectively in any or all combinations of two or more parts, elements or features, and wherein, where specific integers are mentioned herein, if they have known equivalents in the art to which the present invention relates, these known equivalents are deemed to be incorporated herein as if they were individually set forth.

[0335] Although the illustrated embodiments of the present invention have been described, it should be understood that those skilled in the art may make various changes, substitutions and alterations without departing from the invention as defined by the following claims and their equivalents.

[0336] This application claims priority from GB 1915680.1, the contents of which and the contents of the abstract filed herewith are incorporated herein by reference.

Claims

1. A computer-implemented method for identifying one or more regions of the heart responsible for supporting or initiating an abnormal heart rhythm, the computer-implemented method using electrogram data recorded from a plurality of electrodes on a multipolar cardiac catheter, the electrogram data being obtained from a corresponding series of sensing locations on the heart over a recording period, the method The following steps are involved: identifying, based on the electrogram, an area within a chamber of the heart having a sequence of electrical activation that characterizes the area as a potential driver of an abnormal heart rhythm; for each sensing location at or substantially surrounding the region, determining an earliest activated electrode site based on the predominant activation; For each earliest activated electrode site identified: calculating a value for each of a plurality of modifiers associated with the electrode site, the modifiers being determined based on electrogram data for the site; determining a ranking factor calculated based on the plurality of modifiers; sorting each of the earliest activated electrode sites according to a sorting factor; as well as outputting data identifying the regions, the data varying the significance of each of the determined earliest activated electrode sites depending on the ranking, Wherein, the modifier determined according to the electrogram data includes: The minimum cycle length of the electrogram recorded at the electrode, the activation frequency gradient between the electrogram recorded at the electrode or within the region and the electrogram obtained within a predetermined geodesic distance, and the average voltage of the local electrogram recorded within the region.

2. The computer-implemented method of claim 1, in, Outputting the data includes displaying the data with a visual indication that changes the significance of each of the determined earliest activated electrode sites depending on the ranking.

3. A computer-implemented method for identifying one or more regions of the heart responsible for supporting or initiating an abnormal heart rhythm, the computer-implemented method using electrogram data recorded from a plurality of electrodes on a multipolar cardiac catheter, the electrogram data being obtained from a corresponding series of sensing locations on the heart over a recording period, the method The following steps are involved: identifying, based on the electrogram, an area within a chamber of the heart having a sequence of electrical activation that characterizes the area as a potential driver of an abnormal heart rhythm; for each sensing location at or substantially surrounding the region, determining an earliest activated electrode site based on the predominant activation; For each earliest activated electrode site identified: calculating a value for each of a plurality of modifiers associated with the electrode site, the modifiers being determined based on electrogram data for the site; determining a ranking factor calculated based on the plurality of modifiers; sorting each of the earliest activated electrode sites according to a sorting factor; as well as outputting data identifying the regions, the data varying the significance of each of the determined earliest activated electrode sites depending on the ranking, where the modifier is determined based on the treatment outcome at the other earliest activated electrode site, Wherein, the modifier determined according to the electrogram data includes: The minimum cycle length of the electrogram recorded at the electrode, the activation frequency gradient between the electrogram recorded at the electrode or within the region and the electrogram obtained within a predetermined geodesic distance, and the average voltage of the local electrogram recorded within the region.

4. A computer-implemented method for identifying one or more regions of the heart responsible for supporting or initiating an abnormal heart rhythm, the computer-implemented method using electrogram data recorded from a plurality of electrodes on a multipolar cardiac catheter, the electrogram data being obtained from a corresponding series of sensing locations on the heart over a recording period, the method The following steps are involved: identifying, based on the electrogram, an area within a chamber of the heart having a sequence of electrical activation that characterizes the area as a potential driver of an abnormal heart rhythm; for each sensing location at or substantially surrounding the region, determining an earliest activated electrode site based on the predominant activation; For each earliest activated electrode site identified: calculating a value for each of a plurality of modifiers associated with the electrode site, the modifiers being determined based on electrogram data for the site; Determines a ranking factor calculated based on the multiple modifiers: sorting each of the earliest activated electrode sites according to a sorting factor; as well as outputting data identifying the regions, the data varying the significance of each of the determined earliest activated electrode sites depending on the ranking, Wherein, if the angle from the vertex opposite to the potential driving site to at least two electrodes is less than a predetermined angle and activation occurs across the at least two electrodes, the potential driving site is classified as the earliest activated electrode site, and At least one electrographic activation is determined to be later than the potential driver site within a defined arc of excitable tissue, Wherein, the modifier determined according to the electrogram data includes: The minimum cycle length of the electrogram recorded at the electrode, the activation frequency gradient between the electrogram recorded at the electrode or within the region and the electrogram obtained within a predetermined geodesic distance, and the average voltage of the local electrogram recorded within the region.

5. The computer-implemented method of claim 4, in, The predetermined angle is smaller than 180 degrees.

6. The computer-implemented method of claim 4, further comprising: include: The data is output to a display or medical scanning device to cause display or navigation of chamber geometry and guide placement of catheters or electrodes for subsequent electrographic data acquisition.

7. The computer-implemented method of claim 6, further comprising: include: Sites for subsequent placement of the catheter or electrodes are identified to eliminate or confirm a previously determined first activated electrode, or to extend the electrogram data to a previously unscanned or incompletely scanned area.

8. The computer-implemented method of claim 7, further comprising: include: The sites are identified based on the location of the first activation site already identified or based on the gradient in the activation vector and the signal lead score.

9. The computer-implemented method of claim 4, further comprising: include: Electrogram data is received in substantially real time and an indication is provided as to when adequate acquisition timing has been achieved for the corresponding site.

10. The computer-implemented method of claim 9, in, The step of determining whether sufficient acquisition timing has been performed includes one or more of the following: Counting down a predetermined time period, acquiring a preset number of activation cycles for at least a minimum preset number of electrodes on the flowing multipolar mapping catheter, and acquiring a predetermined time or a predetermined number of activation cycles, wherein the activation pattern across the plurality of electrodes remains within a pattern matched sequence.

11. A computer-implemented method according to claim 9 or 10, in, The step of determining whether sufficient acquisition timing has occurred includes determining that the activation pattern of the site being scanned has reached a predetermined level of statistical certainty.

12. A computer-implemented method for identifying one or more regions of the heart responsible for supporting or initiating an abnormal heart rhythm, the computer-implemented method using electrogram data recorded from a plurality of electrodes on a multipolar cardiac catheter, the electrogram data being obtained from a corresponding series of sensing locations on the heart over a recording period, the method The following steps are involved: identifying, based on the electrogram, an area within a chamber of the heart having a sequence of electrical activation that characterizes the area as a potential driver of an abnormal heart rhythm; for each sensing location at or substantially surrounding the region, determining an earliest activated electrode site based on the predominant activation; For each earliest activated electrode site identified: calculating a value for each of a plurality of modifiers associated with the electrode site, the modifiers being determined based on electrogram data for the site; determining a ranking factor calculated based on the plurality of modifiers; sorting each of the earliest activated electrode sites according to a sorting factor; as well as outputting data identifying the regions, the data varying the significance of each of the determined earliest activated electrode sites depending on the ranking, Also included is: deriving a wavefront direction by determining which electrode in each electrode pair is leading, and processing the wavefront direction to determine whether the earliest activated electrode is a true AFD or represents a passive activation site that is activated beyond the measurement boundary, Wherein, the modifier determined according to the electrogram data includes: The minimum cycle length of the electrogram recorded at the electrode, the activation frequency gradient between the electrogram recorded at the electrode or within the region and the electrogram obtained within a predetermined geodesic distance, and the average voltage of the local electrogram recorded within the region.

13. A computer-implemented method for identifying one or more regions of the heart responsible for supporting or initiating an abnormal heart rhythm by analyzing electrograms acquired from the heart, the computer-implemented method using electrogram data recorded from a plurality of electrodes on a multipolar cardiac catheter, the electrogram data being obtained from a corresponding series of sensing locations on the heart over a recording period, the method The following steps are involved: By analyzing the sequence of electrical activation, specific regions within the heart's chambers are defined as potential drivers of abnormal heart rhythms; Classification of areas as potential drivers was weighted based on factors including; During the same acquisition period, the direction of the wavefront generated by the activation travels from the potential driving site to one or more nearby electrodes relative to the driving site, The sequence and timing of activation are within a biologically plausible manner, with reference to conduction velocities and pathways within a plausible activation sequence, The angle from the vertex subtended by the potential actuation site to at least two electrodes is less than a predetermined angle and activation occurs across the at least two electrodes, and At least one electrographic activation was determined to be later than the potential driver site within a defined arc of excitable tissue, and Further acquisitions performed at adjacent locations subtending the missing arc of the first potential driver site failed to confirm a potential driver site at a similar location; as well as displaying the refined potential drivers in a highlighted manner according to the weighting, the display being associated with a computer representation of the heart chambers, Wherein, if the angle from the vertex opposite to the potential driving site to at least two electrodes is less than a predetermined angle and activation occurs across the at least two electrodes, the potential driving site is classified as the earliest activated electrode site, For each of the earliest activated electrode sites: calculating a value for each of a plurality of modifiers associated with the electrode site, the modifiers being determined based on electrogram data for the site; determining a ranking factor calculated based on the plurality of modifiers; ranking each of the earliest activated electrode sites according to a ranking factor; and outputting data identifying the regions, the data varying the significance of each of the determined earliest activated electrode sites depending on the ranking, Wherein, the modifier determined according to the electrogram data includes: The minimum cycle length of the electrogram recorded at the electrode, the activation frequency gradient between the electrogram recorded at the electrode or within the region and the electrogram obtained within a predetermined geodesic distance, and the average voltage of the local electrogram recorded within the region.

14. A computer system for identifying one or more regions of the myocardium responsible for supporting or initiating an abnormal heart rhythm, the computer system using electrogram data recorded from a plurality of electrodes on a multipolar cardiac catheter, the electrogram data being obtained from a corresponding series of sensing locations on the heart over a recording period, the computer system include: processor; a first memory for storing received electrogram data; and a second memory storing program code, which, when executed by the processor, causes the computer system to: identifying, based on the electrogram, regions within chambers of the heart having sequences of electrical activation that characterize the regions as potential drivers of abnormal heart rhythms, for each sensing location at or substantially surrounding the region, determining an earliest activated electrode site based on the predominant activation; For each earliest activated electrode site identified: calculating a value for each of a plurality of modifiers associated with the electrode site, the modifiers being determined based on electrogram data for the site; determining a ranking factor calculated based on the plurality of modifiers; sorting each of the earliest activated electrode sites according to a sorting factor; as well as outputting data identifying the regions, the data varying the significance of each of the determined earliest activated electrode sites depending on the ranking, Wherein, when the program code is executed by the processor, the computer system determines the modifier, and the modifier includes: The minimum cycle length of the electrogram recorded at the electrode, the activation frequency gradient between the electrogram recorded at the electrode or within the region and the electrogram obtained within a predetermined geodesic distance, and the average voltage of the local electrogram recorded within the region.

15. The computer system according to claim 14, in, The program code, when executed by the processor, causes the computer system to determine a modifier by accessing data to obtain a predetermined weighting factor, the predetermined weighting factor being dependent on the location of the electrode site.

16. The computer system according to claim 14, in, The program code, when executed by the processor, causes the computer system to output a visual indication that changes the significance of each of the determined earliest activated electrode sites depending on the ranking.

17. The computer system according to claim 14, in, The program code, when executed by the processor, causes the computer system to output data to a display or medical scanning device to cause display or navigation of chamber geometry and guide placement of catheters or electrodes for subsequent electrographic data acquisition.

Images