Computer implementation method and system for assisting in the mapping of heart rate abnormalities

Abstract

A computer-implemented method and system are described for identifying one or more regions of the heart that are responsible for supporting or initiating abnormal heartbeats, using electrogram data recorded from multiple electrodes on a multipolar cardiac catheter obtained from a corresponding series of sensing locations on the heart over a recording period. The method includes the steps of: identifying regions within the cardiac ventricles from the electrograms having electrical activation sequences that characterize them as potential drivers of abnormal heartbeats, and determining an earliest activated electrode site from the primary activation for each detection location in the region or substantially around the region; for each determined earliest activated electrode site: calculating a value for each of a plurality of modification factors associated with the electrode site, the modification factors being determined from electrogram data for the site, tissue characteristics of the site, or anatomical characteristics of the site; determining a ranking factor calculated from the plurality of modification factors; ranking each of the earliest activated electrode sites according to the ranking factor; and outputting data identifying the region, the data varying the prominence of each of the determined earliest activated electrode sites according to the ranking.

Description

Technical Field

[0001] The present invention relates to a computer-implemented method and system for assisting in the mapping of cardiac arrhythmias, and more particularly, to a method and system for identifying cardiac regions that are statistically likely to be causing abnormal heartbeats.

Background Art

[0002] This application is related to International Publication No. 2019 / 206908 and UK Patent No. 2573109, which are co-pending applications that are incorporated herein by reference. These applications describe systems and methods for Stochastic Trajectory Analysis of Ranked Signals: STAR, a ranked signal mapping method that is referred to below. Embodiments may use STAR mapping, as described below, but any approach is also possible.

[0003] Atrial fibrillation (AF) is the most common persistent heart rhythm abnormality. Its incidence is increasing, partly due to the aging population, and it is called a growing disease. AF results in irregular contractions of the heart, causing unpleasant palpitations and increasing the risk of stroke, heart failure (HF), and death. Percutaneous catheter ablation (CA) is a safe treatment option for symptomatic patients with AF. The success rates of these procedures have improved over time due to a better understanding of AF, the development of new procedures and techniques, and improved physician experience. However, the success rates of these procedures still remain between 50 and 70%. The main reason for the difficulty found in targeting specific sites (hereinafter referred to as AF drivers) that are factors in the persistence and maintenance of AF is the irregular and disordered nature of the excitation wavefront in atrial fibrillation. The individual rotational, reentrant, or meandering and instability of focal activations greatly complicates the interpretation of the excitation sequence.

[0004] More recently, numerous computational and electroanatomical methods have been developed that allow physicians to see electrical data (i.e., electrophoresis) recorded from within the atria, so that specific "driver" regions can be identified. These drivers can also be easier or more difficult to identify depending on the relationship between the frequency of drivers and the frequency of excitation from non-driver random and disordered excitations. Panoramic mapping techniques attempt to address this problem, where a multipolar catheter is inserted into the target ventricular cavity to simultaneously acquire signals across the ventricle. Examples include non-contact mapping (Ensite, Abbott Medical; or Acutus Medical) and certain 2D and 3D contact mapping methods (e.g., Cartofinder, Biosense Webster, J&J; Topera, Abbott Medical; Rhythmia, Boston Scientific).

[0005] Evidence is inconsistent regarding whether electromorphic features are useful as surrogate markers for local drivers in persistent AF in humans. These may be called atrial fibrillation drivers (AFDs). It is understood that a single atrial fibrillation driver may cause regular non-fibrillation arrhythmias, and therefore, an atrial fibrillation driver may be interpreted as indicating an atrial fibrillation / arrhythmic driver. Organizing features of electromorphisms have better identified sites that play a mechanistic role in AF, but markers of rapidity have been unreliable. Optical mapping studies in animals have shown that AF is sustained by sites exhibiting the fastest cycle length (CL) and highest dominant frequency (DF), but in humans, this has proven to be a poor predictor of sites supporting AF. The poor correlation may be due to a lack of spatiotemporal stability of drivers in AF, which may explain the apparent inconsistency of rapidity sites. In addition, optical mapping studies in animals have shown that high-frequency to low-frequency ranges within the atrium in the rotator region. gradient This indicates that in humans with AF, the frequency gradient It has also been demonstrated that these are interatrial gradient It is limited to this.

[0006] STAR mapping method

[0007] The STAR mapping method is detailed in the aforementioned patent application and has been validated by mapping atrial tachycardias (AT) in vitro and in vivo before being used to map AF. In short, the principle of the STAR mapping method is to use the timing comparison of multiple electrophoresis diagrams to establish individual wavefront trajectories associated with AF. This is then used to identify the atrial region that most frequently precedes the excitation of adjacent regions. By collecting data from many excitations, a statistical model can be formed. This makes it possible to rank atrial regions according to the amount of time that excitation precedes excitation of adjacent regions. Unipolar excitation timing is typically considered to be the maximum negative deflection (peak negative dv / dt), however, excitation timing may be derived from other methods such as dipole density, peak bipolar, or resolved omnipolar voltage. By utilizing a predetermined refractory period, the mapping method avoids assigning excitations from separate wavefront or split electrophoresis diagrams. Unbelievable electrode timing relationships due to constraints imposed by conduction velocity are also excluded by the mapping method.

[0008] One form of STAR map display consists of color-coded electrode positions projected onto a replica of the patient's atrial geometry created with a standard 3D mapping system. Each color represents the percentage of time an electrode spends leading other counter electrodes, as highlighted by the color scale on the right side of the STAR map.

[0009] Problems with current methodologies

[0010] When mapping the atria using a basket, a globular distribution of ranked sites that are potential AF drivers (AFDs) can be generated by using a mapping system that attempts to determine the proportion of time an electrode "leads" an electrode relative to its adjacent electrode. However, with sequential, high-density mapping techniques, information about the relative importance of the initial sites to each other is not provided. For example, several sites may all appear to be leading.

[0011] A further problem with the analysis of sequentially acquired data concerns the complexity of interpreting regions designated as preceding at the edges of the acquisition area. This is illustrated in Figure 1. Rule-based methods for interpreting such data are advantageous because they allow for automated interpretation and reduce regions highlighted as potential driver sites by removing or reducing the prominence of passively excited regions at the edges of the acquisition. [Prior art documents] [Patent Documents]

[0012] [Patent Document 1] International Publication No. 2019 / 206908 Brochure [Patent Document 2] British Patent No. 2573109 [Overview of the project]

[0013] According to an aspect of the present invention, a computer-implemented method is provided for identifying one or more regions of the heart that are factors supporting or initiating an abnormal heartbeat, the computer-implemented method using electrographic data recorded from multiple electrodes on a multipolar cardiac catheter obtained from a series of corresponding detection locations on the heart over a recording period, the method comprising the following steps: A step of identifying regions within the ventricle of the heart that have electrical activation sequences that characterize them as potential drivers of abnormal heartbeats, from an electrophysiogram, For each detection location in a region or substantially surrounding that region, the steps include determining and identifying the earliest excitation electrode site from the primary excitation; For each determined earliest excitation electrode site: A step of calculating a value for each of several modification factors related to an electrode site, wherein the modification factors are determined from electrogeographic data of the site, tissue characteristics of the site, or anatomical characteristics of the site; The process of determining a ranking factor calculated from multiple modification factors; A step of ranking each of the earliest excited electrode sites according to its ranking factor; A step of outputting data that identifies the region, wherein the data is output in which the prominence of each of the earliest excited electrode sites determined according to the ranking is varied. Includes.

[0014] The individual variability data for each of the earliest excitation electrode sites, determined according to the ranking, could act as a trigger for scanning systems to suppress the display of sites, highlight them in reports / spreadsheets, alter the visual scalability of sites in illustrations (such as those that can be overlaid on cardiac images), and consider / ignore regions, etc.

[0015] The correction factors determined from the electrophoresis data are as follows: The minimum cycle length of the potential diagram recorded at that electrode, and the excitation frequency between the potential diagram recorded at that electrode or within that region and the potential diagram obtained within a predetermined geodesic distance. gradient , and the average voltage of the local potential diagram recorded within that region It may include.

[0016] The correction factor for tissue characteristics may include measurements of the presence and density of scar tissue determined by image processing.

[0017] The correction factor for tissue characteristics may include measurements of tissue impedance at that site.

[0018] The correction factor may be determined from the results of processing at another earliest excitation electrode site.

[0019] The correction factor for tissue characteristics may be calculated by referring to the results of ablation at similar sites in previous cases.

[0020] The correction factor for anatomical characteristics may include a predetermined weighting factor that depends on the position of the electrode site.

[0021] The data output may include displaying the data by a visual display in which the significance varies for each of the determined earliest excitation electrode sites according to the ranking.

[0022] The method is as follows: determining the main electrogram wavefront trajectory of each electrode site; determining a vector of the predominant derived electrogram activations of the plurality of electrodes and excitations; classifying the electrode sites as the earliest excitation electrode sites and potential driver sites if the direction of excitation progresses from an electrode site to one or more electrode sites closer compared to the potential driver site; may further be included, The order and timing of excitation are within the scope of a predetermined biologically reasonable pattern in relation to the conduction velocity and path within a reasonable excitation order.

[0023] In one embodiment, a site may be classified as an early excitation electrode site if excitation occurs across at least two electrodes at an angle less than a predetermined angle from the apex of the potential driver site. Furthermore, the excitation of at least one electromagnet is determined later than the potential driver site within the defined arc of the excitable tissue.

[0024] The specified angle may be less than 180°.

[0025] This method may further include displaying or navigating the ventricular geometry and outputting data to a display or medical scanning device to guide the placement of a catheter or electrode for subsequent acquisition of electrophysical data.

[0026] The method may further include identifying a site for subsequent placement of a catheter or electrode in order to exclude or confirm a previously determined first excitation electrode, or to extend the electrographic data to areas that have not been previously scanned or have been incompletely scanned.

[0027] This method uses the location of the first excitation site already identified, or the excitation in the signal leading score and gradient This may further include identifying the location from the vector.

[0028] This method may further include receiving electromagnetism data in virtually real time and providing instructions on when sufficient acquisition timing has been taken for the area being scanned.

[0029] The process of determining whether sufficient acquisition timing has been taken may include determining that the excitation pattern of the scanned area has reached a predetermined level of statistical certainty. For example, the probability that this pattern is not random is 95% (this could be achieved within a few seconds if the heartbeat was very regular).

[0030] The process for determining whether the acquisition was timely is as follows: This may include one or more of the following: counting down a predetermined period (for example, 30 seconds, as in the previous case); acquiring a predetermined number of excitation cycles (for example, 50 excitation cycles) at at least a predetermined minimum number of electrodes of a roving multipolar mapping catheter; and acquiring a predetermined time or a predetermined number of excitation cycles in which the excitation patterns at multiple electrodes remain within a pattern matching sequence.

[0031] This method may further include deriving the wavefront direction by determining which electrode of all electrode pairs is leading, and processing the wavefront direction to determine whether the earliest excited electrode is a true AFD or represents a passively excited site excited beyond the measurement boundary.

[0032] Advantageously, in embodiments of the present invention, additional information is available, thereby enabling ranking of the importance of the earliest excitation (preceding) sites. This can be used to compare sites acquired at different time points during a heartbeat with intricate irregularities and to verify whether they truly precede. Other properties of the electrical activity of atrial tissue regions can also be used to help indicate regions where ablation will interrupt or delay AF. Incorporation of other measured factors improves the ability to indicate cardiac regions where treatment will yield the best results. Embodiments incorporate factors to modify, weight, or even rank signal precedence scores resulting from such mappings in order to improve region selection for ablation and to indicate the importance of regions for arrhythmia persistence. Furthermore, the incorporation of such factors can also be used to provide negative weighting to indicate that such regions are less likely to contribute to the arrhythmia mechanism.

[0033] According to another aspect of the present invention, a computer-implemented method is provided for analyzing electromagnetism acquired from the heart to identify one or more regions of the heart that are contributing to or initiating an abnormal heartbeat, wherein the computer-implemented method uses electromagnetism data recorded from multiple electrodes on a multipolar cardiac catheter obtained from a corresponding series of detection locations on the heart over a recording period, and the method comprises the following steps: The process involves analyzing the sequence of electrical excitations to define specific regions within the ventricles of the heart as potential drivers of abnormal heartbeats, A process of weighting and classifying areas as potential drivers according to factors, the following: If the direction of the activation-generated wavefront propagates from the potential driver site to one or more nearby electrodes relative to that driver site during the same acquisition; When the sequence and timing of excitations fall within a biologically plausible range in relation to conduction velocity and pathways that are within a reasonable sequence of excitations; Excitation occurs across at least two electrodes at an angle less than a predetermined angle, for example, less than 180° from the apex relative to the potential driver site, and If the excitation of at least one electromagnet is determined later than the potential driver site within a defined arc of the excitable tissue, and A weighted classification step, including the case where further acquisition performed at an adjacent position to the missing arc of the first potential driver site does not fail to identify the potential driver site at a similar position, A computer implementation method comprising the steps of displaying a refined potential driver in a weighted and emphasized manner, wherein the display relates to a computer display of cardiac chambers.

[0034] According to an aspect of the present invention, a computer system for identifying one or more regions of myocardium that are contributing to or initiating an abnormal heartbeat, using electrographic data recorded from multiple electrodes on a multipolar cardiac catheter obtained from a series of corresponding detection locations on the heart over a recording period, wherein the system includes: Processor and; A first memory for storing the received potentiometer data; The second memory, when executed by the processor, provides the following to the system: From electrophysiological diagrams, identify intraventricular regions of the heart that have an electrical excitation sequence that characterizes them as potential drivers of abnormal heartbeats, For each detection location in or substantially surrounding the region, determine and identify the earliest excitation electrode site from the primary excitation; Regarding the determined earliest excitation "electrode site": Calculating values ​​for each of several modification factors related to an electrode site, wherein the modification factors are determined from electrogeographic data of the site, the tissue characteristics of the site, or the anatomical characteristics of the site; Determining a ranking factor calculated from multiple modification factors; To rank each of the earliest excitation electrode sites according to its ranking factor; and Outputting data that identifies the region, wherein the data varies the prominence of each of the earliest excited electrode sites determined according to the ranking. A second memory contains the program code that causes it to perform the action, It is equipped with.

[0035] When the program code is executed by the processor, it will be sent to the system as follows: The minimum cycle length of the potential diagram recorded at that electrode, and the excitation frequency between the potential diagram recorded at that electrode or within that region and the potential diagram obtained within a predetermined geodesic distance. gradient , and the average voltage of the local potential diagram recorded within that region It is possible to determine a correction factor determined from the electromorphic diagram data, which includes one or more of the following.

[0036] When the program code is executed by the processor, it can cause the system to determine modifying factors for tissue properties by obtaining measurements of the presence and density of scar tissue determined by image processing.

[0037] When the program code is executed by a processor, the system can determine modifiers for tissue properties by obtaining measurements of tissue impedance at that site.

[0038] When the program code is executed by the processor, it can cause the system to determine modifiers for tissue properties by obtaining results from processing at another earliest excitation electrode site.

[0039] When executed by a processor, the program code can allow the system to determine modifying factors for tissue characteristics by accessing data on the results of ablation at similar sites in previous cases.

[0040] When the program code is executed by the processor, it can cause the system to determine a modification factor by accessing data to obtain a predetermined weighting factor that depends on the position of the electrode site.

[0041] When the program code is executed by the processor, it will be sent to the system as follows: To determine the main potential wavefront trajectory of each electrode site; Determining the excitation vectors of the main derived electrophoresis across multiple electrodes and excitations; If the direction of excitation progresses from one electrode site to one or more electrode sites near the potential driver site, the electrode sites may be classified as the earliest excited electrode site and the potential driver site; and The order and timing of excitations fall within a predetermined biologically valid range, in relation to conduction velocity and pathways within a reasonable excitation sequence.

[0042] When executed by the processor, the program code may cause the system to output a visual representation showing changes in prominence for each of the earliest excited electrode sites determined according to the ranking.

[0043] When executed by the processor, the program code can cause the system to output data to a display or medical scanning device to display or navigate the ventricular geometry and guide the placement of a catheter or electrode for subsequent acquisition of electrophysiological data.

[0044] Embodiments of the present invention aim to provide a system and method that can be used to refine the STAR method and other similar mapping methods based on human data, typically electromorphological data acquired during electroanatomical mapping procedures, as well as imaging data (e.g., tissue characteristics or anatomical data acquired from CT or MRI scanning), in order of factors that may include:

[0045] • Cycle lengths of identified potential AFDs, particularly the shortest cycle length, the lowest cycle length variation compared to other identified AFDs, and the steepest cycle length (CL) around the AFD. gradient Measurement and comparison of the presence of.

[0046] • The same acquisition was recorded, but the CL between the potential AFD and the surrounding electrode was recorded. gradient The steepest or present, provided that the direction of the generated wavefront of the STAR map is propagating from the AFD to the electrode paired with the wavefront collision (to avoid the associated high cycle length).

[0047] If the wavefront is found to propagate across at least two electrodes located within a range of less than 180° from the peak relative to the AFD, it is considered to originate from that AFD site.

[0048] Other applicable criteria include: If the AFD does not have pairs of electrographic timings slower than within the 180° arc, it is assumed that there are peripheral points with excitations that potentially originate from directions within that 180° arc rather than from the AFD, unless there is a scar within the 180° arc.

[0049] AFD can only be marked as such if it has two or more electrode pairs after it (these pairs are spaced far enough apart that they do not allow for an empty arc of 180° from the AFD, in which case the AFD is considered peripheral excitation rather than true AFD).

[0050] Since this excitation site can be described as originating not from a potential driver site, but from a direction within its defined arc, a 180° example can be considered.

[0051] In a preferred embodiment, potential AFDs can be further refined in terms of their relative importance by referring to specific modification features derived from the electrogeographic or tissue characteristics of the underlying tissue.

[0052] One embodiment is a system for analyzing electrophoresis obtained from the heart, To define specific regions within the ventricles of the heart that have an electrical excitation sequence that identifies them as potential drivers of abnormal heartbeats, and This system aims to classify the importance of each defined region based on electromagnetism characteristics in that region, other than excitation sequence, anatomical or imaging characteristics, surface electromagnetism, and / or patient characteristics, and this includes: This may include the shortest or minimum cycle length compared to all other identified early excitation sites, and / or If the calculated excitation sequence direction proceeds from the potential driver to the electrode it is being compared to, then between the potential driver and the nearby electrode recorded in the same acquisition... gradient It may be further refined by considering only, and / or This may include the lowest cycle length variation compared to all other identified early excitation sites, and / or The cycle length between one early excitation site and one or more electrodes, recorded in the same acquisition within a defined geodesic distance of the target potential site (e.g., less than 3 cm for irregular heartbeats). gradient This may include the steepest or present nature of; and / or This may include evaluating the geodesic distance to the target site from an area having an abnormal unipolar and / or bipolar electrogram voltage, and / or Frequency analysis of dominant frequencies or frequencies in the target area and other areas within the defined geodesic distance. gradient Evaluation of the analysis, and / or Furthermore, evaluation of anatomical and structural features that can be identified by imaging methods such as intracardiac echocardiography, cardiac magnetic resonance imaging, and cardiac computed tomography, for example, • Proximity to pulmonary veins or atrial tissue • Junction of the atrial appendage and vein • Marshall's vein • Mitral valve ring • Adjustable band ·Aneurysm • Identified scars • Changes in tissue thickness It may include, and / or This may include an evaluation of the organizational electrical impedance, and / or This may include evaluating tissue movement or thickening by image processing, either directly or by referring to the movement of a cardiac catheter in contact with cardiac tissue, and / or The assessment of the correlation between identified target areas and areas identified as important by other heart rate mapping systems, which may include, for example, an assessment that assigns higher importance to target areas 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 geometric shapes created by different cardiac mapping systems; and This may include applying weights to further classify the potential beneficial effects of interventions in these areas and the response to ablation, which can be determined by referring to previously acquired data. Here, machine learning techniques can be used to calculate and optimize the weight factors to be adopted, The data can be displayed along with a visual representation of the calculated importance classification of these parts.

[0053] In one embodiment, a system for analyzing electrophoretic diagrams acquired from the heart is disclosed. The system can define specific regions in which excitation at an electrode generally precedes excitation at the electrode within a given geodesic distance, and can calculate the percentage of time or signal precedence score for each identified location. The preceding score is modified by applying adjustment factors calculated from other characteristics of the electrophysiogram, anatomical or imaging characteristics, surface electrophysiogram, and / or patient characteristics.

[0054] Using mean wavefront direction or vector characteristics (determining which electrode is leading among all pairs, i.e., derived from STAR mapping), it is possible to verify whether an electrode determined to be a potential AFD is actually a true AFD. Electrodes at the edge of a region mapped in a single acquisition that are determined to be a potential AFD may represent passively excited sites that are excited beyond the measurement boundary. If the potential AFD site is likely to be passively excited, this can reduce its significance.

[0055] The embodiments can be used with both recorded data (highlighting potentially suspicious or confirmed AFD sites). Alternatively, the embodiments can operate substantially in real time to highlight or instruct the user to move the electrode mapping catheter to the area most likely to overlap with AFD, or to move away from areas less likely to overlap with AFD (i.e., an invention of a system that uses these features to instruct the movement of the mapping catheter).

[0056] In one embodiment, a system for recording and analyzing electrophoresis obtained from the heart is disclosed. The system can define specific regions within the ventricles of the heart that have an electrical excitation sequence which they identify as potential drivers of abnormal heartbeats, and the apparent potential arrhythmia driver sites obtained by sequential multipolar mapping are further classified by separate rules to determine whether the driver site identified by a single mapping acquisition is the true cause of the arrhythmia, where, An electrode site is classified as a potential driver only if the direction of the excitation generation wavefront progresses from the potential driver site to one or more electrodes compared to its driver site during the same acquisition, and An electrode site is classified as a potential driver only if the order and timing of excitation are in a biologically valid manner, relating to a reasonable excitation sequence and conduction velocity and pathway within excitation across at least two electrodes with an angle less than a specified angle, for example, less than 180° from the apex to the potential driver site, and If an electrode site does not have a pair with a potentialographic timing later than within a defined arc of excitable tissue, for example 180° (because this can indicate that the excitable site does not originate from a potential driver site but rather from a direction within that defined arc), the electrode site may be excluded as a potential driver, i.e., classified as a passive excitable site, and further acquisitions made at a position adjacent to the missing arc of the first potential driver site do not confirm a potential driver site at a similar position, and the identified scar tissue is classified as non-excitable and is considered not to contribute to the arc of excitation.

[0057] Further revisions and modifications to the weighting factors may be made depending on the characteristics of the electrophysics in the region (in addition to the order of excitation), the anatomical structure of the potential driver site, the imaging characteristics around the potential driver site, the characteristics of the surface electrophysics, and / or patient characteristics. Typical electrical characteristics to be considered as weighting factors may include:

[0058] The shortest or minimum cycle length compared to all other identified early excitation sites is between the potential driver and the nearby electrode recorded in the same acquisition, assuming the calculated excitation sequence direction progresses from the potential driver to the electrode being compared. gradient It can be further refined by considering only that factor.

[0059] Furthermore, other electrical properties of the electromorphisms recorded at potential driver sites that can be considered relevant and where quantification can be used as input to modification factors, compared to all other identified early excitation sites, showed the lowest cycle length variability, and / or Cycle length between one early excitation site and one or more electrodes, recorded in the same acquisition within a defined geodesic distance of the target potential site. gradient The steepness or existence of; and / or Evaluation of the geodesic distance to the target site from an area having abnormal unipolar and / or bipolar potential diagram voltages, and / or Frequency analysis of dominant frequencies or frequencies in the target area and other areas within the defined geodesic distance. gradient Evaluation of the analysis, and / or This includes measurements of tissue electrical impedance at potential driver sites.

[0060] Using any one or a combination of the above, it is possible to further determine the likelihood of specific sites contributing to the development of arrhythmias, and a simple quantification of such sites can be used as a corrective factor.

[0061] Furthermore, the evaluation of anatomical and structural features may be performed using imaging methodologies such as intracardiac echocardiography, cardiac magnetic resonance imaging, or cardiac computed tomography, and one or more resulting quantified metrics used as input to modification factors.

[0062] The anatomical location itself can also be used in conjunction with factors based on proximity to anatomical structures, such as the calculated geodesic distance to any one or more defined anatomical points, as follows: • Proximity to pulmonary veins or atrial tissue • Junction of the atrial appendage and vein • Marshall's vein • Mitral valve ring • Adjustable band ·Aneurysm • Identified scars • Changes in tissue thickness The correction factor may be calculated using the following method. Dynamic assessment of tissue movement or thickening, either directly by image processing or by referencing the movement of a cardiac catheter in contact with cardiac tissue, can further improve the importance of the site by being used as a correction factor.

[0063] In one embodiment, a computer implementation method for analyzing recorded electromagnetism acquired from the heart is operated to identify regions from the electromagnetism regions within the ventricles of the heart that have an electrical excitation sequence that characterizes them as potential drivers of abnormal heartbeats, and The importance of each defined region is ranked by applying a ranking factor to each identified region, where the ranking factor is the product of multiple normalization factors and is subject to predetermined weight factors. When extreme outliers are excluded, the minimum cycle length of the potential diagram recorded at that electrode, and The excitation frequency between the potential diagram recorded within that electrode or region and the potential diagram obtained within a predetermined geodesic distance. gradient , and The average voltage of the local potential diagram recorded within that region, and The system is configured to display data along with a visual representation of the calculated importance classification of these parts.

[0064] In another embodiment, a system for analyzing electrophoresis obtained from a human or animal heart executes computer program code as follows: From the electrophysiogram, the excitation sequence of electrical excitations is identified, and specific regions within the cardiac ventricles that have an electrical excitation sequence characterized as a potential driver of abnormal heartbeats are defined, and A computer processor configured to rank the importance of each defined domain by applying a ranking factor to each specific domain, Here, the ranking factor is the product of multiple normalization factors, which are given predetermined weight factors, and this ranking factor is as follows: Once extreme outliers are excluded, the minimum cycle length of the potential diagram recorded at that electrode, and The excitation frequency between the potential diagram recorded within that electrode or region and the potential diagram obtained within a predetermined geodesic distance. gradient , and The average voltage of the local potential diagram recorded within that region, It is calculated from factors selected from a set that includes, and The data is displayed on a representation of the cardiac chambers, along with a visual representation of the classification of these regions.

[0065] Two or more cardiac mapping systems may be used simultaneously, and the correlation between the identified target area and areas identified as important by other cardiac mapping systems can be used as a modifying factor to give greater importance to the target area identified by two or more methods, which may be located on the same cardiac geometry or within the same anatomical region on two or more geometric shapes created by different cardiac mapping systems.

[0066] Each of the defined factors may receive weighting factors designed to provide a measure of the likelihood of a beneficial effect of the intervention at that site. Such weighting factors may be determined by referring to previously acquired data, responses to ablation from previous patients, and / or responses to ablation from the same patients during the trial. Machine learning techniques can be used to calculate and optimize the weighting factors to be adopted, and the resulting sites are displayed along with a visual representation of the importance classification of each ultimately calculated site, for example, by a color scale, a percentage of the likelihood of arrhythmia arrest or beneficial effect, or an importance ranking.

[0067] Embodiments of the present invention aim to provide systems and methods for improved analysis of CL, demonstrating greater speed and organization by improving the specificity and accuracy of driver sites in AF, where the site is identified by a method that seeks to identify cardiac regions where excitation precedes adjacent regions. In one embodiment, the STAR mapping method is employed, but other methods exist that can thereby identify potential driver sites. Potential AFDs can be identified by statistical methodologies that seek to identify the earliest local excitation regions, or by other commercial methodologies (e.g., Cartofinder [Biosense Webster, Haifa, Israel], Acutus, ECGi mapping [e.g., Cardioinsight, Medtronic, Ireland], Ablacon, Topera [Abbott Ltd., Mn, USA]). It will be understood that electrogeographic characteristics applicable to the atria (such as AFDs) may also be applicable to other heartbeats and ventricles, such as ventricular fibrillation or tachycardia.

[0068] In experimental trials, potential AF drivers (AFDs) were identified using the STAR method, and the identified AFDs were those that responded to ablation at those sites, accompanied by either a delay of AF cycle length exceeding 30 ms or AF termination. However, it will be understood that other factors may also be applied to more accurately identify AF drivers.

[0069] Furthermore, the studies demonstrate that driver sites identified using the STAR approach, which exhibits greater speed and organization, were more mechanistically important in maintaining AF, as evidenced by the higher likelihood of AF termination by ablation.

[0070] There are several novel methods that can be used as weighting factors when modifying signal-leading scores (ranking factors) that may be used in embodiments of the present invention. These can be subdivided into modification factors related to a single electrode, modification factors related to the relationship between a single electrode and surrounding electrodes, modification factors related to location-specific and individual patient-specific data of non-electroanatomical or physiological origin, location-specific modification factors derived from patient group outcome characteristics, and general modification factors that may include demographic data.

[0071] Embodiments of the present invention aim to improve the effectiveness of identifying potential arrhythmia driver sites from electroanatomical mapping, enabling better classification of these sites and determination of their significance, and include apparatus and associated computer implementation methods. The generated data may be displayed to a physician, or output in other ways, or passed to other systems. This may be used during cardiac catheterization procedures, such as catheter ablation procedures (either immediately or after implantation), to highlight locations where ablation has the most beneficial effect. Such data may be used to enable targeting of non-invasive therapies, such as radiotherapy, gamma knife, and proton beam therapy.

[0072] One potential use of embodiments of the present invention is in the treatment of patients diagnosed with persistent atrial fibrillation in whom ablation therapy consisting of pulmonary vein isolation has not resulted in complete termination of the arrhythmia. In these patients, another mapping process, such as STAR mapping, may be employed to better target further ablation, and this process is improved in embodiments of the present invention.

[0073] Herein, embodiments of the present invention will be described merely as examples with reference to the accompanying drawings. [Brief explanation of the drawing]

[0074] [Figure 1a] Figure 1a is a schematic diagram showing an embodiment of the method for determining the driver portion in the present invention. [Figure 1b] Figure 1b is a schematic diagram showing an embodiment of the method for determining the driver portion in the present invention. [Figure 1c] Figure 1c is a schematic diagram showing an embodiment of the method for determining the driver portion in the present invention. [Figure 1d] Figure 1d is a schematic diagram showing an embodiment of the method for determining the driver portion in the present invention. [Figure 2a] Figure 2a is a further schematic diagram showing an embodiment of the driver part determination in the present invention. [Figure 2b] Figure 2b is a further schematic diagram showing an embodiment of the driver part determination in the present invention. [Figure 2c] Figure 2c is a further schematic diagram showing an embodiment of the driver part determination in the present invention. [Figure 2d] Figure 2d is a further schematic diagram showing an embodiment of the driver part determination in the present invention. [Figure 2e] Figure 2e is a further schematic diagram showing an embodiment of the driver portion determination in the present invention. [Figure 3a] Figure 3a shows the determination and use of the maximum spreading angle in an embodiment of the present invention. [Figure 3b] Figure 3b shows the determination and use of the maximum spreading angle in an embodiment of the present invention. [Figure 3c] Figure 3c shows the determination and use of the maximum spreading angle in an embodiment of the present invention. [Figure 3d] Figure 3d shows the determination and use of the maximum spreading angle in an embodiment of the present invention. [Figure 3e] Figure 3e shows the determination and use of the maximum spreading angle in an embodiment of the present invention. [Figure 4a] Figure 4a is a schematic diagram illustrating the operation of an embodiment of the present invention. [Figure 4b] Figure 4b is a schematic diagram illustrating the operation of an embodiment of the present invention. [Figure 5a] Figure 5a shows the cycle length (CL) histogram obtained with a basket-type catheter electrode. [Figure 5b] Figure 5b shows the cycle length (CL) histogram obtained with a basket-type catheter electrode. [Figure 6] Figure 6 is a flowchart of the studies that identify potential AFDs. [Figure 7a] Figure 7a shows the image and potential diagram obtained from the subject. [Figure 7b] Figure 7b shows the image and potential diagram obtained from the subject. [Figure 7c] Figure 7c shows the image and potential diagram obtained from the subject. [Figure 7d] Figure 7d shows the image and potential diagram obtained from the subject. [Figure 7e] Figure 7e shows the image and potential diagram obtained from the subject. [Figure 7f] Figure 7f shows the image and potential diagram obtained from the subject. [Figure 8a] Figure 8a shows the image and potential diagram obtained from the subject. [Figure 8b] Figure 8b shows the image and potential diagram obtained from the subject. [Figure 8c]Figure 8c shows the image and potential diagram obtained from the subject. [Figure 9a] Figure 9a shows the image and potential diagram obtained from the subject. [Figure 9b] Figure 9b shows the image and potential diagram obtained from the subject. [Figure 9ci] Figure 9ci shows the image and potential diagram obtained from the subject. [Figure 9cii] Figure 9cii shows the image and potential diagram obtained from the subject. [Modes for carrying out the invention]

[0075] Detailed description of the drawing

[0076] In the embodiments described below, the system may be used with whole-ventricular basket catheters (Constellation catheter, Boston Scientific, Ltd, USA, and FIRMap catheter, Abbott, USA) to enable simultaneous panoramic left atrium (LA) mapping. However, electrographic data may be obtained using other suitable catheters / electrodes, and it is not necessary to collect data from the entire target region simultaneously. The target region can be divided into smaller regions, for example, electrographic data collected sequentially before analysis. The results of the analysis can then be combined and displayed on a single STAR map.

[0077] The system used to acquire and process electrocardiogram data typically includes one or more multipolar electrical catheters inserted into the patient's ventricular cavity (e.g., the basket catheter mentioned above, and other intracardiac catheters, such as a decapolar catheter placed in the coronary sinus), an amplifier and analog-to-digital (AD) converter, a signal analyzer, a processor, and a console including a GPU, a display unit, a control computer unit, and a system for determining and integrating 3D positional information of the electrodes.

[0078] The system may use known hardware and software, such as the Carto® system for catheters (Biosense Webster, J&J), NavX Precision® (Abbott Medical), or Rhythmia® (Boston Scientific), or a 3D electroanatomical integration and processing unit. Catheters such as "basket" catheters, circular or multi-spline mapping catheters (e.g., Lasso catheter, Biosense Webster, J&J, HD-mapping catheter, Abbott Medical SJM, Pentarray Biosense Webster, J&J), decapolar catheters, or ablation catheters may be used. These systems and catheters are used to collect electrographic signals and corresponding positional and temporal data for electrical activity at different locations within the cardiac chambers. This data is passed to a processing unit that performs algorithmic calculations on this data, and the data is transformed to provide the physician with locational information on the cardiac regions most likely to be contributing to the maintenance and persistence of abnormal heartbeats. Alternatively, the system may use custom-made catheters, tracking systems, signal amplifiers, control units, computing systems, and displays.

[0079] The patent application identified above describes a mapping system called the "Probabilistic Trajectory Analysis (STAR) Ranked Signal Mapping" system. This system aims to identify the location of drivers of cardiac arrhythmias, which can be displayed, for example, in the form of a 3D map. Maps created using the STAR mapping system are referred to as "STAR maps."

[0080] When performing STAR mapping (and also when acquiring data according to embodiments of the present invention), the physician may position a multipolar panoramic mapping catheter outside the left atrium, acquire data for a certain period (e.g., 5 seconds to 5 minutes), and then reposition the catheter so that it is securely aligned with the left atrial septum or anterior wall, and ensure that another recording is made. This can be done in advance, and it will be understood that the processing is performed on the previously recorded data.

[0081] The proportion of the "leading" electrode was coherent across maps, and therefore, the data and proportions could be displayed on the same map without any problems. In this way, multiple coherent statistical maps may be constructed sequentially by moving the catheter within the ventricle and taking further records.

[0082] The following are the main steps in the process used to identify “preceding” signals (indicating regions / locations within the heart that are statistically more likely to be driving an abnormal heartbeat) after acquiring electrographic data (along with corresponding spatial and temporal data).

[0083] First, interfering and remote field signal components are removed from the input electrographic signal. In one embodiment, the system decomposes the signal into relevant components by means of, for example, spectral analysis, remote field signal blanking, remote field signal subtraction, filtering, or other methods known in the art. Signal components occurring in the target ventricle within the heart (e.g., atrial signals) are identified. The relative timing of the atrial signals is established, which may be in an explicit, estimation, or probabilistic manner. In some embodiments, the phase of each signal can be determined and relative timing can be established from the relative phase displacement between different electrodes.

[0084] Secondly, the signal timings from adjacent electrodes are paired. Signals are paired only when electrode positions are within a specified geodesic distance from each other, i.e., only electrodes in close proximity to each other. This can be further improved by pairing electrodes located in the same aspect of the ventricular wall. That is, adjacent electrodes on the posterior wall of the heart are considered adjacent, but electrodes are not considered adjacent if they are located in a discontinuity, for example, on either side of a pulmonary vein, even if the absolute distance between them is small. Thus, the relative timing of excitation at the counter electrode is established and a value is assigned relative to the "preceding" electrode. This pairing can typically be performed over discrete analysis periods of 10 ms to 200 ms in duration. The length of the analysis period does not need to be constant across all the data being analyzed. The purpose is to compare the timing between excitations at counter electrodes caused by the same excitation sequence, and the analysis period can be determined accordingly. For example, each analysis period can be chosen to encompass excitations at electrodes that are likely to have resulted from the same excitation sequence. Therefore, the analysis periods may overlap.

[0085] Thirdly, this process is repeated many times over a given period (i.e., for each pair formation, over many analysis periods). Advantageously, the analysis periods overlap and are shifted to an initial analysis period, for example, 10-120 seconds. Within a given period, the analysis periods can overlap as described above. For example, if the analysis period is 200ms, the analysis periods may overlap by 100ms, or 50%. In other words, the leading electrode is determined over a given period, such as the first 200ms period, then a second 200ms period, and a second period starting 100ms after the start of the first period. As with the analysis periods themselves, the degree of overlap can vary across the dataset. Such repeated analysis makes it possible to discard excitation sequences that do not repeat very often or never, and to rank the excitation sequences that appear more frequently by importance and priority.

[0086] In atrial fibrillation, the excitation pattern appears disordered, with frequent changes in wavefront propagation. In the mapped excitation, the relative proportion of "time" that each site precedes each of its adjacent sites is calculated, thus creating a percentage map of electrode sites that "preced" more frequently.

[0087] Fourth, the proportion of "time" each recording region spends on "preceding" excitation is calculated. This calculation may be based on the actual duration for which each electrode is judged to precede its paired electrode. Alternatively, it may be the proportion of the total analysis period (whether those periods are the same or different from each other) in which the region precedes the mapped excitation. Thus, in some examples, the relative proportion is actually determined by examining the total number of atrial excitation signals observed by a given electrode and determining the proportion of those excitation signals that the electrode precedes relative to several other electrodes paired with it. Mapping only adjacent electrodes may result in errors, but the system maps all electrodes relative to all others for each excitation cycle to establish the direction of excitation within the mapped field. The sequence is analyzed to identify the dominant excitation sequence during the recording period and the regions preceding those excitations, i.e., the points where the excitation originates. Excitation sequences with trajectories suggesting a localized source, the AFD, are presumed to be mechanistically important. The STAR mapping system calculates the proportion of excitation sequences with a given vector to establish the dominant vector (if any) and the proportion of time the excitation follows that vector. For all sites within the mapping field, their relative importance is determined by calculating the proportion of mapped excitations originating from each site. These proportions are called "leading signal scores" and allow for further modification, comparison, and calculation.

[0088] The leading signal scores are normalized so that the proportions can be compared across hearts. Many embodiments of the statistical processing, normalization, and subsequent presentation of this data can be assumed.

[0089] Electrodes that overlap with completely passive areas of cardiac excitation tend to have little to no excitation, even if there appears to be excitation originating from them. Only excitation sites that frequently "preced" within the excitation sequence are considered to have a high probability of being the source of excitation. Similarly, considering overlapping electrode sampling locations A and B that repeatedly excitation early on the edge of sample A, it can be seen that the excitation progresses from B to A, and therefore the early excitation site at A may be considered passive, and the electrode from sample B may be considered precedent, and therefore the preceding electrode from sample B is more emphasized.

[0090] This process may be repeated over multiple recording periods and locations to further refine and define excitation patterns that repeat and discard excitations that are not repeated and discarded, and thus it may be possible to construct a wider area mapping than could be achieved from a single excitation recording from a multipolar electrode catheter.

[0091] The STAR mapping method can, in embodiments, be used to identify the earliest excitation electrode sites for regions within the cardiac ventricles that have an electrical excitation sequence that characterizes them as potential drivers of abnormal heartbeats. Alternatively, methods such as the CARTOFINDER® module running on the Carto® system (Biosense Webster, J&J) can be used to indicate the percentage of time that an electrode location precedes excitation in its vicinity. This information can be normalized to form a signal-leading score that can be used in the described methods to provide improved information regarding the importance of such sites.

[0092] In one embodiment, a computer-implemented method is used to identify one or more regions of the heart that are contributing to or initiating an abnormal heartbeat. The computer-implemented method uses electrographic data recorded from multiple electrodes of a multi-electrode array on a multipolar cardiac catheter, obtained from a corresponding series of detection locations on the heart over a recording period. The method involves the following steps: A step of identifying regions within the ventricle of the heart that have an electrical excitation sequence that characterizes them as potential drivers of abnormal heartbeats, from an electrophysiogram, A step of determining and identifying the primary earliest excitation electrode site for each detection location in or substantially surrounding the said region, For each determined earliest excitation electrode site: A step of calculating a value for each of several correction factors related to an electrode site, wherein the correction factors are determined from electrogeographic data of the site, tissue characteristics of the site, or anatomical characteristics of the site. The process of determining a ranking factor calculated from multiple modification factors; A process of ranking each of the earliest excited electrode sites according to its ranking factor; A step of outputting data that identifies the region, wherein the data is output in which the prominence of each of the earliest excited electrode sites determined according to the ranking is varied. This includes [something]. This will be explained with the following example.

[0093] Figures 1a to 1d are schematic diagrams illustrating the process of determining the driver site (the earliest excitation electrode site corresponding to the potential driver of an abnormal heartbeat).

[0094] Figure 1a shows a schematic diagram of the atrium of a human heart. This diagram represents the three sites of excitation driving atrial fibrillation as circles A, B, and C. These can be identified as driver sites by several methods, such as stochastic trajectory analysis of ranked signal mapping (STAR ​​mapping). In sequential mapping of the arrhythmia wavefront, the electrodynamic wavefront trajectory is determined using a multi-electrode array represented by a large circle MEA surrounding many individual electrode sites, including electrode e1. The excitation vectors of the derived electrodynamic wavefront are derived across the entire MEA, represented here by arrow lines. In this example, the earliest excitation site is determined to be e1. As shown in Figure Panel 1c, when the MEA moves to a new location, the electrode site overlapping the original site e1 no longer appears to be a leading site representing the characteristics of the driver site, and electrode site e1' actually generally leads the excitation.

[0095] In embodiments of the present invention, if an electrode region on the edge of an electrode array acquisition appears to be preceding in one acquisition but is not preceding in another acquisition that overlaps with the preceding region in the first acquisition, that region is classified as a driver.

[0096] Here, we consider region e1', which precedes the acquisition shown in panel 1c and actually overlaps the true driver region (region A in Figure 1a). Now, the third acquisition shown in panel 1d is performed, during which the electrode (e2'') overlapping region e1' continues to have the characteristics of a signal-leading electrode, while the edge electrode region e1'' is moved to the next region. The system confirms that the driver region located at region e1' (and e2'') is now clearly the driver region.

[0097] In this way, by using the continuous acquisition of electromagnetisms performed around the cardiac chambers, it is possible to distinguish between true driver sites and apparent driver sites that are passively excited by conduction from distant driver sites.

[0098] Those skilled in the art will find it possible to perform such sequential mapping and determination of driver sites dynamically during mapping, or at any intermediate point, after all sites have been mapped. For example, a map may be generated from acquired data, and the locations of determined potential driver sites, as well as potential driver sites that require further mapping or electrogram acquisition within the area to be actively classified as potential drivers (or not), can be highlighted to the user or physician within the map. It is conceivable that such a system could be employed to instruct a robot-controlled catheter to map in the direction and surroundings of potential driver sites.

[0099] Figure 2a is a diagram of the cardiac chambers (here, a schematic diagram of the left atrium) with two independent driver sites represented by stars A1 and A2. The general vectors of the cardiac excitation wavefront are indicated by diagonal arrows.

[0100] In one embodiment of the present invention, as shown in Figure 2b, a multipolar mapping catheter having electrodes e1 to e10 is placed on the AF driver of A1. Electrode e2 is excited first, and the excitation vector generally acts away from this site. As a result, all displayed vectors are directed towards the e2 electrode, which has the highest leading signal score (for clarity, only arrows from e2, e3, e5, and e9 are shown). This highlights e2 to the user.

[0101] As shown in Figure 2c, the multipolar mapping catheter is then moved to a site away from both excitation sources. Here, the “leading signal score” is highest on the peripheral electrode e9'. The excitation is generally unidirectional, and generally, the peripheral electrode e9' leads all others.

[0102] Figure 2d shows a simplified concatenated map, similar to that seen in STAR mapping, where the electrode with the highest signal leading score from each sequential acquisition is highlighted. Two regions representing the AFD are shown here only by outline, not filled in. In this example, two electrodes, e2 and e9', are highlighted. Although electrode e9' is said to have a high leading signal score, it is clear that this does not indicate that this electrode indicates the true location of the AFD, unlike e2. The described invention modifies the representation of such regions by identifying any of the resulting vectors relative to regions around acquisitions where the excitation order of adjacent acquisitions is consistent (i.e., approximately the same direction / distance from region e9'). Experimental observations have also shown that such regions exhibit differences in excitation frequency, electrographic voltage, and other metrics such as dominant frequency when compared to the true AFD.

[0103] Figure 2e shows a map resulting from one embodiment, where e9' is identified as unlikely to be an AFD site despite its clearly high signal-leading score. Therefore, the electrode visualization is modified by reducing the projection size and decreasing color enhancement or similar effects. Site e2 can be further emphasized by increasing color intensity, size, or other visual modifications.

[0104] In a preferred embodiment, a graphical representation of the driver area map is displayed on the display unit.

[0105] In one embodiment, the relative importance of identified potential AFDs can be further refined by referring to specific modifying features derived from the electropotential properties, tissue properties, or other criteria (e.g., anatomical structure) of the underlying tissue.

[0106] In this embodiment, electrophoresis is acquired at different electrode locations throughout the ventricular cavity, group (g 1- etc~g x) is performed sequentially. For example, electrodes on a pentaarray mapping catheter (e 1 ~e 20 The electrophoresis can be moved to different locations within the left atrium of a patient with atrial fibrillation, and different electrophoretic graphs can be sequentially acquired from each location.

[0107] From the dataset of sequentially acquired electrophoresis diagrams, a signal-leading score is calculated for each electrode within the group. This may be calculated using the STAR method or another statistically based method, for example, indicating the percentage of time that the excitation observed on that electrode precedes the excitation detected by other electrode sites in the simultaneous acquisition.

[0108] Alternatively, other methods may provide a percentage of excitations that precede other sites, or a percentage of electrodes in an acquisition that appear to precede a particular electrode, or a hybrid metric.

[0109] [Table 1]

[0110] For each of these regions, one or more further factors, such as cycle length variation or minimum mean cycle length at each electrode, may be calculated.

[0111] The cycle length metric is calculated by first identifying all excitations on each electrode during acquisition.

[0112] To calculate the minimum stable cycle length (Min-CL), the initial inter-excitation coupling intervals are filtered to remove any excitations that are faster or slower than a setpoint near the mean cycle length. For example, excitations that are 30% faster or slower than the mean CL are ignored. The shortest cycle length among the remaining cycle lengths may represent unstable excitations, and therefore the shortest 10% of excitations are excluded. The value assigned to the minimum CL is the smallest of the remaining values, corresponding to the shortest cycle length at the 9th decile of the ranked excitation intervals, where the longest cycle length is at the 1st decile, and so on.

[0113] [Table 2]

[0114] Min-max feature scaling normalization can be performed across all sites, allowing for comparison of minimum CLs between separate acquisitions. In the case of a modified variable minimum CL, this normalization is performed over the range of acquired values. In this example, the absolute value of the minimum CL, subtracted from the maximum value of all minimum CLs across all electrodes and acquisitions, is normalized over the variation of this value.

[0115] For example, in the case of g2e2, abs(187ms-201ms)=14ms; and CL fluctuation = 36 ms; therefore, The normalized minimum CL for g2e2 is 14 / 36. =0.39

[0116] By multiplying this value by the original signal-leading score of that electrode (in this case, 0.2), we can obtain the corrected signal-leading score. That is, 0.2 × 0.39 = 0.05 In this regard, all modified signal-leading scores may be normalized again.

[0117] [Table 3]

[0118] Weighting may be used for each modification variable that can be applied before normalization.

[0119] As can be understood, this method, which provides weighting for electrode leading signal scores derived from STAR mapping or other electroanatomical mapping methods, can be applied to other electrophysiological phenomena such as cycle length variability, bipolar or unipolar voltage signals at a site, electrographic duration, or the excitation stability of other electrodes (i.e., low CL variability at other electrodes).

[0120] Further modification variables may be applied, for example, from quick reference tables of anatomical sites that can themselves be manually or automatically labeled. Alternatively, these may be calculated, for example, from anatomical or histological measurements derived from MRI or CT scans.

[0121] Geometric regions corresponding to the locations where the highest-ranked normalized modified signal-leading score regions were acquired are preferably highlighted, for example, by increasing or changing the size, opacity, color, or markers of those regions or areas on the surface of the ventricular cavity as rendered in the data output or visual rendering on the display.

[0122] In addition to the embodiments described above, or in preferred embodiments that may be used as alternatives to the embodiments described above, the determination of whether a potential AFD is actually a true AFD may be made by referring to, for example, the excitation vector determined by STAR mapping.

[0123] As mentioned above, the electrophysiogram shows the group (g) at different electrode locations throughout the ventricular cavity. 1 etc~g xThe acquisition is preferably performed by sequential acquisitions. For each simultaneous acquisition of the electromorphism, an approximate excitation vector is derived by referring to the signal-leading score (uncorrected) or by other means (e.g., wavefront direction calculation). Electrodes with a high signal-leading score surrounded by electrodes with a lower signal-leading score are more likely to represent the true AFD site. However, electrodes around the geometry of an acquisition with a high signal-leading score may either represent the true AFD or merely be the passively excited site closest to the AFD of that particular acquisition. It is advantageous to automatically indicate whether an acquisition is likely to be a passively excited site.

[0124] To determine whether the leading electrode site at acquisition (e.g., e1) can be a true AFD, the electrode with the highest signal leading score in the acquisition of that group is identified. A tangent plane to the geometric surface of the ventricle is created at the midpoint of the electrogram acquisition site. To prevent internal plane shift, it is advantageous to use a highly smoothed geometric representation of the ventricle. The in-plane angular distribution from an arbitrary bisector of e1 is then calculated, and the in-plane angular distribution of the electrodes around that point is determined (spread angle). The absence of a separation angle between surrounding electrodes greater than a given angle (e.g., 120°) indicates that e1 is likely a true AFD.

[0125] Therefore, the correction factor can be applied to the preceding electrode site identified as AFD, either as a value determined by a quick reference table or a pre-selected value. Alternatively, the maximum separation angle between electrodes at the time of acquisition can be rescaled inversely (1-(min-max normalization)) and used as the correction variable.

[0126] For example, consider a situation where e2 is surrounded by four electrodes in acquisition g1, and the sequential separation angles of these electrodes are 93°, 65°, 119°, and 83°. Therefore, the maximum angle for acquisition in this example is 119°. For example, a maximum spread angle for acquisition exceeding 170° indicates that the potential AFD site is located at the edge of the electrode group.

[0127] In this example, the three additional acquisition sites, g2 to g4, demonstrate that the maximum separation angles for each acquisition are 174°, 120°, and 240°.

[0128] These angles undergo normalization, yielding correction factors that can be weighted to each of the most likely AFDs observed at each acquisition. In this example, 1, 0.55, 0.99, and 0 can result from standard max-min normalization. Using the maximum possible separation (360°) instead of the measured maximum separation angle yields non-zero normalized correction factors, in this example, 1, 0.77, 0.99, and 0.49.

[0129] If the maximum spread angle of a potential AFD site is greater than 170°, it must be determined whether this site is passively excited. This can be done by referring to other acquisitions within a given geodesic distance of the potential AFD and within the arc of the maximum spread angle.

[0130] Determination of passive excitation

[0131] Electrodes that are potential AFD sites around a simultaneously acquired site can be classified into potential true AFDs or passively excited sites by referring to the excitation sequence or mean wavefront vector of separate acquisitions near that site, which gives the scope of application within the arc of maximum divergence angle. These are preferably located within a specific geodesic distance from the AFD site in question, typically within a predetermined distance, for example, within 3 cm.

[0132] For example, electrode acquisition g1e1 is located on the periphery of the acquisition and is identified as a potential AFD. Further acquisition g2 is performed within 3 cms and within the arc of the maximum spread angle, and the calculation is applied. If g1e1 is passively excited, the nearest-neighbor electrode signal-leading score of g2 is expected to be low. This can be defined as the signal-leading score on one (or more) nearest-neighbor electrodes of g2 (e.g., g2e1) being in the bottom 50% of all signal-leading scores of g2. Furthermore, the excitation vector at such an electrode is directed away from g1e1, i.e., between 90° and 270° on the plane of the surface, and the vector from g2e1 to g1e1 is considered to be 0°.

[0133] If both of these conditions are met, g1e1 can be defined as a site that is reliably passively excited. Thus, a modification factor may be applied to reduce the signal precedence score of g1e1 by decreasing its significance in the calculation. This modification factor may be arbitrary from a quick reference table or previous studies and may be applied either to a single electrode site (g1e1) or across all electrodes in a sequential acquisition (e.g., g1).

[0134] Next, an iterative process may be performed in which the signal-leading scores are recalculated following the reallocation and normalization of the signal-leading scores.

[0135] However, if the preceding signal scores for g2e1 and / or its adjacent sites are high (e.g., within the top 75% of preceding signal scores for excitation g2) and the excitation vector is directed towards site g1e1 (e.g., between -90° and +90° on the plane inter-electrode vector), then the true AFD driver is likely to be located in or very close to these two electrode sites. A positive correction factor may be applied, and the region between g2e1 and g1e1 may be visually highlighted.

[0136] There may be situations where an electrode with a high signal leading score is at the edge of an acquisition, and no acquisitions are located within both the arc of the maximum spread angle and the defined geodesic distance. In this case, the area can be highlighted on the display screen to indicate that further acquisitions are needed in that area. This can be done either by highlighting the area with a different color, or by indicating that further acquisitions should be performed with arrows, pointers, or other animations.

[0137] During such acquisition, an indicator may be displayed to show whether sufficient electrographic data has been acquired. Most simply, this could be a simple timer, but other indicators may count the number of electrographic excitations on each electrode by a preset target number referencing a given value.

[0138] Figures 3a to 3e show the determination and use of the maximum spreading angle in embodiments of the present invention.

[0139] Figure 3a shows a multipolar mapping catheter with six electrodes labeled e1 to e6. In this example, electrode e6 has a higher signal lead score than the other electrodes. The excitation vectors between this electrode and the surrounding electrodes are drawn with solid arrows, which are projected onto a tangent plane to the ventricular cavity surface representation smoothed at e6. An angle exists between these vectors, for example, two vectors e6 to e1 and e6 to e5, where the angle is given by φ1-5. In this example, e6 is enclosed by the electrodes, and the maximum angle φ5-6, which can be called the maximum spreading angle, is smaller than a preset maximum value, e.g., 120°. This confirms e6 as AFD, and therefore a correction factor can be applied.

[0140] Here, Figure 3b shows the same arrangement of electrodes moved to a different position. Here, the electrode with the potential AFD, i.e., the highest signal lead score, is e1. However, in this example, the maximum inter-electrode angle here is φ2~5, which is greater than the set amount (e.g., 170°), and we confirm this as a peripheral point of excitation.

[0141] Figure 3c shows an arc of a set diameter (e.g., 3 cm), which may be considered centered at the maximum divergence angle (in this example, φ2~5), and is shown in the shaded area. This can completely cover the angle φ2~5, or it can be smaller and centered at this angle. Excitations at two or more electrodes closest to separately grouped acquisitions on this arc are then considered.

[0142] As shown in Figure 3d, excitations at two or more nearest electrodes from separately grouped acquisitions (g2e1~g2e6) in this arc are then considered. In this example, the two nearest acquisitions, g2e4 and g2e3, both have low signal-leading scores and are not potential AFDs. Furthermore, the mean excitation vector, indicated by the thick solid arrow, points towards e1 (and not away from e1 as would have been expected if e1 were a true AFD). This confirms that e1 in this example is not an AFD, and a correction factor may be applied to reduce its visual splendor.

[0143] Figure 3f shows another situation where one of the nearest electrodes on further acquisition (g3e4) has a high signal leading score, and the vector from this electrode is in the opposite direction to the vector from e1. This indicates that the AFD is located near these two electrodes, and the region between them can be highlighted (e.g., a striped circle here).

[0144] Figures 4a and 4b illustrate a method for better classifying and ranking the importance of identified regions by using data from other electromorphological analysis or image processing techniques in combination with regions identified by electromorphological vector mapping. In this example, regions A, B, and C are initially identified using a mapping technique, e.g., STAR mapping. Further datasets that can indicate the potential importance of regions for maintaining cardiac arrhythmias can be combined and used to rank the identified driver regions. One way to do this is to present normalized modification factors using regions subsequently applied to the corresponding driver regions. Several modification factors can be applied to each identified driver region to obtain a final possible ranking or representation of the importance of each of the entire driver region identified by the original mapping method. Further data, such as historical patient data, may be applied, and machine learning algorithms may be used to determine the exact modification factors to use and the order in which they are applied.

[0145] As shown in Figure 4a, data obtained from electroanatomical mapping can be used to determine potential driver sites by excitation sequence mapping, e.g., STAR mapping, and can also be used to analyze other electrical properties in these sites (i.e., analysis of the features of the electrophysics in these regions). Further rule-based approaches may be employed if mapping is performed sequentially to remove driver sites that are actually passively excited, as detailed in Figure 1. These resulting data can be further combined with other information about the patient's heart during the clinical trial, for example, metrics derived from image processing, which can be normalized, weighted, and used to adjust the importance attributable to each identified driver site by this imaging data. Such data could also be cardiac MRI data containing percentages of contrast enhancement scales, e.g., scar, wall thickness, or tissue edema measurements, e.g., from delayed gadolinium contrast. Another source of corrective information may refer to historical data, which may be linked to machine learning techniques to further derive corrective factors for each identified potential driver site.

[0146] Figure 4b shows the data flow through the proposed device. Electrogeographic data is acquired using a multipolar mapping catheter connected to a cardiac mapping system and may be integrated into a 3D electroanatomical mapping system, or separately. It will be understood that instead of using an intracardiac catheter to collect data on electrocardiograms, surface electrograms may be employed by reverse analysis to derive a "virtual" electrogram representing the ventricular cavity surface. The data is then transferred to an analysis module, which may exist as a separate computer or be integrated into the electroanatomical mapping system. Here, the data integration and processing described in Figure 4a are performed before being integrated with a 3D anatomical representation of the heart and displayed to a user, such as a physician.

[0147] Figures 5a and 5b show cycle length (CL) histograms obtained with one of the basket-type catheter electrodes, where the percentage of recordings is comprised of each CL on the y-axis and CL on the x-axis. Each bar represents a defined CL. For illustrative purposes, less frequent CLs along the mean were excluded. Figure 5a shows the minimum CL at 131 ms, which also represents a dominant CL. CLs at 102 ms and 105 ms are excluded as part of the methodology, as they represent more than 30% of the average CL recorded with that electrode and less than 10% of all recorded CLs. Figure 5b shows the minimum CL at 135 ms, while the dominant CL is 142 ms. Again, CLs at 105 ms and 109 ms are excluded as part of the methodology, as described above.

[0148] Figure 6 shows the identified potential AFDs and potential AFDs associated with the ablation response, as well as some of these being minimal CL, minimal CLV, and localized DF. gradient This is a research flowchart showing whether the site is located in the same place as the LVZ (Low Level Zone).

[0149] Figures 7A-7F show images and electrographs obtained from the subject (patient number 6). (Figure 7A) STAR map of LA in a titled roof view showing a latent AFD mapped to the intermediate roof. (Figure 7B) CL map in the titled roof view demonstrates the fastest minimum CL overall in the mapped latent AFD. (Figure 7C) CLV map in the titled roof view demonstrates the lowest CLV in the mapped latent AFD. (Figure 7D) Electrographs obtained at the latent AFD (highlighted with a star) and adjacent electrodes, assigned by the STAR mapping method. The electrograph obtained at the latent AFD demonstrates that this region is leading compared to its adjacent pair and also has a faster CL. (Figure 7E) CARTO map of LA in a roof view showing ablation in the latent AFD guided by the STAR map. (Figure 7F) Electrograph shows termination from AF to sinus rhythm in ablation at the latent AFD. LUPV - Left superior pulmonary vein RUPV - Right Upper Pulmonary Vein LAA-left atrial appendage MVA-Mitral valve ring

[0150] Figures 8A–8C are images from different subjects (patient 16). (Figure 8A) Shows a STAR map of the LA in the title lateral view indicating potential AFD. (Figure 8B) Ablation was performed at this site, as demonstrated in the CARTO left atrial map in the title lateral view which resulted in AF termination to the AT. (Figure 8C) The potential AFD was located in the same place as the site of the fastest locally obtained CL, with decreasing CL obtained from adjacent electrodes. LUPV - Left superior pulmonary vein LAA-left atrial appendage MVA-Mitral valve ring

[0151] Figures 9A-9C are images from yet another subject (patient ID 18). (Figure 9A) Shows a STAR map of the LA in a lateral view showing potential AFD. (Figure 9B) The potential AFD was located in the same place as the site of the highest local DF as the DF obtained from the adjacent electrode decreased. (Figures 9Ci-9Cii) Ablation of the potential AFD delayed the CL from 164 ms to 229 ms, as shown in the electrophoresis obtained from BARD. LUPV - Left superior pulmonary vein LAA-left atrial appendage MVA-Mitral valve ring

[0152] Experimental results

[0153] To further refine and develop STAR mapping, the inventors conducted experiments as described below and established a set of parameters that can be used as modifiers to determine further factors that may classify potential driver sites as true driver sites in terms of their importance or potential.

[0154] Parameter development methods

[0155] All patients had high-density bipolar voltage maps created using a PentaRay Nav catheter with electrode spacings of 2-6-2 mm. Points greater than 3 mm from the geometric surface were filtered out as not in contact with the myocardium, and the acquired points were respiratory-gated to optimize the accuracy of anatomical localization.

[0156] Parameters considered

[0157] Bipolar voltage and AFD

[0158] To ensure adequate atrial coverage, a minimum of 800 bipolar voltage points were taken for each patient. An interpolation threshold of 5 mm was set for surface color projection, and points were collected with the goal of achieving complete LA coverage (i.e., no areas exceeding 5 mm from the data point).

[0159] The region with a bipolar voltage of less than 0.5mV was defined as the low voltage zone (LVZ). The voltage map was divided into non-LVZ and LVZ zones. Next, the identified potential AF drivers were broadly categorized as existing in either the non-LVZ or LVZ zone. The relationship between potential AFDs that map to the LVZ and achieve AF termination during ablation was evaluated. The relationship between the proportion of LVZ and the number of identified AFDs was also evaluated.

[0160] Spectral analysis and AFD

[0161] Using unipolar signals recorded from basket catheters, CL was determined for each electrode pole in contact over each 5-minute recording in each patient. CL was measured as the time difference between two consecutive atrial signals using an automated custom-described Matlab script. A custom algorithm was used to model reasonable biological behavior based on well-described refractory periods. This algorithm was used to avoid double counting of split potentials. Unipolar excitation timing was defined as the maximum negative deviation (peak negative dv / dt).

[0162] A histogram of all identified CLs (rounded to the nearest integer millisecond) was plotted on the x-axis, along with the percentage of recordings comprised by each CL on the y-axis, for each individual electrode for each 5-minute recording in each patient. Various methods have been previously used to identify the site of rapid 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 containing 50% of the cycles, defining this as the “dominant CL”. The dominant CL for each electrode is then projected onto a replica of the anatomical geometry to identify the site of the fastest dominant CL, defined as the upper decile value, and their spatial relationship with AFD is evaluated.

[0163] In a novel methodology for determining the location(s) of the fastest CL, defined as the minimum CL (Min-CL), extreme CL outliers were initially ignored, defined as CLs that were 30% slower or faster than the average CL at the electrode and contained more than 10% of the cycles. From the remaining CLs, the minimum CL was identified for each electrode (Figure 1). Thus, the minimum CL was the shortest CL that constituted more than 10% of the cycles after elimination of outliers. The minimum CLs were compared across all electrodes to identify the location(s) of the fastest overall minimum CL in the LA. Two definitions of the fastest overall minimum CL were tested: (i) the minimum CL within the upper decile of all electrodes, and / or (ii) a single shortest overall minimum CL or "absolute minimum CL," and any other point with a minimum CL that is within 5% of the fastest overall minimum CL. The locations of the electrode(s) with the fastest overall minimum CL were then identified on the same STAR map, demonstrating ESA to ensure accurate anatomical correlations.

[0164] Cycle length variability (CLV) was used as a marker for organization. CLV was determined for each electrode by taking the standard deviation (SD) of CL. Therefore, a smaller CLV indicates less CL variability. CLV was compared across all electrodes to identify the site of the overall lowest CLV in the LA. The site of the lowest CLV was defined as (i) the CLV at the lowest decile, and / or (ii) the absolute lowest CLV, which is within 5% of the overall lowest CLV. The location of the electrode(s) with the lowest CLV was then identified on the STAR map and correlated again with the AFD.

[0165] Local CL and frequency in latent AFD gradient

[0166] The minimum CL at the potential AFD determined using the STAR mapping method is compared with the minimum CL obtained at the adjacent electrode, and the CL from the potential AFD to the surrounding area is compared. gradient This was used to determine whether or not there was a potential AFD. This was repeated for all potential AFDs identified in each STAR map for each patient. Adjacent electrodes were defined as electrodes that were within 3 cm of the potential AFD.

[0167] To determine the DF, a Butterworth second-order filter was applied to the unipolar signal following filtering of the remote field ventricular signal. The signal was then rectified and its absolute value was taken, followed by the application of a low-pass filter to the signal. A Hamming window was then applied, and a Fourier transform was performed. The DF was then determined for each 4-second window. The median of all these values ​​recorded over 5 minutes was taken to determine the electrode's DF. The DF at electrodes identified as potential AFDs was compared to the DF obtained at adjacent electrodes to determine the high-low frequency response from the potential AFD to the surrounding region. gradientWe determined whether or not there was a problem. This was repeated for all potential AFDs identified in each STAR map for each patient. The relationship between the LA site and the overall highest DF, as well as the potential AFD, was also evaluated according to their upper decile values.

[0168] Result summary

[0169] Thirty-two patients were included, of which 83 potential AFDs identified by STAR mapping were targeted for ablation. Ablation responses were observed at 73 sites (24 AF arrests and 49 CL delays of ≥30ms). Of these 73 sites, 54 (74.0%) and 55 (75.3%) were located in the same locations as the sites with the fastest overall CL and lowest CLV, respectively. However, when using conventional markers, the sites with the fastest dominant CL and highest DF were rarely located in the same locations as potential AFDs with ablation responses (39 sites, 53.4% ​​CL, and 41 sites, 56.2% DF). PVI did not affect potential AFDs in the form of 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 affected local CLs (61 / 73 locations, 83.6%) and frequency gradient (58 / 73 locations, 80.8%) were frequently demonstrated. Latent AFDs were generally mapped by LVZ, and the proportion of LVZs correlated with the number of identified latent AFDs (r s (=0.91; p<0.001). By utilizing these novel markers of ablation and organization in conjunction with STAR mapping, high sensitivity and specificity were enabled in predicting which potential AFDs would generate an ablation response. Potential AFDs located at the same sites as the lowest CLVs (24 / 24 sites (100%) vs. 31 / 49 sites (63.2%); p<0.001) and fastest CLs (24 / 24 sites (100%) vs. 30 / 49 sites (61.2%); p<0.001) were consistently associated with AF termination during ablation, rather than CL delay.

[0170] Result details

[0171] Thirty-two patients underwent STAR mapping-guided ablation. The mean duration of AF was 15.4 ± 4.3 months, and 21 of the 32 patients (65.6%) had undergone antiarrhythmic drug ablation beforehand.

[0172] Ablation response in latent AFD

[0173] In short, in 32 patients (2.8 ± 0.8 per patient), 92 potential AFD sites were identified by STAR mapping after PVI, of which 83 (90.2%, 2.6 ± 0.7 per patient) were targeted by ablation (Figure 3). The 9 potential AFD sites that were not ablated were patients in whom previous ablation at the same site had resulted in AF termination.

[0174] Ablation responses were achieved in 73 potential AFDs (2.3 ± 0.6 per patient), with at least one response in all 32 patients. Based on potential AFDs, AF arrest was achieved by ablation at 24 sites (18 organizing events for AT and 6 arrests for sinus rhythm), and CL delays of 30 ms or more were achieved at 49 sites. On a patient-by-patient basis, AF arrest was achieved in 24 patients, and CL delays of 30 ms or more were achieved in the remaining 8 patients.

[0175] Bipolar voltage and potential AFD

[0176] Based on patient-to-patient data, an average of 3.4 ± 0.7 clearly defined LVZs were identified. The majority of identified potential AFDs were mapped to LVZs (62 / 92 sites, 67.4%). Of the 83 potential AFDs ablated, 60 were mapped to LVZs (72.3%), and of these, 59 (98.3%) were associated with an ablation response.

[0177] Patients with more than 50% of LAs in the LVZ (59.2±6.5mV) were more likely to have more than two potential AFDs identified. There was a strong positive correlation between the proportion of LVZs present and the number of potential AFDs identified (r s =0.91; p<0.001). However, LVZ alone had almost no accuracy in predicting AFD sites.

[0178] The association between latent AFD and LVZ highly predicted the response to ablation. Latent AFD mapped to LVZ was also more frequently associated with AF termination during ablation than latent AFD mapped to non-LVZ (odds ratio = 20.0, 95% CI, 1.1–351.5; p = 0.04).

[0179] Spectral analysis and latent AFD

[0180] For spectral analysis, a total of 170 5-minute unipolar recordings were used. Of these 170 recordings, 84 were made before PVI and 86 were made after PVI. The mean dominant CL and mean minimum CL obtained in the latent AFD after PVI were 137.9 ± 64.2 ms and 131.2 ± 15.5 ms, respectively.

[0181] Co-arrangement of sites identified by spectral analysis and potential AFD

[0182] (i) Fastest Advantage CL

[0183] Of the 92 potential AFDs identified after PVI, 47 (51.1%) were located at the same site as the fastest dominant CL(s). The dominant 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 positive and negative predictive values ​​were 57.3% (95% CI, 51.2–63.2%) and 15.1% (95% CI, 8.4–25.6%), respectively.

[0184] (ii) The smallest CL site and the absolute smallest CL within the upper decile

[0185] On a patient-by-patient basis, an average of 4.1 ± 1.1 minimal CL sites were identified in the upper decile. Of the 92 potential AFDs identified after PVI, 58 (63.0%) were located at the same site as one of these sites. Minimal CL sites, such as those in the upper decile, 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.

[0186] Based on individual patients, the average of 2.5 ± 0.9 absolute minimum CL sites that were within 5% of the fastest minimum CL sites was identified. Of the 92 potential AFDs identified after PVI, 56 (60.9%) were located in the same location as one of these sites.

[0187] (iii) The lowest decile and the lowest CLV site within the absolute lowest CLV

[0188] Based on patient-specific data, an average of 3.8 ± 1.0 lowest CLV sites were identified at the top decile. Of the 92 potential AFD sites identified after PVI, 61 (66.3%) were located at the same site as one of these sites. Lowest CLV sites, such as the lowest decile sites, demonstrated 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 AFD.

[0189] Based on individual patients, these accounted for less than 5% of the total least CLV sites, with an average of 2.3 ± 0.8 least CLV sites identified. Of the 92 potential AFDs identified after PVI, 60 (65.2%) were located in the same location as one of these sites.

[0190] Use of spectral analysis to predict the response to ablation in latent AFD

[0191] (i) Fastest Advantage CL

[0192] Of the 73 potential AFDs associated with the ablation response, only 39 (53.4%) were located at the same site as the fastest-predominant CL. The fastest-predominant 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 AFDs. The positive and negative predictive values ​​were 58.2% (95% CI, 51.1–65.0%) and 22.7% (95% CI, 14.1–34.6%), respectively.

[0193] (ii) The smallest CL site and the absolute smallest CL within the upper decile

[0194] Of the 73 potential AFDs with a defined ablation response in the study, 54 (74.0%) were located at the same site as one of the smallest CL sites (Figures 3 and 4A-4F).

[0195] Co-placement of these sites has once again demonstrated high diagnostic accuracy in predicting potential AFD with ablation response.

[0196] (iii) The lowest decile and the lowest CLV site within the absolute lowest CLV

[0197] Of the 73 potential AFDs with a defined ablation response in the study, 56 (76.7%) were located at the same site as one of the lowest CLV sites defined within the lowest decyl (Figures 5a, 5b, and 6A–6F).

[0198] Of the 73 potential AFDs associated with ablation response, 55 (75.3%) were located in the same location as one of the absolute lowest CLV sites (Figures 4 and 6A–6F). Co-location to these sites again demonstrated high diagnostic accuracy in predicting potential AFDs with ablation response.

[0199] (iv) Prediction of AF stoppage

[0200] • Latent AFDs located at the same site as the smallest CL, according to either definition, were more frequently associated with AF arrest at ablation, in contrast to CL delay (24 / 24 (100%) vs. 29 / 49 (59.2%); p<0.001). This marker showed an odds ratio of 34.1 (95% CI, 2.0~592.3; p=0.02) for predicting AF arrest at ablation in AFDs.

[0201] • Latent AFDs located at the same site as the lowest CLV, according to either definition, were more frequently associated with AF arrest at ablation, in contrast to CL delay (24 / 24 (100%) vs. 31 / 49 (63.2%); p<0.001). This marker showed an odds ratio of 28.8 (95% CI, 1.7–501.6; p=0.02) for predicting AF arrest at ablation in latent AFDs.

[0202] Local CL and frequency gradient and potential AFD

[0203] Of the 92 identified potential AFDs, 56 (60.9%) had clear minimum CLs to the surrounding poles. gradient This was observed (Figures 5a, 5b, and 8A-8D). Of the 73 latent AFDs with ablation responses, 61 (83.6%) involved the fastest-slowest CL from the latent AFD to the adjacent pole. gradient This was demonstrated. The mean minimum CL reduction from a latent AFD within 3 cm to an adjacent pole was 10.5 ± 4.2 ms (p = 0.01). Local CL in latent AFD gradient The presence of [specific element] resulted in an odd ratio of 5.1 (95% CI, 1.3–20.3, p=0.02) in predicting latent AFD with ablation response.

[0204] The mean DF in potential AFDs was 6.2 ± 0.7 Hz. In 56.5% (52 / 92) of cases, potential AFDs were located only in the same location as the highest DF site in the LA, and 41 out of 73 (56.2%) of cases were accompanied by an ablation response located in the same location as the highest DF site. Of the 92 identified potential AFDs, there was a clear frequency from the potential AFD to the adjacent pole with 59 potential AFDs (64.1%). gradient This was observed (Figures 5a, 5b, and 7A-7D). The mean decrease in DF from latent AFD to the adjacent pole was 1.8 ± 0.7 Hz (p = 0.01). When considering only the 73 latent AFDs with ablation responses, 58 (80.8%) were frequency gradient This was demonstrated. Local frequencies in latent AFD gradient The presence of frequency showed an odd ratio of 5.8 (95% CI, 1.4–23.2, p=0.01) in the prediction of latent AFD with ablation response. gradient The presence of AF did not differ significantly between those with AF arrest and those with CL delay at the time of ablation (18 / 24, 75.0% vs. 39 / 49, 79.6%; p=0.78).

[0205] The effect of PVI on spectral analysis data

[0206] Of the 92 latent AFDs identified after PVI, 42 were also detected on the pre-PVI map, of which 39 (92.9%) were associated with the ablation response as defined in the study. The mean pre-PVI CLV (obtained by taking the mean CLV of all electrodes) was lower in patients with both pre-PVI identified latent AFDs compared to patients with only post-PVI identified latent AFDs (31.4±4.8ms vs. 49.5±7.3ms; p=0.01).

[0207] PVI did not affect CL (131.0±12.1ms before PVI vs. 131.2±15.5ms; p=0.96 after PVI) and CLV (10.3±3.9ms before PVI vs. 11.0±5.4ms after PVI; p=0.80) in potential AFD.

[0208] Latent AFD identified prior to PVI also showed a sensitivity of 53.4% ​​(95% CI, 41.4–65.2) and a specificity of 90.0% (95% CI, 55.5–99.8) in predicting latent AFD with ablation response. The positive predictive value and negative predictive value were 97.5% (95% CI, 85.7–99.6) and 20.9% (95% CI, 16.1–26.7), respectively.

[0209] It should be understood that certain embodiments of the present invention, as described later, can be incorporated as code (e.g., software algorithms or programs) residing on a computer-usable medium having control logic for enabling execution within firmware and / or on a computer system having a computer processor. Such a computer system typically includes memory storage configured to provide output from the execution of code that constitutes the processor according to its execution. The code can be configured as firmware or software and can be organized as a set of modules, such as code-behind modules, function calls, procedure calls, or objects in an object-oriented programming environment. When implemented using modules, the code can comprise one or more modules that work together.

[0210] Any embodiment of the present invention can be understood as comprising, individually or collectively, any or all combinations of two or more parts, elements, or features mentioned or indicated herein, and where certain integers having known equivalents in the art relating to the present invention are mentioned herein, such known equivalents are considered to be incorporated herein as if they were mentioned separately.

[0211] While exemplary embodiments of the present invention have been described, it should be understood that various modifications, substitutions, and alternatives can be made by those skilled in the art without departing from the present invention as defined by the following enumeration of claims and equivalents.

[0212] This application claims priority to British Patent No. 1915680.1, the contents of which and the abstract submitted with this specification are incorporated herein by reference.

Claims

1. A computer implementation method for identifying one or more regions of the heart that are factors supporting or initiating an abnormal heartbeat, wherein the computer uses electrographic data acquired from a corresponding series of detection locations on the heart, recorded from multiple electrodes of a multipolar cardiac catheter over a recording period, The process of identifying regions of cardiac chambers having an electrical excitation sequence that characterizes them as potential drivers of abnormal heartbeats from the aforementioned electromagnetism, For each detection position in the region or substantially surrounding the region, the step of determining the earliest excitation electrode site from the primary excitation, For each of the determined earliest excitation electrode sites, A step of calculating a value for each of a plurality of modification factors related to the electrode site, which are determined from the potential diagram data other than the excitation order for each of the electrode sites, A step of determining a ranking factor calculated from the aforementioned multiple correction factors, A step of ranking each of the earliest excitation electrode sites according to its ranking factor, A step of outputting data that identifies the region, wherein the visual prominence of each of the determined earliest excitation electrode sites changes according to the ranking, Perform The correction factor determined from the data of the aforementioned potential diagram is The minimum cycle length of the potential diagram recorded at the electrode site, the gradient of the excitation frequency between the potential diagram recorded at the electrode site or within the region and the potential diagram obtained within a predetermined geodesic distance, and the bipolar voltage of the multiple potential diagrams recorded within the region. A computer implementation method including a modification factor selected from.

2. The computer mounting method according to claim 1, wherein the modification factor is determined from the tissue characteristics of the electrode site.

3. The computer implementation method according to claim 2, wherein the correction factor determined from tissue characteristics includes measurements of the presence and density of scar tissue determined by image processing.

4. The computer implementation method according to claim 2, wherein the modification factor determined from tissue characteristics includes a measurement of tissue impedance at the electrode site.

5. The computer implementation method according to claim 2, wherein the computer determines the modification factor, which is determined from tissue characteristics, from the results of treatment at another earliest excitation electrode site.

6. The computer implementation method according to claim 2, wherein the computer calculates the modification factor determined from the tissue characteristics by referring to the results of ablation at similar sites in previous cases.

7. The computer implementation method according to claim 1, wherein the modification factor is determined from the anatomical characteristics of the electrode site.

8. The computer implementation method according to claim 7, wherein the modification factor determined from anatomical characteristics includes a predetermined weighting factor that depends on the position of the electrode site.

9. The computer implementation method according to claim 1, wherein the step of outputting the data includes the computer displaying the data by a visual display in which the visual prominence of each of the determined earliest excited electrode sites changes according to the ranking.

10. The computer To determine the main potential wavefront trajectory of each electrode site; Determining the excitation vectors of the main derived electrophysics across multiple electrodes and excitations; If the direction of excitation progresses from one of the multiple electrode sites from which the vector has been determined to one or more nearby electrode sites, and the order and timing of the excitations are within a predetermined biologically reasonable range compared to the conduction velocity and conduction path of a reasonable sequence of excitations, then classifying that electrode site as the earliest excited electrode site and a potential driver site; further comprising: The computer implementation method according to claim 1.

11. The computer is If excitation occurs across at least two electrodes such that an arc with an angle less than a predetermined angle is defined centered on a vertex defined in the potential driver site, and if the excitation of at least one potential diagram within the arc defined as the excited tissue is determined to be slower than the excitation in the potential driver site, then the potential driver site is classified as the earliest excited electrode site. The computer implementation method according to claim 1.

12. The computer mounting method according to claim 11, wherein the predetermined angle is less than 180°.

13. The computer implementation method according to claim 1, further comprising the step of the computer outputting data to a display or medical scanning device to display or navigate the geometric shape of the cardiac chambers and guiding the placement of a catheter or electrode for the acquisition of subsequent electrophoretic data.

14. The computer implementation method according to claim 13, further comprising the step of the computer identifying a site to position the catheter or the electrode so that data for the next electromagnetism can be acquired in order to eliminate or confirm an electrode at a detection site where the earliest excitation electrode site has already been determined, or to extend the electromagnetism data to areas that have not been previously scanned or have been incompletely scanned.

15. The computer implementation method according to claim 14, further comprising the step of the computer identifying a site for the next catheter or electrode from a detection site where the earliest excited electrode site has already been identified, or from the excitation vector and the gradient of the ranking factor.

16. The computer implementation method according to claim 1, further comprising the steps of: the computer receiving electromagnetism data in substantially real time; and the computer displaying a determination of whether a sufficient acquisition period has been taken for the part of the electromagnetism data being scanned.

17. The computer implementation method according to claim 16, wherein the step of displaying the determination of whether a sufficient acquisition period has been taken includes one or more of the following: the computer counts down a predetermined period; acquires a predetermined number of excitation cycles at at least a predetermined minimum number of electrodes of a movable multipolar mapping catheter; and acquires a predetermined time or a predetermined number of excitation cycles in which the excitation patterns across the plurality of electrodes remain within the same pattern sequence.

18. The computer implementation method according to claim 16, wherein the step of displaying the determination of whether a sufficient acquisition period has been taken includes the computer determining that the excitation pattern of the region from which the electrophoretic diagram data is being scanned has reached a predetermined level of statistical certainty.

19. The computer implementation method according to claim 1, further comprising: the step of the computer deriving a wavefront direction by determining which electrode is leading for all electrode pairs; and the step of the computer processing the wavefront direction to determine whether the earliest excited electrode site is a true AFD or represents a passively excited site excited beyond the measurement boundary.

20. A computer system for identifying one or more regions of the myocardium that are contributing to or initiating an abnormal heartbeat, using electrographic data obtained from a series of corresponding detection locations on the heart, recorded from multiple electrodes of a multipolar cardiac catheter over a recording period, wherein the system Processor and; A first memory for storing the received potentiometer data; A second memory where the program code is stored and The system is provided, and when the program code is executed by the processor, the system From the aforementioned electrophysics diagram, identify regions of cardiac chambers that have an electrical excitation sequence that characterizes them as potential drivers of abnormal heartbeats, For each detection location in or substantially surrounding the said region, the earliest excitation electrode site is determined from the primary excitation. For each of the determined earliest excitation electrode sites, The calculation of a value for each of several modification factors related to the electrode site, which are determined from the data of the potential diagram other than the excitation order for the electrode site, The ranking factor calculated from the aforementioned multiple modification factors is determined, Each of the earliest excitation electrode sites is ranked according to its ranking factor, and Outputting data that identifies the region, wherein the visual prominence of each of the determined earliest excited electrode sites changes according to the ranking. Perform When the program code is executed by the processor, the system determines the correction factor determined from the data of the potential diagram. The correction factors include the minimum cycle length of the potential diagram recorded at the electrode site, the gradient of excitation frequency between the potential diagram recorded at the electrode site or within the region and the potential diagram obtained within a predetermined geodesic distance, and the bipolar voltage of the multiple potential diagrams recorded within the region. A computer system that includes one or more of the following.

21. The computer system according to claim 20, wherein when the program code is executed by the processor, the system also determines a modification factor based on the tissue characteristics of the electrode site.

22. The computer system according to claim 21, wherein when the program code is executed by the processor, the system determines the modification factor based on tissue characteristics by obtaining measurements of the presence and density of scar tissue determined by image processing.

23. The computer system according to claim 21, wherein when the program code is executed by the processor, the system determines the modification factor based on tissue characteristics by obtaining a measurement of the tissue impedance at the electrode site.

24. The computer system according to claim 21, wherein, when the program code is executed by the processor, the system determines the modification factor based on tissue characteristics by obtaining it from the results of treatment of another earliest excitation electrode site.

25. The computer system according to claim 21, wherein when the program code is executed by the processor, the system determines the modification factor based on tissue characteristics by accessing data relating to the results of ablation at similar sites in previous cases.

26. The computer system according to claim 20, wherein when the program code is executed by the processor, the system also determines a modification factor based on the anatomical characteristics of the electrode site.

27. The computer system according to claim 26, wherein when the program code is executed by the processor, the system determines the modification factor by accessing data and obtaining a predetermined weighting factor that depends on the position of the electrode portion.

28. When the program code is executed by the processor, the system To determine the main potential wavefront trajectory of each electrode site; Determining the excitation vectors of the main derived electrophysics across multiple electrodes and excitations; If the direction of excitation progresses from one of the multiple electrode sites from which the vector has been determined to one or more nearby electrode sites, and the order and timing of the excitations are within a predetermined biologically reasonable range compared to the conduction velocity and conduction path of a reasonable sequence of excitations, then the electrode site is classified as the earliest excited electrode site and the potential driver site; The computer system according to claim 20.

29. The computer system according to claim 20, wherein when the program code is executed by the processor, the system outputs a visual display for each of the determined earliest excited electrode sites, the visual prominence of which changes according to the ranking.

30. The computer system according to claim 20, wherein, when the program code is executed by the processor, the system outputs data to a display or medical scanning device to display or navigate the geometric shape of the cardiac chambers and guide the placement of a catheter or electrode for the acquisition of subsequent electrophoretic data.

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