System for generating a composite map

Abstract

To provide an improved system for mapping cardiac electrical activity.SOLUTION: A system for modeling cardiac electrical activity data of a patient includes at least one diagnostic catheter for insertion into a heart of the patient and a processing unit. The at least one diagnostic catheter includes at least one recording element and records patient data over multiple cardiac cycles. The patient data includes biopotential data and localization data of the at least one recording element. The processing unit includes a clustering routine that segments the recorded patient data by cardiac cycle and combines the segmented patient data in each segmented data group to create one or more composite records. The system generates one or more models of the patient's cardiac electrical activity based on the one or more composite records.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates generally to systems and methods for the diagnosis and treatment of cardiac arrhythmias or other abnormalities, and more particularly, the present invention relates to systems, devices, and methods for mapping cardiac electrical activity. [Background technology]

[0002] (Related Applications) This application may be related to (but does not claim priority from) U.S. Provisional Application No. 62 / 835,538, entitled "System for Creating a Composite Map," filed April 18, 2019, which is incorporated herein by reference.

[0003] This application may be related to (but does not claim priority from) U.S. Application No. 16 / 335,893, entitled "Ablation System with Force Control," filed March 22, 2019, which is a national stage application under 35 U.S.C. § 371 of Patent Cooperation Treaty Application No. PCT / US2017 / 056064, entitled "Ablation System with Force Control," filed October 11, 2017, published as WO2018071490, which claims priority to U.S. Provisional Application No. 62 / 406,748, entitled "Ablation System with Force Control," filed October 11, 2016, and U.S. Provisional Application No. 62 / 504,139, entitled "Ablation System with Force Control," filed March 20, 2017, each of which is incorporated herein by reference.

[0004] This application may be related to (but does not claim priority from) Patent Cooperation Treaty Application No. PCT / US2017 / 030915, entitled "Cardiac Information Dynamic Display System and Method," filed May 3, 2017, which claims priority to U.S. Provisional Patent Application No. 62 / 331,351, entitled "Cardiac Information Dynamic Display System and Method," filed May 3, 2016, each of which is incorporated herein by reference.

[0005] This application may be related to (but does not claim priority from) U.S. Application No. 14 / 422,941, entitled "Catheter, System and Methods of Medical Uses of Same, Including Diagnostic and Treatment Uses for the Heart," filed February 20, 2015, which is a national stage application under 35 U.S.C. § 371 of Patent Cooperation Treaty Application No. PCT / US2013 / 057579, entitled "Catheter System and Methods of Medical Uses of Same, Including Diagnostic and Treatment Uses for the Heart," filed August 30, 2013, published as WO2014 / 036439, which is entitled "System and Method for Diagnosing and Treating Heart," filed August 31, 2012. This application claims priority to U.S. Provisional Patent Application No. 61 / 695,535, entitled "Synthetic Tissue," each of which is incorporated herein by reference.

[0006] This application may be related to (but does not claim priority from) U.S. Application No. 14 / 762,944, entitled "Expandable Catheter Assembly with Flexible Printed Circuit Board (PCB) Electrical Pathways," filed July 23, 2015, which is a national stage application under 35 U.S.C. § 371 of Patent Cooperation Treaty Application No. PCT / US2014 / 015261, entitled "Expandable Catheter Assembly with Flexible Printed Circuit Board (PCB) Electrical Pathways," filed February 7, 2014, published as WO2014 / 124231, which is a national stage application under 35 U.S.C. § 371 of Patent Cooperation Treaty Application No. PCT / US2014 / 015261, entitled "Expandable Catheter Assembly with Flexible Printed Circuit Board (PCB) Electrical Pathways," filed February 8, 2013, published as WO2014 / 124231. This application claims priority to U.S. Provisional Patent Application No. 61 / 762,363, entitled "Pathways," each of which is incorporated herein by reference.

[0007] This application may be related to (but does not claim priority from) U.S. patent application Ser. No. 14 / 865,435, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls," filed September 25, 2015, which is a continuation of U.S. Patent No. 9,167,982, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls," filed November 19, 2014, which is a continuation of U.S. Patent No. 8,918,158, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls" (hereinafter the '158 patent), issued December 23, 2014, which is a continuation of U.S. Patent No. 8,918,158, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls" (hereinafter the '158 patent), issued April 15, 2014. No. 8,700,119 (hereinafter the '119 patent), entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls," which issued on April 9, 2013, is a continuation of U.S. Patent No. 8,417,417, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls" (hereinafter the '119 patent).'313 (hereinafter the '313 patent), which is a continuation of PCT Application No. CH2007 / 000380, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls," filed August 3, 2007, published as WO2008 / 014629, which claims priority to Swiss Patent Application No. 1251 / 06, filed August 3, 2006, each of which is incorporated herein by reference.

[0008] This application may be related to (but does not claim priority from) U.S. patent application Ser. No. 14 / 886,449, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," filed on October 19, 2015, which is a continuation of U.S. Patent Application No. 9,192,318, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," filed on July 19, 2013, which is a continuation of U.S. Patent Application No. 9,192,318, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," filed on August 20, 2013, and published as US 2010 / 0298690 (hereinafter the '690 Publication). No. 8,512,255, entitled "A Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," which is a continuation of U.S. Pat. No. 8,512,255, entitled "A Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," which is a 35 U.S.C. national stage application of Patent Cooperation Treaty Application No. PCT / IB09 / 00071, entitled "A Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," filed January 16, 2009, published as WO2009 / 090547, which claims priority to Swiss Patent Application No. 00068 / 08, filed January 17, 2008, each of which is incorporated herein by reference.

[0009] This application may be related to (but does not claim priority from) U.S. Application No. 14 / 003,671, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," filed September 6, 2013, which is a national stage application under 35 U.S.C. § 371 of Patent Cooperation Treaty Application No. PCT / US2012 / 028593, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," published as WO 2012 / 122517 (hereinafter the '517 publication), which claims priority to U.S. Provisional Application No. 61 / 451,357, each of which is incorporated herein by reference.

[0010] This application may be related to (but does not claim priority from) U.S. Design Application No. 29 / 475,273, entitled "Catheter System and Methods of Medical Uses of Same, Including Diagnostic and Treatment Uses for the Heart," filed December 2, 2013, which is a national stage application under 35 U.S.C. § 371 of Patent Cooperation Treaty Application No. PCT / US2013 / 057579, entitled "Catheter System and Methods of Medical Uses of Same, Including Diagnostic and Treatment Uses for the Heart," filed August 30, 2013, which claims priority to U.S. Provisional Patent Application No. 61 / 695,535, entitled "System and Method for Diagnosing and Treating Heart Tissue," filed August 31, 2012, which is incorporated herein by reference.

[0011] This application may be related to (but does not claim priority from) Patent Cooperation Treaty Application No. PCT / US2014 / 15261, entitled "Expandable Catheter Assembly with Flexible Printed Circuit Board (PCB) Electrical Pathways," filed February 7, 2014, which claims priority to U.S. Provisional Patent Application No. 61 / 762,363, entitled "Expandable Catheter Assembly with Flexible Printed Circuit Board (PCB) Electrical Pathways," filed February 8, 2013, which is incorporated herein by reference.

[0012] This application may be related to (but does not claim priority from) Patent Cooperation Treaty Application No. PCT / US2015 / 11312, entitled "Gas-Elimination Patient Access Device," filed January 14, 2015, which claims priority to U.S. Provisional Patent Application No. 61 / 928,704, entitled "Gas-Elimination Patient Access Device," filed January 17, 2014, which is incorporated herein by reference.

[0013] This application may be related to (but does not claim priority from) Patent Cooperation Treaty Application No. PCT / US2015 / 22187, filed March 24, 2015, entitled "Cardiac Analysis User Interface System and Method," which claims priority to U.S. Provisional Patent Application No. 61 / 970,027, filed March 28, 2014, entitled "Cardiac Analysis User Interface System and Method," which is incorporated herein by reference.

[0014] This application may be related to (but does not claim priority from) Patent Cooperation Treaty Application No. PCT / US2014 / 54942, filed September 10, 2014, entitled "Devices and Methods for Determination of Electrical Dipole Densities on a Cardiac Surface," which claims priority to U.S. Provisional Patent Application No. 61 / 877,617, filed September 13, 2013, entitled "Devices and Methods for Determination of Electrical Dipole Densities on a Cardiac Surface," which is incorporated herein by reference.

[0015] This application may be related to (but does not claim priority from) U.S. Provisional Patent Application No. 62 / 161,213, entitled "Localization System and Method Useful in the Acquisition and Analysis of Cardiac Information," filed May 13, 2015, which is incorporated herein by reference.

[0016] This application may be related to (but does not claim priority from) U.S. Provisional Patent Application No. 62 / 160,501, entitled "Cardiac Virtualization Test Tank and Testing System and Method," filed May 12, 2015, which is incorporated herein by reference.

[0017] This application may be related to (but does not claim priority from) U.S. Provisional Patent Application No. 62 / 160,529, entitled "Ultrasound Sequencing System and Method," filed May 12, 2015, which is incorporated herein by reference.

[0018] This application may be related to (but does not claim priority from) U.S. Provisional Patent Application No. 62 / 619,897, entitled "System for Recognizing Cardiac Conduction Patterns," filed January 21, 2018, which is incorporated herein by reference.

[0019] This application may be related to (but does not claim priority from) U.S. Provisional Patent Application No. 62 / 668,647, entitled "System for Identifying Cardiac Conduction Patterns," filed May 8, 2018, which is incorporated herein by reference.

[0020] This application may be related to (but does not claim priority from) U.S. Provisional Patent Application No. 62 / 668,659, entitled "Cardiac Information Processing System," filed May 8, 2018, which is incorporated herein by reference.

[0021] This application may be related to (but does not claim priority from) U.S. Provisional Patent Application No. 62 / 757,961, entitled "Systems and Methods for Calculating Patient Information," filed November 9, 2018, which is incorporated herein by reference.

[0022] This application may be related to (but does not claim priority from) U.S. Provisional Patent Application No. 62 / 811,735, entitled "Cardiac Information Processing System," filed February 28, 2019, which is incorporated herein by reference.

[0023] Cardiac signals (e.g., charge density, dipole density, voltage, etc.) vary in magnitude across the endocardial surface. The magnitude of these signals depends on several factors, including local tissue characteristics (e.g., healthy vs. diseased / scar / fibrosis / lesion) and local activation characteristics (e.g., the "electrical mass" of activated tissue prior to local cell activation). A common practice is to assign a single threshold for all signals at all times across the surface. The use of a single threshold can cause low-amplitude activation to be overlooked or can cause high-amplitude activation to dominate / saturate, leading to confusion in map interpretation. Failure to properly detect activation can lead to inaccurate identification of regions of interest for therapy delivery or incomplete characterization of ablation efficacy (excess or lack of block).

[0024] Continuous global mapping of atrial fibrillation produces a vast array of temporally and spatially variable activation patterns. Limited, discrete sampling of map data may be insufficient to provide a comprehensive picture of the drivers, mechanisms, and supporting substrates of arrhythmias. Long-term clinician review of AF may be challenging to recall and generate a valuable clinical diagnosis. A need exists for improved systems, methods, and devices for mapping cardiac electrical activity. [Prior art documents] [Patent documents]

[0025] [Patent Document 1] U.S. Patent Application Publication No. 2002 / 0045810 [Patent Document 2] U.S. Patent Application Publication No. 2015 / 0223757 Summary of the Invention [Means for solving the problem]

[0026] According to one aspect of the inventive concept, there is provided a system for modeling a patient's cardiac electrical activity data, the system including at least one diagnostic catheter for insertion into the patient's heart, the at least one diagnostic catheter including at least one recording element configured to record patient data over a plurality of cardiac cycles, the patient data including biopotential data and localization data including a location of the at least one recording element, and the system also includes a processing unit including a clustering routine configured to receive the recorded patient data, segment the recorded patient data by cardiac cycle to produce segmented patient data including the segments, group the segments based on one or more characteristics of the segments to produce segmented data groups, and combine the segmented patient data within each segmented data group to produce one or more composite records. The system may be configured to generate one or more models of the patient's cardiac electrical activity based on the one or more composite records.

[0027] In some embodiments, the one or more models of cardiac electrical activity include two or more models of cardiac electrical activity.

[0028] In some embodiments, the biopotential data includes a biopotential signal recorded by each of at least one recording element, and segmenting the recorded patient data includes segmenting each of the biopotential signals by cardiac cycle into a plurality of biopotential signal segments; each of the one or more composite recordings includes two or more of the plurality of biopotential signal segments. Two or more of the plurality of biopotential signal segments may include at least 1,000 biopotential signal segments. Two or more of the plurality of biopotential signal segments may include at least 2,000 biopotential signal segments. Two or more of the plurality of biopotential signal segments may include at least 5,000 biopotential signal segments.

[0029] In some embodiments, the one or more segment characteristics are selected from the group consisting of: pattern; period length; signal morphology; amplitude; frequency; frequency content; wavelet composition; and combinations thereof.

[0030] In some embodiments, the clustering routine comprises an algorithm selected from the group consisting of a connectivity model-based algorithm, such as a hierarchical clustering algorithm, where the model is based on distance connectivity; a centroid model-based algorithm, such as a k-means clustering algorithm, where each cluster is represented by a single mean vector; a density model-based algorithm that defines clusters as being connected to dense regions in the data space; a distribution model-based algorithm, such as a Gaussian mixture model clustering algorithm, where clusters are modeled using a statistical distribution, such as a multivariate normal distribution; a graph-based model algorithm; a neural model-based algorithm, such as a self-organizing map and / or other unsupervised neural network, where artificial neural networks and / or other non-linear statistical data modeling tools are used to model complex relationships and patterns in the data; and combinations thereof.

[0031] In some embodiments, the system further includes an automatic timing annotation algorithm configured to identify and annotate one or more signal characteristics of cardiac electrical activity, the characteristics may correspond to cardiac tissue depolarization, activation, and / or repolarization.

[0032] In some embodiments, the segments are grouped based on template matching, which can be based on one or more segment templates, which can be dynamically adjusted.

[0033] In some embodiments, one or more of the segmented data groups are fused to form a fused group of all segments in the one or more segmented data groups.

[0034] In some embodiments, the system further includes a display. One or more models of cardiac electrical activity may be shown on the display. The one or more models may be shown on the display during recording. Portions of the one or more models may be shown on the display during recording. The operator may be shown visual feedback information on the display in a closed-loop manner. The feedback information may be configured to prompt the operator to perform actions selected from the group consisting of expanding, extending, and / or modifying the operator-determined pattern to include regions with scarce data, regions with insufficient data, and / or regions with insufficient spatially distributed data; improving data quantity and / or quality in a particular region of interest; replacing data within a region; achieving full coverage, including high-quality data, across the complete ventricular cavity surface and / or throughout the entire volume being assessed; and combinations thereof. The visual feedback information may be configured to indicate the quantity and / or quality of the recorded patient data. The quantity and / or quality may be determined over time. The quantity and / or quality may be determined across space.

[0035] In some embodiments, at least one of the at least one recording element is in contact with cardiac tissue for at least a portion of the data recording.

[0036] In some embodiments, at least one of the at least one recording element is not in contact with cardiac tissue for at least a portion of the data recording. At least one of the at least one recording element can be in contact with cardiac tissue for at least a portion of the data recording. Data recorded by a recording element in contact with cardiac tissue can be processed separately from data recorded by a recording element not in contact with tissue.

[0037] In some embodiments, the at least one diagnostic catheter includes at least two diagnostic catheters, each diagnostic catheter including at least one recording element.

[0038] In some embodiments, the at least one recording element comprises an array of recording elements. The array can comprise a basket array. The array can include at least 48 recording elements. The array of recording elements can be steered during recording to cover at least 25%, 40%, and / or 60% of the volume of the ventricular cavity.

[0039] In some embodiments, the clustering routine may include a detection rejection algorithm configured to identify undesirable signal characteristics.

[0040] In some embodiments, the clustering routine is configured to filter out inconsistent, erroneous, and / or otherwise undesired data.

[0041] The technology described herein, together with its attributes and attendant advantages, will be best appreciated and understood in consideration of the following detailed description taken in conjunction with the accompanying drawings, in which representative embodiments are illustrated by way of example.

[0042] According to an aspect of the inventive concept, there is provided a system for modeling cardiac electrical activity data, the system including: a plurality of transducers for sensing locations of features of a patient's heart; a plurality of sensors for sensing biopotential data of the patient's heart; a recording unit for recording the sensed locations and biopotential data; and a processing unit including a clustering routine. The processing unit includes one or more processors configured to receive recorded patient data including the sensed locations and biopotential data related to the patient's heart; segment the recorded patient data by cardiac cycle to produce segmented patient data including the segments; group the segments based on one or more characteristics of the segments to produce segmented data groups; and combine the segmented patient data within each segmented data group to produce one or more composite records. The system is configured to generate one or more models of the patient's cardiac electrical activity based on the one or more composite records.

[0043] In various embodiments, the biopotential data includes biopotential signals produced by each of the sensors, and segmenting the recorded patient data includes segmenting each of the biopotential signals by cardiac cycle into a plurality of biopotential signal segments; and / or each of the one or more composite recordings includes two or more of the plurality of biopotential signal segments.

[0044] In various embodiments, the one or more segment characteristics are selected from the group consisting of: pattern; period length; signal morphology; amplitude; frequency; frequency content; wavelet composition; and combinations thereof.

[0045] In various embodiments, the clustering routine comprises an algorithm selected from the group consisting of a connectivity model-based algorithm, such as a hierarchical clustering algorithm, where the model is based on distance connectivity; a centroid model-based algorithm, such as a k-means clustering algorithm, where each cluster is represented by a single mean vector; a density model-based algorithm that defines clusters as being connected to dense regions in the data space; a distribution model-based algorithm, such as a Gaussian mixture model clustering algorithm, where clusters are modeled using statistical distributions such as multivariate normal distributions; a graph-based model algorithm; a neural model-based algorithm, such as a self-organizing map and / or other unsupervised neural network, where artificial neural networks and / or other non-linear statistical data modeling tools are used to model complex relationships and patterns in the data; and combinations thereof.

[0046] In various embodiments, the system further includes a display on which the one or more models are shown during recording and the operator is shown visual feedback information on the display in a closed-loop manner.

[0047] In various embodiments, the feedback information is configured to prompt the operator to perform an action selected from the group consisting of expanding, extending, and / or modifying the operator-determined pattern to include regions with scarce data, regions with insufficient data, and / or regions with poorly spatially distributed data; improving the amount and / or quality of data in a particular region of interest; replacing data within a region; achieving full coverage with high-quality data across the complete ventricular cavity surface and / or throughout the entire volume being assessed; and combinations thereof.

[0048] In various embodiments, the clustering routine includes a detection rejection algorithm configured to identify undesirable signal characteristics.

[0049] In various embodiments, the clustering routine is configured to filter out inconsistent, erroneous, and / or otherwise undesired data.

[0050] According to another aspect of the inventive concept, there is provided an ablation system including at least one diagnostic catheter for insertion into a patient's heart, the at least one diagnostic catheter including at least one recording element configured to record patient data over multiple cardiac cycles. The patient data includes biopotential data and localization data including a location of the at least one recording element. The system also includes an ablation catheter including an elongate shaft with a distal portion and at least one ablation element positioned on the distal portion of the ablation catheter shaft and configured to deliver energy to tissue. The system also includes a processing unit including a clustering routine configured to receive the recorded patient data; segment the recorded patient data by cardiac cycle to create segmented patient data including the segments; group the segments based on one or more characteristics of the segments to create segmented data groups; and combine the segmented patient data within each segmented data group to create one or more composite records. The system is configured to generate one or more models of the patient's cardiac electrical activity based on the one or more composite records.

[0051] In various embodiments, the biopotential data includes biopotential signals produced by each of the sensors, and segmenting the recorded patient data includes segmenting each of the biopotential signals by cardiac cycle into a plurality of biopotential signal segments; and / or each of the one or more composite recordings includes two or more of the plurality of biopotential signal segments.

[0052] In various embodiments, the one or more segment characteristics are selected from the group consisting of: pattern; period length; signal morphology; amplitude; frequency; frequency content; wavelet composition; and combinations thereof.

[0053] In various embodiments, the clustering routine comprises an algorithm selected from the group consisting of a connectivity model-based algorithm, such as a hierarchical clustering algorithm, where the model is based on distance connectivity; a centroid model-based algorithm, such as a k-means clustering algorithm, where each cluster is represented by a single mean vector; a density model-based algorithm that defines clusters as being connected to dense regions in the data space; a distribution model-based algorithm, such as a Gaussian mixture model clustering algorithm, where clusters are modeled using statistical distributions such as multivariate normal distributions; a graph-based model algorithm; a neural model-based algorithm, such as a self-organizing map and / or other unsupervised neural network, where artificial neural networks and / or other non-linear statistical data modeling tools are used to model complex relationships and patterns in the data; and combinations thereof.

[0054] In various embodiments, the system further includes a display on which the one or more models are shown during recording and the operator is shown visual feedback information on the display in a closed-loop manner.

[0055] In various embodiments, the feedback information is configured to prompt the operator to perform an action selected from the group consisting of expanding, extending, and / or modifying the operator-determined pattern to include regions with scarce data, regions with insufficient data, and / or regions with poorly spatially distributed data; improving the amount and / or quality of data in a particular region of interest; replacing data within a region; achieving full coverage with high-quality data across the complete ventricular cavity surface and / or throughout the entire volume being assessed; and combinations thereof.

[0056] In various embodiments, the clustering routine includes a detection rejection algorithm configured to identify undesirable signal characteristics.

[0057] In various embodiments, the clustering routine is configured to filter out inconsistent, erroneous, and / or otherwise undesired data.

[0058] In various embodiments, the system further includes an energy source configured to provide energy to at least one ablation element of the ablation catheter, the energy source configured to provide a form of energy selected from the group consisting of radiofrequency energy; cryogenic energy; laser energy; light energy; microwave energy; ultrasound energy; chemical energy; and combinations thereof.

[0059] According to another aspect of the inventive concept, there is provided an ablation system including at least one diagnostic catheter for insertion into a patient's heart, the at least one diagnostic catheter including at least one recording element configured to record patient data over multiple cardiac cycles. The patient data includes biopotential data and localization data including a location of the at least one recording element. The system also includes an ablation catheter including an elongate shaft with a distal portion and at least one ablation element positioned on the distal portion of the ablation catheter shaft and configured to deliver energy to tissue. The system also includes a processing unit including a clustering routine configured to receive the recorded patient data; segment the recorded patient data by cardiac cycle to create segmented patient data including the segments; group the segments based on one or more characteristics of the segments to create segmented data groups; and combine the segmented patient data within each segmented data group to create one or more composite records. The system is configured to generate one or more models of the patient's cardiac electrical activity based on the one or more composite records. The system also includes a display on which at least a portion of one of the one or more models of cardiac electrical activity is shown.

[0060] In various embodiments, the system is configured to generate one or more active area plots on the display.

[0061] In various embodiments, the system is configured to generate one or more streamline plots on the display.

[0062] In various embodiments, the system is configured to generate one or more automatic route plots on the display.

[0063] Incorporation by Reference All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. [Brief explanation of the drawings]

[0064] [Figure 1] 1 is a schematic diagram of a system configured to perform a cardiac mapping procedure consistent with the concepts of the present invention; [Figure 2] 1 is a flowchart of a method for recording and modeling a patient's electrical activity consistent with the inventive concepts. [Figure 3A] 1 is a graph of recorded electrical activity consistent with the concepts of the present invention. [Figure 3B] 1 is a graph of time-aligned recorded electrical activity consistent with the concepts of the present invention. [Figure 3C] 1 is a graph of recorded electrical activity consistent with the concepts of the present invention. [Figure 3D] 1 is a graph of recorded electrical activity consistent with the concepts of the present invention. [Figure 4] FIG. 1 illustrates a visual representation of pattern clustering consistent with the inventive concepts. [Figure 5A] 1A-1C illustrate a graphical representation of a ventricular cavity with multiple recording locations, a graph of multiple cardiac electrograms, and an activation map, respectively, consistent with the concepts of the present invention. [Figure 5B] 1A-1C illustrate a graphical representation of a ventricular cavity with multiple recording locations, a graph of multiple cardiac electrograms, and an activation map, respectively, consistent with the concepts of the present invention. [Figure 5C]1A-1C illustrate a graphical representation of a ventricular cavity with multiple recording locations, a graph of multiple cardiac electrograms, and an activation map, respectively, consistent with the concepts of the present invention. [Figure 5D] 1A-1C illustrate a graphical representation of a ventricular cavity with multiple recording locations, a graph of multiple cardiac electrograms, and an activation map, respectively, consistent with the concepts of the present invention. [Figure 6] 1 is a flowchart of a method for recording and modeling a patient's electrical activity consistent with the inventive concepts. [Figure 7] 1 is a flowchart of a method for recording and modeling a patient's electrical activity consistent with the inventive concepts. [Figure 8A] 1A-1C provide various displays of cardiac activity maps consistent with the concepts of the present invention. [Figure 8B] 1A-1C provide various displays of cardiac activity maps consistent with the concepts of the present invention. [Figure 8C] 1A-1C provide various displays of cardiac activity maps consistent with the concepts of the present invention. [Figure 9A-1] 1A-1C provide a representation of sequential data acquisition, cluster samples, fuzzy membership functions, and biodata for beats within a given group consistent with the inventive concepts. [Figure 9A-2] 1A-1C provide a representation of sequential data acquisition, cluster samples, fuzzy membership functions, and biodata for beats within a given group consistent with the inventive concepts. [Figure 9A-3] 1A-1C provide a representation of sequential data acquisition, cluster samples, fuzzy membership functions, and biodata for beats within a given group consistent with the inventive concepts. [Figure 9A-4] 1A-1C provide a representation of sequential data acquisition, cluster samples, fuzzy membership functions, and biodata for beats within a given group consistent with the inventive concepts. [Figure 9B]1A-1C provide a representation of sequential data acquisition, cluster samples, fuzzy membership functions, and biodata for beats within a given group consistent with the inventive concepts. [Figure 9C] 1A-1C provide a representation of sequential data acquisition, cluster samples, fuzzy membership functions, and biodata for beats within a given group consistent with the inventive concepts. [Figure 9D] 1A-1C provide a representation of sequential data acquisition, cluster samples, fuzzy membership functions, and biodata for beats within a given group consistent with the inventive concepts. DETAILED DESCRIPTION OF THE INVENTION

[0065] Reference will now be made in detail to the present embodiments of the technology, examples of which are illustrated in the accompanying drawings. Like reference numerals may be used to refer to like components. However, the description is not intended to limit the disclosure to the particular embodiments, which should be construed to include various modifications, equivalents, and / or alternatives to the embodiments described herein.

[0066] It will be understood that the words "comprising" (and any form of comprising, such as "comprise" and "comprises"), "having" (and any form of having, such as "have" and "has"), "including" (and any form of including, such as "includes" and "include"), or "containing" (and any form of containing, such as "contains" and "contain"), as used herein, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups.

[0067] While terms such as first, second, and third may be used herein to describe various limitations, elements, components, regions, layers, and / or sections, it will be further understood that these limitations, elements, components, regions, layers, and / or sections are not to be limited by these terms. These terms are used only to distinguish one limitation, element, component, region, layer, or section from another limitation, element, component, region, layer, or section. Thus, a first limitation, element, component, region, layer, or section discussed below could be referred to as a second limitation, element, component, region, layer, or section without departing from the teachings of the present application.

[0068] It will be further understood that when an element is referred to as being "on," "attached," "connected," or "coupled to" another element, it can be directly on or above, or connected or coupled to the other element, or there can be one or more intervening elements. In contrast, when an element is referred to as being "directly on," "directly attached," "directly connected," or "directly coupled to" another element, there are no intervening elements present. Other words used to describe relationships between elements should be interpreted in a similar manner (e.g., "between" vs. "directly between," "adjacent to" vs. "directly adjacent to," etc.).

[0069] It will be further understood that when a first element is referred to as being "in," "on," and / or "within" a second element, the first element may be positioned within the interior space of the second element, within a portion of the second element (e.g., within a wall of the second element); positioned on an exterior surface and / or interior surface of the second element; and positioned in one or more combinations thereof.

[0070] As used herein, the term "proximate," when used to describe the proximity of a first component or location to a second component or location, should be interpreted to include one or more locations near the second component or location, as well as locations within, on, and / or within the second component or location. For example, a component positioned proximate to an anatomical location (e.g., a target tissue location) is intended to include a component positioned near the anatomical location, as well as a component positioned within, on, and / or within the anatomical location.

[0071] Spatially relative terms such as "below," "lower," "lower side," "above," and "upper" may be used to describe the relationship of an element and / or feature to another element and / or feature, for example, as illustrated in the figures. It will be further understood that the spatially relative terms are intended to encompass different orientations of the device in use and / or operation in addition to the orientation shown in the figures. For example, if a device in the figures were turned over, elements described as being "below" and / or "below" other elements or features would then be oriented "above" the other elements or features. The device may be otherwise oriented (e.g., rotated 90 degrees or to another orientation), and the spatially relative descriptors used herein may be interpreted accordingly.

[0072] Terms such as "reduce," "reducing," and "reduction," as used herein, are intended to include a reduction in amount (including a reduction to zero). Reducing the likelihood of occurrence is intended to include prevention of occurrence. Correspondingly, the terms "prevent," "preventing," and "prevention" are intended to include the acts of "reduce," "reducing," and "reduction," respectively.

[0073] The term "and / or," as used herein, should be construed as a specific disclosure of each of the two specified features or components, regardless of the presence or absence of the other. For example, "A and / or B" should be construed as a specific disclosure of (i) A, (ii) B, and (iii) each of A and B, as if each were set forth individually herein.

[0074] The term "one or more," as used herein, can mean one, two, three, four, five, six, seven, eight, nine, ten, or more (up to any number).

[0075] The terms "and combinations thereof" and "and combinations thereof" may be used herein after a list of items that are included singly or collectively, respectively. For example, components, processes, and / or other items selected from the group consisting of A; B; C; and combinations thereof, is intended to include a set of one or more components that includes one, two, three, or more of item A; one, two, three, or more of item B; and / or one, two, three, or more of item C.

[0076] In this specification, unless expressly stated otherwise, "and" can mean "or" and "or" can mean "and." For example, if a feature is described as having A, B, or C, the feature can have A, B, and C, or any combination of A, B, and C. Similarly, if a feature is described as having A, B, and C, the feature can have only one or two of A, B, or C.

[0077] As used herein, when a quantifiable parameter is described as having a value "between" a first value X and a second value Y, it is intended to include parameters having values ​​at least X, less than or equal to Y, and / or at least X and less than or equal to Y. For example, a length between 1 and 10 is intended to include a length of at least 1 (including values ​​greater than 10), less than 10 (including values ​​less than 1), and / or greater than 1 and less than 10.

[0078] As used in this disclosure, the phrase "configured to" may be used interchangeably with, for example, "suitable for," "capable of," "designed to," "adapted to," "made to," and "capable of," depending on the context. The phrase "configured to" does not mean only "specifically designed" in hardware. Alternatively, in some contexts, the phrase "device configured to" may mean that the device is "capable of" operating with another device or component.

[0079] As used herein, the term "threshold" refers to a maximum level, a minimum level, and / or a range of values ​​that correlate to a desired or undesirable state. In some embodiments, a system parameter is maintained above a minimum threshold, below a maximum threshold, within a threshold range of values, and / or outside a threshold range of values, e.g., to cause a desired effect (e.g., effective treatment) and / or prevent or otherwise reduce (hereinafter "prevent") an undesired event (e.g., device and / or clinical adverse event). In some embodiments, a system parameter is maintained above a first threshold (e.g., above a first temperature threshold to cause a desired therapeutic effect on tissue) and below a second threshold (e.g., below a second temperature threshold to prevent undesired tissue damage). In some embodiments, the thresholds are determined to include safety margins, accounting for, for example, patient variability, system variability, tolerances, etc. As used herein, "exceeding a threshold" refers to a parameter going above a maximum threshold, below a minimum threshold, within a threshold range, and / or outside a threshold range.

[0080] The term "diameter," when used herein to describe a non-circular geometry, should be interpreted as the diameter of an imaginary circle that approximates the geometry being described. For example, when describing a cross-section, such as a cross-section of a component, the term "diameter" shall be interpreted to represent the diameter of an imaginary circle having the same cross-sectional area as the cross-section of the component being described.

[0081] The terms "major axis" and "minor axis" of a component, as used herein, are the length and diameter, respectively, of an imaginary cylinder of smallest volume that can completely enclose the component.

[0082] As used herein, the term "functional element" should be interpreted to include one or more elements constructed and arranged to perform a function. A functional element can include a sensor and / or a transducer. In some embodiments, a functional element is configured to deliver energy and / or otherwise treat tissue (e.g., a functional element configured as a therapeutic element). Alternatively or additionally, a functional element (e.g., a functional element including a sensor) may be configured to record one or more parameters, such as patient physiological parameters; patient anatomical parameters (e.g., tissue geometry parameters); patient environmental parameters; and / or system parameters. In some embodiments, a sensor or other functional element is configured to perform a diagnostic function (e.g., to gather data used to perform a diagnosis). In some embodiments, a functional element is configured to perform a therapeutic function (e.g., to deliver therapeutic energy and / or a therapeutic agent). In some embodiments, the functional element includes one or more elements constructed and arranged to perform a function selected from the group consisting of: delivering energy; extracting energy (e.g., to cool a component); delivering a drug or other agent; manipulating a system component or patient tissue; recording or otherwise sensing a parameter, such as a patient physiological parameter or a system parameter; and combinations of one or more of these. The functional element may include a fluid and / or a fluid delivery system. The functional element may include a reservoir, such as an inflatable balloon or other fluid-maintaining reservoir. A "functional assembly" may include an assembly constructed and arranged to perform a function, such as a diagnostic function and / or a therapeutic function. The functional assembly may include an inflatable assembly.A functional assembly can include one or more functional elements.

[0083] The term "transducer," as used herein, should be interpreted to include any component or combination of components that receives energy or any input and produces an output. For example, a transducer can include an electrode that receives electrical energy and distributes the electrical energy to tissue (e.g., based on the size of the electrode). In some configurations, the transducer converts an electrical signal into an output, such as light (e.g., a transducer including a light-emitting diode or a light bulb), sound (e.g., a transducer including a piezoelectric crystal configured to deliver ultrasound energy), pressure, thermal energy, cryogenic energy, chemical energy; mechanical energy (e.g., a transducer including a motor or solenoid), magnetic energy, and / or a different electrical signal (e.g., Bluetooth or other wireless communication element). Alternatively or additionally, a transducer can convert a physical quantity (e.g., a change in a physical quantity) into an electrical signal. The transducer may include any component that delivers energy and / or agents to tissue, such as transducers configured to deliver electrical energy to tissue (e.g., a transducer including one or more electrodes); deliver optical energy to tissue (e.g., a transducer including a laser, a light emitting diode, and / or an optical component such as a lens or prism); deliver mechanical energy to tissue (e.g., a transducer including a tissue manipulation element); deliver acoustic energy to tissue (e.g., a transducer including a piezoelectric crystal); chemical energy; electromagnetic energy; magnetic energy; and combinations of one or more of these.

[0084] As used herein, the term "fluid" can refer to a liquid, gas, gel, or any flowable material, such as a material that can be propelled through a lumen and / or opening.

[0085] It will be appreciated that certain features of the invention, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable subcombination. For example, it will be appreciated that all features set forth in any claim (whether independent or dependent) may be combined in any given manner.

[0086] It should be understood that at least some of the figures and descriptions of the invention have been simplified to focus on elements relevant to a clear understanding of the invention, while for purposes of clarity excluding other elements that may also form part of the invention as those skilled in the art will recognize. However, because such elements are well known in the art, and because they do not necessarily facilitate a better understanding of the invention, descriptions of such elements are not provided herein.

[0087] The terms defined in this disclosure are used only to describe particular embodiments of the present disclosure and are not intended to limit the scope of the present disclosure. Terms provided in the singular are intended to include the plural as well, unless the context clearly indicates otherwise. All terms used herein (including technical or scientific terms) have the same meaning as commonly understood by those skilled in the art, unless otherwise defined herein. Terms defined in commonly used dictionaries should be interpreted as having the same or similar meaning as in the context of the relevant art, and should not be understood as having an ideal or exaggerated meaning unless explicitly defined as such herein. In some cases, terms defined in this disclosure should not be interpreted to exclude embodiments of the present disclosure.

[0088] Provided herein are systems, methods, and devices for modeling a patient's cardiac activity. The system includes at least one diagnostic catheter for insertion into the patient's heart, the catheter including one or more recording elements (e.g., electrodes and / or ultrasound transducers) configured to record data (e.g., patient electrical data, patient anatomical data, and / or device location data) over multiple cardiac cycles. The system includes a processing unit including a clustering routine configured to receive recorded patient data; segment the recorded patient data by cardiac cycle to create segmented patient data including the segments; group the segments based on one or more characteristics of the segments to create segmented data groups; and combine the segmented patient data within each segmented data group to create one or more composite records. The system generates one or more models of the patient's cardiac electrical activity based on the one or more composite records. In some embodiments, multiple models of the cardiac electrical activity are created.

[0089] 1, there is shown a schematic diagram of a system configured to perform a cardiac mapping procedure consistent with the concepts of the present invention. System 10 includes various components and subsystems, etc., configured to cooperatively record and analyze physiological information, diagnose physiological conditions and / or diseases, and / or treat physiological conditions and / or diseases. System 10 may include a console 5000 and one or more catheters 1000 for insertion into a patient.

[0090] The catheter 1000 may include one or more mapping catheters, mapping catheter 1100. The mapping catheter 1100 may include one or more arrays of elements, basket array 1150. The basket array 1150 may include a compressible and / or expandable structure containing these elements (e.g., electrodes and / or ultrasound transducers). The basket array 1150 may include one or more splines; a linear array of elements; a circular or spiral array of elements; a grid of elements; and / or a multi-arm array of elements. The basket array 1150 may include multiple splines. One or more of the multiple splines may include one or more functional elements (e.g., electrodes) configured to sense and / or record voltage potentials (also referred to herein as "potentials") related to cardiac activity and / or functional elements (e.g., electrodes and / or ultrasound transducers) used for localization. The array 1150 can include between three and eight splines (e.g., six splines, etc.), each including multiple sensing, recording, and / or localizing functional elements. The functional elements of the basket 1150 can include electrodes 1151 and / or ultrasound transducers (USTs) 1153. The electrodes 1151 can be used for mapping, for localization, and / or, in some embodiments, for delivering ablation energy. The electrodes 1151 can be coupled to the console 5000, which can be configured to drive the electrodes 1151 and to receive and record data from the electrodes 1151. The ultrasound transducers 1153 can include at least one ultrasound emitter and an ultrasound sensor. The ultrasound transducers 1153 can be configured for localization of the basket array 1150 and / or for localization of other catheters and / or structures within the heart H.The ultrasound transducer 1153 may also be configured to gather data useful for generating and / or updating images of the patient's heart H and / or other anatomical structures. The catheter 1100 includes a catheter shaft 1120. The shaft 1120 may be configured to slide within a lumen 1325 of a shaft 1320 of a transseptal sheath or other introducer device (sheath 1300) used for insertion and translation inside the patient P, for example, to deliver the basket array 1150 to the heart H.

[0091] A handle 1110, used to manipulate the catheter within the patient P, is positioned at the proximal end of the catheter shaft 1120. A basket array 1150 extends from the distal end of the catheter shaft 1120. In various embodiments, the array 1150 can be, or at least include, an expandable / collapsible basket array coupled to the distal end of the catheter shaft 1120. An actuator (not shown, but typically a flexible mechanical linkage) can be slidable within the shaft 1120, which can be coupled to and / or engaged with the distal end of the array 1150. In various embodiments, the actuator extends distally to collapse the array 1150, for example, by straightening splines 1157, and retracts proximally to expand the array 1150 by bending the splines 1157 outward. The catheter 1100 may also include at least one other functional element 1190 (eg, an electrode, etc.) and / or other physiological sensor positioned on the catheter shaft 1120.

[0092] The catheter 1000 may include one or more diagnostic catheters (diagnostic catheter 1200). The catheter 1200 may include a shaft 1220 for insertion into the patient P and delivery to the heart H. For example, in some embodiments, the catheter 1200 may be a coronary sinus mapping catheter, which is structured and arranged for positioning within the coronary sinus of the heart H. The catheter 1200 may include an electrode array 1250 including one or more functional elements in the form of electrodes 1251 (e.g., electrodes used in cardiac activity mapping and / or localization, etc.). The catheter 1200 may also include at least one other functional element 1290 (e.g., electrodes, etc.) and / or other physiological sensors, which may be positioned on the catheter shaft 1220.

[0093] The catheter 1000 can include one or more treatment catheters (treatment catheter 1500). The treatment catheter 1500 can be, for example, an ablation catheter, such as a radiofrequency (RF) ablation catheter, an alternative optical energy delivery catheter, a cryoablation catheter, an electric field generation catheter (e.g., a pulsed electric field catheter), and / or an ultrasound or other acoustic energy catheter. The catheter 1500 can include a shaft 1520 with a handle 1510 at its proximal end. A treatment array 1550 including at least one functional element 1551 is disposed at the distal end of the shaft 1520. By way of example, the functional element of the treatment array 1550 can include one or more types of energy delivery elements 1551 (e.g., one or more RF delivery electrodes), one or more optical components for delivering optical energy, cryogenic energy, and / or one or more acoustic transducers for delivering ultrasonic energy. In some embodiments, electrodes 1551a-d can be used for ablation therapy, while in other embodiments, one electrode (1551a) can be used for ablation, with one or more of the remaining electrodes 1551b-c still present for localization, for example, if array 1550 includes a cryoablation tip (1551d). In various embodiments, functional element 1551a can be a treatment element (e.g., RF, CRYO, etc.), and functional elements 1551b, c, d can be electrodes for localizing treatment element 1551a. In another embodiment, array 1550 includes four electrodes 1551a-d for RF ablation. Catheter 1500 can also include at least one other functional element 1590 (e.g., an electrode, a coil, a physiological sensor, etc.) positioned on catheter shaft 1520.

[0094] System 10 may include one or more patches 550. Patch 500 may be an electrode, a magnetic element, and / or a combination thereof. Patch 550 may be external to the patient; for example, patch 550 may be configured to be applied to the torso of patient P. Patch 550 and catheter 1000, and / or components thereof, may be configured to provide data and information to console 5000 to perform localization of one or more devices of system 10 in and / or on patient P. Localization may be performed as described in applicant's co-pending U.S. patent application Ser. No. 15 / 569,457, filed Oct. 26, 2017, entitled "LOCALIZATION SYSTEM AND METHOD USEFUL IN THE ACQUISITION AND ANALYSIS OF CARDIAC INFORMATION," the contents of which are incorporated herein by reference in their entirety for all purposes.

[0095] The patches 550 may be coupled to the console 5000 by one or more cables and / or cable assemblies 501 and / or other wired or wireless data transfer elements. The patches 550 may be skin-contact patches including adhesive for removably applying to the torso of the patient P, and each patch may include one or more types of functional elements. The patches 550 may include at least one impedance functional element (e.g., an electrode, etc.) configured to measure impedance at the patch and / or provide a drive signal for an impedance-based localization modality. The impedance measurements may be used by the console 5000 to perform impedance-based localization.

[0096] In some embodiments, one or more of the patches 550 can be a combination (or "combo") patch that includes two or more different types of functional elements. By way of example, a combo patch 550 can include at least one magnetic element and at least one impedance element, which can optionally include another type of functional element 599. For example, the functional element can be a 12-lead EKG / ECG (electrocardiogram) element that is typically positioned on a patient during a clinical procedure. Alternatively or additionally, the system 10 can include one or more EKG / ECG electrodes (e.g., electrode patches) 560.

[0097] In various embodiments, any one or more of the patches 550 can include at least one other functional element 599. In addition to EKG / ECG-based functional elements, the functional elements 599 can, by way of example, be or at least include general-purpose sensors, transducers, and / or other functional elements, such as accelerometers, sweat detectors, physiological sensors, and / or imaging markers (e.g., radiopaque markers, MR markers). As another example, in some embodiments, the functional elements 599 include microwave functional elements, ultrasound functional elements, or a combination thereof.

[0098] The console 5000 may include a patient interface module 5010, a biopotential module 5020, a localization module 5030, and an anatomical structure module 5040. The console 5000 may further include a processor 5050 and algorithms 5055. The patient interface module 5010 may be configured to operably attach one or more patient devices (e.g., one or more catheters 1000 and / or patches 550) to one or more components of the console 5000. The patient interface module 5010 may provide patient electrical shielding such that the patient P is protected from any potentially harmful voltage and / or current sources within the console 5000.

[0099] The biopotential module 5020, the localization module 5030, and the anatomical structure module 5040 may each be configured to receive data from a number of different external functional elements (e.g., functional elements of one or more catheters 1000), process the received data, and generate an output (e.g., generated information shown on one or more displays of the system 10 (the displays, not shown, are typically one or more touchscreens and / or other video displays)) based at least in part on the processed data. The biopotential module 5020 may generate one or more outputs related to the patient's electrical activity, e.g., dipole density information, surface charge information, and / or voltage information related to the patient's cardiac activity. The biopotential module is disclosed in Applicant's co-pending U.S. patent application Ser. No. 16 / 014,370, filed Jan. 21, 2018, entitled "METHOD AND DEVICE FOR DETERMINING AND PRESENTING SURFACE CHARGE AND DIPOLE DENSITIES ON CARDIAC WALLS"; U.S. patent application Ser. No. 15 / 882,097, filed Jan. 29, 2018, entitled "DEVICE AND METHOD FOR THE GEOMETRIC DETERMINATION OF ELECTRICAL DIPOLE DENSITIES ON THE CARDIAC WALL"; U.S. patent application Ser. No. 29 / 681,827, filed Feb. 28, 2019, entitled "SET OF TRANSDUCER-ELECTRODE PAIRS FOR A CATHETER"; and U.S. patent application Ser. No. 29 / 681,827, filed Oct. 31, 2018, entitled "CARDIAC MAPPING SYSTEM WITH EFFICIENCY The present invention may be of a similar construction and arrangement with similar components as described in U.S. patent application Ser. No. 16 / 097,959, entitled "A HIGH-SPEED COMPONENT ALGORITHM," the contents of each of which are incorporated herein by reference in their entirety for all purposes.The localization module 5030 may generate one or more outputs related to the position of one or more components of the system 10 relative to the patient P (e.g., relative to a coordinate system established by the localization module 5030, etc.). The localization module 5030 may be of a similar construction and arrangement to like components described in applicant's co-pending U.S. patent application Ser. No. 15 / 569,457, entitled "LOCALIZATION SYSTEM AND METHOD USEFUL IN THE ACQUISITION AND ANALYSIS OF CARDIAC INFORMATION," filed October 26, 2017, the contents of which are incorporated herein by reference in their entirety for all purposes. The anatomy module 5040 may generate one or more outputs related to the anatomy of the patient P, for example, the size, shape, and / or structure of at least a portion (e.g., a heart chamber) of the patient P's heart H. The anatomical structure module 5040 may be of a similar construction and arrangement to similar components described in the applicant's co-pending: U.S. patent application Ser. No. 29 / 681,827, entitled "SET OF TRANSDUCER-ELECTRODE PAIRS FOR A CATHETER," filed February 28, 2019; and U.S. patent application Ser. No. 15 / 569,185, entitled "ULTRASOUND SEQUENCING SYSTEM AND METHOD," filed October 25, 2017, the contents of each of which are incorporated herein by reference in their entirety for all purposes.

[0100] In some embodiments, the biopotential module 5020, the localization module 5030, and / or the anatomy module 5040 generate one or more drive signals, such as, for example, one or more drive signals configured to drive one or more external functional elements of the system 10 (e.g., the localization electrodes 550 and / or the ultrasound transducers 1153). In some embodiments, the processor 5050 is configured to function cooperatively with the biopotential module 5020, the localization module 5030, and / or the anatomy module 5040 (e.g., receive and analyze data, generate one or more outputs, etc.). In some embodiments, outputs generated by the biopotential module 5020, the localization module 5030, and / or the anatomy module 5040 are received as data inputs to the processor 5050 and / or to other modules of the console 5000. In some embodiments, algorithm 5055 contains instructions configured to enable processor 5050 to perform one or more operations. For example, algorithm 5055 may include instructions for performing method 100, described below with reference to FIG.

[0101] Referring now to Figure 2, there is shown a flowchart of a method for recording and modeling a patient's electrical activity consistent with the inventive concepts. The method 100 of Figure 2 is described using various components of the system 10 of Figure 1, as described above.

[0102] In step 110, data is recorded (e.g., by console 5000) from recording elements (e.g., electrodes and / or ultrasound transducers) of one or more catheters 1000 inserted into patient P. Data may be recorded over multiple cardiac cycles. In some embodiments, data is recorded when the recording elements are in contact with cardiac tissue, not in contact with cardiac tissue, or a combination of the two. In some embodiments, only data recorded from elements in contact with cardiac tissue is processed. In some embodiments, only data recorded from elements not in contact with cardiac tissue is processed. In some embodiments, data recorded by recording elements in contact with cardiac tissue is processed together with data recorded by recording elements not in contact with tissue, for example, in a combined (e.g., integrated) solution. In some embodiments, only data recorded by recording elements in contact with cardiac tissue is processed (e.g., independently). In some embodiments, data recorded from elements in contact with cardiac tissue is processed separately from data recorded from elements not in contact with cardiac tissue. In some embodiments, cardiac information resulting from separately processed data based on the state of contact of the recording elements can be combined and further processed to refine the cardiac information. In some embodiments, cardiac information resulting from separately processed data based on the state of contact of the recording elements can be quantitatively compared, and the results of the quantitative comparison can be displayed. In some embodiments, the results of the quantitative comparison of cardiac information at various locations on the cardiac anatomy can be displayed in a two-dimensional or three-dimensional representation (e.g., an image, etc.) of the cardiac anatomy.

[0103] In some embodiments, data is recorded from a catheter (such as, for example, the mapping catheter 1100) that includes an array of recording elements. As described above, the array of recording elements may be positioned on a basket and / or other radially expandable structure. In some embodiments, the array of recording elements is positioned linearly along the shaft, circularly or spirally along the shaft, in a grid configuration, and / or in a multi-arm catheter, where each arm is attached to the shaft at one end (e.g., similar to the geometry of a household mop). Data may also be recorded from a reference catheter (such as, for example, the coronary sinus catheter 1200). For example, a distal portion of the catheter 1200 (e.g., at least the electrode array 1250) may be positioned in the coronary sinus of the heart H of the patient P. The electrode array 1150 (e.g., at least the basket array of electrodes 1151) of the catheter 1100 is positioned in a chamber of the heart H (e.g., in the left atrium of the heart H). Data (e.g., biopotential data and / or localization data) is recorded from both catheters 1100 and 1200. As data is recorded, array 1150 may be steered within the heart chamber such that electrodes 1151 are positioned to collect data from (e.g., "cover") a larger volume of the heart chamber compared to the volume that would be covered if array 1150 were not steered during recording. In some embodiments, array 1150 is steered such that at least 25%, at least 40%, and / or at least 60% of the volume of the heart chamber is covered during recording (e.g., at least 70% or at least 85%, etc.). In some embodiments, array 1150 is steered slowly (e.g., steadily, limiting rapid movement of array 1150). In some embodiments, array 1150 is steered in a pattern.The pattern can be a robotically controlled pattern and / or a defined pattern, such as a pattern known by (e.g., taught to) an operator of system 10 (e.g., one or more clinicians or other operators performing or assisting in performing a diagnostic and / or therapeutic procedure on a patient using system 10). Alternatively or additionally, the pattern can be an operator-determined pattern, such as a pattern determined by visual feedback provided to the operator by system 10 (e.g., maneuvers performed during recording and / or other patterns determined at the time of the procedure). The visual feedback can provide operator information in a closed-loop manner, such as to assist the operator in completing one or more tasks and / or desired goals of the procedure. For example, the visual feedback can prompt the operator to perform actions such as expanding, extending, and / or modifying the operator-determined pattern to include regions with scarce data, regions with insufficient data, and / or regions with poorly spatially distributed data; improving the amount and / or quality of data in a particular region of interest; replacing data within a region (e.g., re-recording data within a region); achieving full "coverage" with high-quality data across the complete ventricular cavity surface and / or throughout the entire volume being assessed; and combinations of one or more of these. The visual feedback can indicate the amount and / or quality of data in time and / or across space.For example, the visual feedback may indicate one or more of the following conditions: sufficient data (points, locations, areas, and / or volumes) within a region; insufficient data within a region; coverage across the region (whether data is present or absent); data density within a region; spatial distribution of data without or with "artifacts" (e.g., measurement anomalies and / or errors); consistency or stability of data over time (within a particular region or regardless of location); and combinations of one or more of these.

[0104] The visual feedback provided by system 10 may include one or more visual elements (e.g., points, lines, arrows, meshes, charts, meters, plots, etc.) The visual elements may possess one or more variable characteristics, such as characteristics selected from the group consisting of size, thickness, color, hue, texture, gradient, translucency, brightness, and combinations thereof, where variations in the characteristics are used to provide additional feedback.

[0105] The steering pattern can be a repeating and / or non-repeating set of sub-patterns (herein "patterns"). These patterns in which the array 1150 is steered can be configured to cover one or more portions of the heart chamber multiple times and / or for a predetermined percentage of the recording time (e.g., at least 15% or at least 20% of the recording time, etc.). In some embodiments, data is recorded continuously for a time period of at least 60 seconds (e.g., at least 90 seconds, etc.) or at least 120 seconds.

[0106] In step 120, the recorded data is analyzed, such as by one or more algorithms 5055 and / or processor 5050 of console 5000. Cardiac cycles are identified and correlated to the recorded data over a time period of the recorded data. In some embodiments, system 10 analyzes at least one data signal recorded from a reference catheter (such as, for example, coronary sinus catheter 1200) and / or from one or more ECG electrodes 560, e.g., to identify (e.g., and correlate) cardiac cycles. In some embodiments, system 10 analyzes data recorded from two, three, or more “channels” (e.g., data recorded from two, three, or more individual electrodes). In some embodiments, algorithms of system 10 analyze multiple channels of data and use a stable timing reference to determine the optimal channel (e.g., optimal electrode) for cardiac cycle identification. In some embodiments, the optimal channel may be selected based on the electrical signals recorded by that channel, such as by the consistency of detected timing of the channel's electrical signal features and / or by the amplitude (e.g., more easily measured amplitude) of the electrical signals in that channel. In some embodiments, the optimal channel is selected based on an operator's visual inspection of a graphical display of one or more channels of recorded data. In some embodiments, system 10 presents the operator with a graphical display with a subset of the total number of recorded channels, which the operator can visually inspect to select the optimal channel. The subset may be selected based on an algorithmic analysis of the recorded data performed by system 10, such as eliminating one or more suboptimal channels. In some embodiments, system 10 may filter one or more of the recorded electrical signals, such as with a V-wave filter (also known as a QRS filter).For example, system 10 may include filters based on one or more templates (e.g., templates with varying window sizes for one or more QRS signals) for V wave filtering and / or individual QRS signal filtering. In some embodiments, a V wave filter may be used that includes V wave blanking (also known as V wave subtraction, V wave removal, or V wave rejection), such as that described in applicant's co-pending application, U.S. Patent Application No. 16 / 097,959, entitled "CARDIAC MAPPING SYSTEM WITH EFFICIENCY ALGORITHM," filed October 31, 2018, the contents of which are incorporated herein by reference in their entirety for all purposes. Alternatively or additionally, system 10 can selectively process one or more of the recorded electrical signals, such as when system 10 identifies known or expected electrical events and processes intervals of the signal corresponding to the events using specific and different sets of steps (e.g., removing, subtracting, reducing, keeping, amplifying, or filtering the specific events). In some embodiments, system 10 identifies T waves (corresponding to ventricular repolarization) in the signals and excludes T-wave morphology or excludes T-wave intervals when processing cardiac information from the atria. In some embodiments, system 10 identifies P waves (corresponding to electrical activity in the atria) in the signals and excludes intervals of the signals containing P waves when processing cardiac information related to the ventricles.

[0107] In some embodiments, system 10 includes a “detection rejection” algorithm (e.g., algorithm 5055 described above with reference to FIG. 1 ). The detection rejection algorithm may be configured to identify signal features known to be undesirable (e.g., features that degrade signal quality and / or the accuracy of subsequent method steps). In some embodiments, the detection rejection algorithm is further configured to modify and / or reject detection of cardiac cycles for durations proximate to signal features identified as undesirable (e.g., durations of 1 second or less, e.g., 20 ms or 100 ms, etc.). In some embodiments, these cardiac cycles are excluded from further calculations. In some embodiments, cardiac cycles overlapped and / or interfered with by undesirable signal features may be corrected and / or modified, for example, for use in subsequent method steps. Examples of undesirable signal features include, but are not limited to, abnormal cardiac rhythms and / or rhythm components (e.g., ventricular depolarization or repolarization), respiratory artifacts, cardiac tissue contact artifacts, and / or any electrical activity from tissue contact.

[0108] In step 130, system 10 analyzes the lengths of the identified cardiac cycles identified in step 120. If the cycle lengths are determined to be sufficiently consistent (also referred to herein as "regular") (e.g., within a threshold length deviation between the identified cycles), method 100 continues to step 140. If the cycle lengths are determined to be inconsistent (also referred to herein as "irregular"), for example, if there is too much change in length from one cycle to the next, system 10 determines that the cardiac rhythm is irregular, and method 100 exits in step 135. In some embodiments, if the cycle length pattern is determined to repeat on a larger scale (e.g., a pattern of cycle lengths that vary within a pattern (e.g., a first cycle length followed by a second, different cycle length)), the change from one cycle to the next is not determined to be irregular. In some embodiments, all cardiac cycles identified in step 120, regardless of the regularity of the cycle lengths, In some embodiments, different period lengths are treated as subgroups if they cluster into regular subgroups around distinct values. In other words, multiple period lengths may be simultaneously present in the recorded data (e.g., interspersed or interleaved with each other) and may be identified as distinct groups based on the separation of period length values. In some embodiments, the number of period length groups may be determined algorithmically by a clustering technique. In some embodiments, period length groups may be added, removed, and / or merged by an operator. In some embodiments, lower and upper thresholds for each group of period lengths may be established and adjusted (e.g., algorithmically or manually by an operator) for any group.

[0109] In step 140, the recorded data is segmented by the cardiac cycle determined in step 120. In some embodiments, the recorded data that is segmented includes data recorded from one or more of the coronary sinus catheter 1200 (e.g., the coronary sinus catheter or other reference catheter); the basket catheter 1100; and / or the surface ECG electrodes 560; as well as any two or all three of these. In some embodiments, the recorded data is segmented into segments having a duration of at least a period of time equal to the maximum difference between activation times recorded by two or more (e.g., all) electrodes of the reference catheter (e.g., the data is segmented using “narrowband clustering”).

[0110] In optional steps 150 and 160, the recorded data can be filtered to remove unwanted data so that it is not incorporated into the electrical activity model of method 100. In step 150, the segmented data can be filtered by cycle length, e.g., so that segments with cycle length changes exceeding a threshold are removed or processed as a separate subgroup. In some embodiments, the threshold is determined based on the average cycle length determined in step 120, as described above. In step 160, the recorded data can be analyzed, e.g., by an algorithm and / or processor of system 10 (e.g., algorithm 5055 and / or processor 5050 of console 5000, respectively), to filter out inconsistent, erroneous, and / or otherwise unwanted data. In some embodiments, the unwanted data is caused by recording from a malfunctioning electrode (e.g., malfunctioning electrode 1151, 1251, etc.). If a malfunctioning electrode is identified, the data recorded from the electrode may be selectively removed from the recorded data, either completely or, for example, only after the moment the malfunction occurred (e.g., if the electrode "dies" during recording). In some embodiments, step 160 may include a filtering and / or outlier detection process that processes the data (e.g., combines and / or removes data) based on signal similarity.

[0111] In step 170, the recorded data segments are time-aligned to generate a composite recording (a composite of one or more time-aligned segments), e.g., over the duration of a single cardiac cycle. In some embodiments, "pattern clustering" is performed, where segments are grouped based on one or more identified patterns (e.g., patterns of electrical activity) and / or characteristics. In some embodiments, segments are differentiated and / or grouped based on signal characteristics, such as cardiac cycle length, signal morphology (e.g., comparison based on cross-correlation and / or wavelet decomposition), amplitude, frequency, frequency content, and / or wavelet analysis. In some embodiments, clustering algorithms are used, such as algorithms including cluster model-based algorithms. For example, the cluster model-based algorithm may include one or more of the following: connectivity model-based algorithms (such as, for example, hierarchical clustering algorithms), where the model is based on distance connectivity; centroid model-based algorithms (such as, for example, k-means clustering algorithms), where each cluster can be represented by a single mean vector; split and merge based algorithms, where data is first split into many clusters and then merged based on affinity; density model-based algorithms, which define clusters as being connected to dense regions in the data space; distribution model-based algorithms (such as, for example, Gaussian mixture model clustering algorithms), where clusters are modeled using statistical distributions (such as, for example, multivariate normal distributions); graph-based model algorithms; neural model-based algorithms (such as, for example, self-organizing maps and / or other unsupervised neural networks), where artificial neural networks and / or other nonlinear statistical data modeling tools may be used to model complex relationships and patterns in the data; and combinations of one or more of these.In some embodiments, data projection and / or pre-processing approaches are applied, such as, for example, dimensionality reduction, principal component analysis, and / or multidimensional scaling.

[0112] In some embodiments, segments are differentiated (e.g., clustered) from a reference signal based on “template matching,” such as matching to a templated (e.g., previously categorized) set of signal characteristics across one or more channels (e.g., from individual electrodes). In some embodiments, segments are differentiated based on the consistency of relative time intervals measured by and / or between each of a plurality of electrodes. For example, detected reference times T1 through T10 may be detected on electrodes E1-E10, respectively. Segments may be grouped by their degree of consistency and / or similarity to a set of times T1′-T10′ (in the form of a template). Some cardiac rhythms may have two or more sets of times T1-T10 recurringly. In some embodiments, reference times are detected from difference signals from two or more electrodes (e.g., difference signals between neighboring electrodes obtained by subtracting one from the other).

[0113] In some embodiments, the segments are transformed into a different domain, such as Wavelet or Fourier, and / or projected into a lower-dimensional space using linear / nonlinear dimensionality reduction, such as T-distributed Stochastic Neighbor Embedding or Locally Linear Embedding. The segments or transformed segments are clustered using any and / or combinations of the clustering methods described above. Clustering can be performed without specifying the number of groups in advance or with a known number of groups. Matching segments to groups can be achieved by satisfying required criteria (e.g., correlation coefficient higher than a threshold, distance metric such as vector norm, cosine, KL-divergence, etc. lower than a threshold, etc.) or by requiring statistical metrics (e.g., mean, median, standard deviation, etc.) to satisfy conditions (lower than a threshold for some distance metrics and higher than a threshold for others).

[0114] In some embodiments, segment differentiation is further refined by grouping segments based on the degree of correlation (also referred to herein as "matching") to unique signal morphologies measured by each of multiple electrodes (e.g., 10) of a reference catheter. For example, signal morphologies S1 through S10 are identified from signals E1 through E10, respectively, to be templates. Segments with signal morphologies S1' through S10' from signals E1 through E10 at different times may be compared to the templates, e.g., by using mathematical and / or statistical comparisons, such as cross-correlation, to determine the degree of correlation between S1 and S1', between S2 and S2', and so on. A set of correlation values ​​may be used to determine whether a segment matches a template. Template matching may include methods selected from the group consisting of: requiring a set of criteria to be met, such as requiring the correlation values ​​for all signals to exceed a threshold (e.g., X-corr greater than 0.8, or X-corr greater than 0.9); requiring the correlation values ​​for a subset of signals to exceed a threshold (e.g., greater than 60% of the signals, or greater than 80% of the signals); requiring a statistical metric (e.g., mean, median, standard deviation, etc.) to exceed a threshold; and combinations of one or more of these.

[0115] In some embodiments, a set of signal features from a single event (e.g., a single cardiac event such as a single heart beat) is used as a template, and all other segments are matched to the template. In some embodiments, the template is updated at fixed and / or variable time intervals. In some embodiments, when a segment is found not to match an existing template, that segment is used as a second template to form a second group. In these embodiments, when additional segments are found not to match any of the existing templates, they can be used to expand the set of templates to form additional groups. In this way, the set of templates can continue to expand throughout the segment collection process.

[0116] In some embodiments, one or more templates and their respective groups are fused and / or combined to form a combined group of all matching segments. Combining can be done manually by an operator and / or automatically by an algorithm based on similarities between the templates and / or common differences from all remaining templates. In some embodiments, the system visually displays unique groups (e.g., from any subset of catheters, any set of signals or recorded data from a group) to assist the operator in merging groups and / or determining a suitable group for further processing. In some embodiments, the visual display of the groups of segments includes a user interface that allows the operator to use a user input device to manually exclude a subset of signals within a group and / or select a subset of signals within a group to form a new unique group.

[0117] In some embodiments, an algorithm (e.g., algorithm 5055) of system 10 is configured to assess the degree of matching of a segment to a template based on a set of criteria. For example, the algorithm may compare the degree of matching against a threshold. In some embodiments, the criteria are dynamically adjusted to become more or less restrictive to successfully match a segment to a template. In some embodiments, when the criteria are adjusted, previously matched segments are reprocessed to regenerate unique templates and groups. In some embodiments, when the criteria are adjusted (e.g., if the criteria become more restrictive), only templates representing each group may be processed using the adjusted criteria to determine whether the groups should be combined. In some embodiments, when the criteria are adjusted (e.g., if the criteria become less restrictive, among other things), only segments within each group need be reprocessed against new unique templates that emerge from the group based on the adjusted criteria to determine whether the group should be subdivided. In some embodiments, two or more of the clustering, templating, matching methods described herein and / or any other methods of differentiating beats or rhythms are used in combination (e.g., sequentially or simultaneously).

[0118] After pattern clustering, two, three, or more composite records may be generated, each containing a unique pattern of activation (e.g., a pattern of activation across a cardiac cycle). Alternatively or additionally, after pattern clustering, the cluster with the largest percentage of segments may be chosen as the (single) composite record. In some embodiments, any clusters with a predetermined percentage of segments above a threshold may be identified for the generation of a composite record (e.g., one, two, or more clusters), e.g., when clusters below the threshold are discarded. In some embodiments, composite records may be generated based on data including one, two, or more cycle lengths. In some embodiments, for example, with narrowband clustering, the input segment width for the composite map to be generated is not defined. In these embodiments, data including two cycle lengths is used, and activation activity around segment boundaries can be determined (e.g., because there are multiple inversions in the data across two cycle lengths).

[0119] At step 180, cardiac electrical activity during the simulated cardiac cycle is modeled based on the composite recording. In some embodiments, cardiac electrical activity is modeled by indexing the cardiac electrical data, such as by indexing representative times and / or amplitudes with respect to one or more locations on the cardiac anatomy. In some embodiments, cardiac electrical data is modeled by interpolating and / or projecting cardiac electrical data from one set of locations to a separate set of locations. In some embodiments, cardiac electrical activity is calculated via measurements made using electrodes positioned in contact with tissue. In these embodiments, cardiac electrical activity may include voltage-based measurements. In some embodiments, cardiac electrical activity is calculated using forward modeling and / or inverse modeling (e.g., via non-contact recordings, which may be made with or without contact recordings). Alternatively or additionally, cardiac electrical activity may be calculated using an inverse solution. In some embodiments, cardiac electrical activity is analyzed using the methods and techniques described in applicant's co-pending applications: U.S. patent application Ser. No. 16 / 014,370, filed June 21, 2018, entitled "METHOD AND DEVICE FOR DETERMINING AND PRESENTING SURFACE CHARGE AND DIPOLE DENSITIES ON CARDIAC WALLS"; and U.S. patent application Ser. No. 16 / 242,810, filed January 8, 2019, entitled "EXPANDABLE CATHETER ASSEMBLY WITH FLEXIBLE PRINTED CIRCUIT BOARD (PCB) ELECTRICAL PATHWAYS," the contents of each of which are incorporated herein by reference in their entirety for all purposes, to produce, for example, dipole density and / or surface charge density data.In some embodiments, cardiac electrical activity may be calculated using an automatic timing annotation algorithm (e.g., an algorithm that identifies signal features, such as those corresponding to cardiac tissue depolarization, activation, and / or repolarization). In some embodiments, an analysis of cardiac electrical activity may be displayed to an operator, such as by an activation timing map produced on a display. In some embodiments, an analysis of cardiac electrical activity may be displayed as described in applicant's co-pending application: U.S. Patent Application No. 16 / 097,955, entitled "CARDIAC INFORMATION DYNAMIC DISPLAY SYSTEM AND METHOD," filed October 31, 2018, the contents of which are incorporated by reference in their entirety for all purposes, such as when activity is displayed as a dipole density map, a surface charge map, a voltage map, an activation timing map, an isochrone map, and / or a Laplacian amplitude map.

[0120] In some embodiments, steps 120-180 of method 100 are performed during a clinical procedure, for example, when calculating the model of cardiac electrical activity is performed in "real time" (e.g., such that the model is provided to the operator in near real time (e.g., within milliseconds, seconds, or minutes of the recorded data, etc.)). Alternatively or additionally, steps 120-180 may be performed once after completion of the clinical procedure (during which the biopotential data recorded in step 110 was recorded).

[0121] Referring now to FIG. 3A, a graph of recorded electrical activity consistent with the inventive concepts is shown. FIG. 3A illustrates signals (first signal ECG1, second signal CS1, and third signal CS2) recorded on three channels of system 10, where first signal ECG1 is recorded from ECG leads attached to console 5000, and second and third signals CS1 and CS2 are recorded from two electrodes 1251, respectively, of coronary sinus catheter 1200. FIG. 3A illustrates exemplary signals analyzed by system 10, such as in steps 120 and / or 140 of method 100 described above with reference to FIG. 2. In some embodiments, system 10 is configured to record two, three, or more ECG signals recorded from internal and / or external patient locations (e.g., one or more internal electrodes and / or one or more external electrodes). Additionally, system 10 may be configured to record multiple signals (e.g., from multiple electrodes of catheter 1200) (e.g., 10 or 12 signals from 10 or 12 electrodes 1251, etc.) from coronary sinus catheter 1200. Figure 3A shows an illustrative subset of these recorded signals.

[0122] In some embodiments, system 10 is configured to analyze signal ECG1 and identify V-, T-, and / or P-waves, such as with respect to V-, T-, and / or P-wave blanking (also known as subtraction or removal), as described above with reference to FIG. 2. For illustrative purposes, V-wave filtering is indicated by the shaded segment SB1.

[0123] In some embodiments, system 10 is configured to compare signals recorded from each electrode 1251 of coronary sinus catheter 1200, identify the channel with the best recorded data (e.g., data with the most clearly identifiable features), and select that channel as the reference channel. For example, as shown in FIG. 3A , signal CS1 includes a relatively low-amplitude recorded representation of cardiac cycle features, while CS2 includes a relatively high-amplitude recording of those features. As shown, activations ACT1 and ACT2 are more clearly identifiable on signal CS2, and as such, signal CS2 is selected as the reference signal. In some embodiments, system 10 is configured to compare signals recorded from each electrode of a multi-electrode catheter and / or multiple catheters positioned in one or more locations of the heart, for example, in the right and / or left atrium, in the right and / or left ventricle, in the pulmonary outflow tract, and / or in any of the circulatory (e.g., venous) structures of the heart. In some embodiments, system 10 is configured to compare signals recorded from one or more electrodes placed at one or more locations on the body surface (e.g., including any or all of ECG electrodes, impedance-driven patch electrodes, and / or any other auxiliary electrodes placed on the body to measure signals generated by the heart). In some embodiments, an algorithm of system 10 (e.g., algorithm 5055 as executed by processor 5050 of console 5000) analyzes the recorded signals and determines an appropriate reference signal. Alternatively or additionally, system 10 can display one or more signals (e.g., signals CS1 and CS2), allowing an operator of system 10 to access and manually select an appropriate reference signal. The shaded region AS1 indicates an identified active segment based on activation ACT1.

[0124] 3B, a graph of time-aligned recorded electrical activity is shown, consistent with the inventive concepts. As described above with reference to step 170 of method 100, the recorded signals may be time-aligned, such as by aligning identified activations on a selected reference signal (e.g., signal CS2 described above with reference to FIG. 3A). FIG. 3B shows a graph of multiple time-aligned ECG signals (signals ECG TA ), multiple time-aligned coronary sinus reference signals (CSRs) TA ), and a plurality of time-aligned signals recorded from a plurality of mapping electrodes 1151 (signals EGM TA ) as described herein. TA may be analyzed by the system 10 (e.g., by an algorithm 5055 and / or processor 5050 of the console 5000) to produce one or more maps of cardiac electrical activity.

[0125] Referring now to FIG. 4, a visual representation of pattern clustering consistent with the inventive concepts is shown. As described above with reference to step 170 of method 100, system 10 may be configured to group two or more segments of recorded data using a pattern clustering algorithm (e.g., algorithm 5055 executed by processor 5050 of console 5000). System 10 may analyze the segments identified in the data and / or identify one or more patterns within the data. For example, three patterns P1, P2, and P3 are illustrated in FIG. 4. A pattern may include activation waveforms as recorded from electrodes 1151 over a predetermined period of time (e.g., over a single activation period or a cardiac cycle). As an illustrative example, as shown by pattern P1, the waveform as recorded by each electrode 1151 includes more temporal variation, while as shown by pattern P2, the waveform as recorded by each electrode 1151 includes less temporal variation.

[0126] After identifying two or more patterns, system 10 may be configured to group (also referred to herein as "clusters") the segments based on these identified patterns. Again, as an example for illustrative purposes, system 10 may cluster the recorded data (e.g., segments) into four clusters, with cluster 1 representing data that matched pattern P1, cluster 2 representing data that matched pattern P2, cluster 3 representing data that matched pattern P3, and a fourth grouping representing segments that do not match any of patterns P1, P2, or P3. In some embodiments, the patterns may include a unique wave morphology and / or activation pattern. In some embodiments, this fourth grouping represents a percentage of segments below a threshold, such that additional patterns are not identified by system 10. In some embodiments, only patterns identified and determined to represent a population of segments above a predetermined percentage threshold will be identified for clustering. In some embodiments, the percentage threshold is greater than 5%, such as greater than 10% or 15%. In some embodiments, the system 10 is configured to identify only one, two, or three patterns (e.g., one, two, or three dominant patterns), regardless of the overall percentage of the segment population. In some embodiments, pattern clusters may be visually identified in a time course of a signal as recorded, such as that shown in FIG. 3C, where each unique color identifies segments within the same cluster. In some embodiments, the user interface may be configured to select a cluster of segments by selecting a correspondingly colored area, and cardiac information corresponding to the selected cluster may be displayed.In some embodiments, the clusters can each have a unique period length, as shown in FIG. 3D (a two-phase rhythm is shown, where the period length varies between two values).

[0127] 5A, 5B, 5C, and 5D, there are shown graphical representations of a ventricular chamber with multiple recording locations, multiple cardiac electrogram graphs, activation maps, and amplitude maps, respectively, consistent with the concepts of the present invention. FIG. 5A shows a model of a ventricular chamber C1 and a point P1 within the chamber C1. X Each point P X represents the physical location of the electrode 1151 in the cardiac chamber C1 during which a segment of data was recorded by that electrode (e.g., during which a segment of signal was recorded, such as a signal recorded over a predetermined period of time greater than the length of the segment). For example, if the mapping catheter 1100 includes 48 electrodes 1151 and 100 segments are included in the illustrated embodiment (e.g., 100 segments of biopotential signals segmented by the cardiac cycle, and 100 segments are included in the composite recording), then 4800 points P X will be illustrated (e.g., assuming no filtering for malfunctioning electrodes). In some embodiments, the mapping catheter 1100 includes at least 48 electrodes 1151, or at least 16 electrodes, or at least 10 electrodes, or at least 4 electrodes. As described above with reference to step 110 of method 100, the electrode array 1150 is navigated within the heart chamber C during this recording, and points P Xis distributed throughout the heart chamber C1 (e.g., electrode 1151 covers a large volume and / or surface area of ​​chamber C1). In some embodiments, points Px in chamber C1 are points recorded when the recording electrode is in contact with and / or close to (e.g., less than 5 mm) the chamber wall. In some embodiments, points Px in chamber C1 are points recorded when the recording electrode is not in contact with the chamber wall, e.g., an electrode positioned at a greater than a threshold distance from the chamber wall (e.g., >5 mm). In some embodiments, all points Px in chamber C1 are used regardless of the contact status of the recording electrode. In some embodiments, contacting and non-contacting points are processed separately, and the outputs of each generated model of cardiac activation are fused, integrated, blended, and / or otherwise combined.

[0128] FIG. 5B illustrates a graph of electrical activity recorded by electrodes 1151 (e.g., a composite recording as described above with reference to step 170 of method 100). In the above example, 4,800 signals would be represented in FIG. 5B. Each electrode 1151 can record biopotential data, including a biopotential signal, which can be segmented by the cardiac cycle into multiple biopotential signal segments. As described herein, one or more composite recordings can include two or more of the multiple biopotential signal segments. In some embodiments, the total number of recorded biopotential signal segments can include more than 500 biopotential signal segments, such as more than 1,000, more than 2,000, more than 5,000, or more than 10,000 biopotential signal segments. FIG. 5C illustrates an activation map, such as a local activation time (LAT) map, representing a pattern of activation calculated from the data shown in FIG. 5B. In Figure 5D, an amplitude map, such as a voltage amplitude map or a normalized charge density amplitude map, is illustrated, representing the amplitude of cardiac electrical activity at various locations on the anatomical structure calculated from the data shown in Figure 5B. As described above with reference to step 170 of method 100, activation maps may be generated from the composite recording shown in Figure 5B using, for example, automatic annotation and / or inverse methods, as described above.

[0129] System 10 is capable of displaying a composite map using a visual display. The visual display may be displayed continuously, and it may be dynamically updated. In some embodiments, the "map" (e.g., cardiac electrical information) may be dynamically updated. Additionally or alternatively, data quality and / or quantity information may be dynamically updated as the operator collects new data (e.g., all processing and display steps repeat in a loop while the operator is actively collecting). Alternatively or additionally, the display may only show data quality and / or quantity information while the operator is collecting data, and the map may be processed and displayed after data collection is complete. In some embodiments, the system is configured to process all or a portion of the steps described above in the background, and it may provide feedback to the operator while data is being recorded (e.g., operating in a closed-loop configuration). For example, system 10 may automatically inform the operator of one or more of the following: when some of the criteria for generating a composite map are currently being achieved (e.g., reference catheter criteria for stability, pattern clustering, template matching, and / or any of the above-mentioned criteria are being achieved); a suggestion to the operator that the operator can complete the composite map by completing a particular set of suggested steps (e.g., traveling to additional locations for continued recording); and a combination of one or more of these. In some embodiments, system 10 at least partially displays the map to the operator during data collection. Alternatively or additionally, system 10 may display the map to the operator following completion of data collection. In some embodiments, previously recorded data may be post-processed using one or more methods of the inventive concepts to produce the map.In some embodiments, previously recorded data may be collected when performing a separate task (eg, tracing and / or scanning an anatomical structure with ultrasound).

[0130] Referring now to Figure 6, there is illustrated a flowchart of a method for recording and modeling a patient's electrical activity consistent with the inventive concepts. The method 600 of Figure 6 is described using various components of the system 10 of Figure 1, as described above.

[0131] In step 610, data is recorded (e.g., by the console 5000) by recording elements (e.g., electrodes and / or ultrasound transducers) of one or more catheters 1000 inserted into the patient P. The data may be recorded over one or more cardiac cycles. In some embodiments, data is recorded from a catheter (e.g., the mapping catheter 1100 described herein) including an array of recording elements positioned on a basket and / or other structure. Data may also be recorded from an auxiliary catheter, such as a reference catheter, such as the coronary sinus catheter 1200. For example, a distal portion of the catheter 1200 (e.g., at least the electrode array 1250) may be positioned in the coronary sinus of the heart H of the patient P. The electrode array 1150 (e.g., the basket array including the electrodes 1151) of the catheter 1100 may be positioned in a chamber of the heart H (e.g., in the left atrium of the heart H). Data (e.g., biopotential data and / or localization data) may be recorded from both the catheters 1100 and 1200. As data is recorded, the array 1150 may be steered within the heart chamber such that the electrodes 1151 are positioned to collect data from (e.g., "cover") a larger volume of the heart chamber compared to the volume that would be covered if the array 1150 were not steered during recording. In some embodiments, the array 1150 is steered slowly (e.g., steadily, limiting rapid movements). In some embodiments, the array 1150 is steered in a pattern. The pattern can be a defined pattern, such as a robotically controlled pattern and / or a pattern known (e.g., taught) and performed by the operator.Alternatively or additionally, the pattern can be an operator-determined pattern (e.g., determined during a procedure), such as, for example, a pattern generated by an operator using visual feedback provided by system 10 (e.g., via a video monitor or other display). The provided visual feedback can assist the operator in completing one or more tasks or goals, for example, expanding, extending, and / or modifying the operator-determined pattern for areas lacking data, areas with insufficient data, and / or areas with poorly spatially distributed data; improving the amount and / or quality of data in a particular area of ​​interest; and / or replacing data within an area. Visual feedback can be used to indicate the amount and / or quality of data in time and / or across space. For example, visual feedback may be used to indicate one or more of the following: sufficient data in a region (e.g., points, locations, areas, or volumes); insufficient data in a region; coverage across the region (e.g., whether data is present or absent); data density in a region; spatial distribution of data without or with "artifacts" (e.g., measurement anomalies or errors); and consistency and / or stability of data over time (e.g., within a particular region or regardless of location).

[0132] The visual feedback may include one or more visual elements (e.g., points, lines, arrows, meshes, charts, meters, plots, etc.) that may be presented to the operator (e.g., on a display of system 10 to provide feedback, etc.). The visual elements may possess one or more attributes that are varied (e.g., varied to provide feedback), such as attributes selected from the group consisting of size; thickness; color; hue; texture; visual gradient; translucency; brightness; and combinations thereof. The patterns may be repeating and / or non-repeating sets of sub-patterns (herein “patterns”). These patterns, into which array 1150 is navigated (e.g., by a robot and / or by an operator), may be configured to cover one or more portions of the heart chamber multiple times and / or for a predetermined percentage of the recording time (e.g., at least 15% or at least 20% of the recording time, etc.). In some embodiments, data is recorded continuously for a time period of at least 10 seconds, such as at least 30 seconds, or such as at least 90 seconds. In some embodiments, visual feedback is provided to the operator in steps 691-693 described below. In some embodiments, one or more additional recordings may be performed to collect additional data to be processed (e.g., in combination with previous recordings). In some embodiments, the recording electrodes may be changed to an alternative set of electrodes (e.g., alternative electrodes on the same and / or another device). In some embodiments, devices with different numbers and configurations of electrodes may be used to record data for processing.

[0133] In step 620, the recorded data is filtered to remove or at least reduce noise, artifacts, and / or other erroneous data. Data filtering can correct for data errors including electrical interference, artifacts from mechanical movement, artifacts caused by electrode contact with tissue, temporary or permanent disconnection of the measurement electrodes, and combinations of one or more of these. In some embodiments, system 10 can filter one or more of the recorded electrical signals as described above with reference to step 120 of FIG. 2.

[0134] In step 630, a portion of the recorded data is segmented for processing. In some embodiments, the segmented portion can be as short as a single time sample or as long as several hours, such as between 100 milliseconds and 3 seconds, such as 100 milliseconds, 200 milliseconds, or 400 milliseconds. In some embodiments, the length of the data segment is governed by the stability of the measurement device. For example, the length of the segmented portion of data can be automatically determined and selected for processing based on a period of minimal movement of the recording electrode array. In some embodiments, the system determines that the recording electrode array remains stable within a region (e.g., a particular volume within a ventricular cavity) for a predetermined amount of time by determining the time the array is moved into the region and the time the array is moved out of the region.

[0135] In step 640, the recorded data is analyzed, such as by one or more algorithms 5055 and / or processor 5050 of console 5000. The recorded data may be processed using inverse techniques to solve for cardiac electrical activity information. In some embodiments, the electrical activity information may include raw surface charge density data (e.g., dipole density data), surface voltage data, and / or activation time data.

[0136] In step 650, cardiac electrical properties are calculated using the cardiac electrical activity information determined in step 640. In some embodiments, conduction velocity (e.g., magnitude, direction, and / or both) and / or one or more conduction patterns are calculated. The one or more identified conduction patterns may include those described in Applicant's co-pending U.S. patent application Ser. No. 16 / 097,959, entitled "CARDIAC MAPPING SYSTEM WITH EFFICIENCY ALGORITHM," filed October 31, 2018, the contents of which are incorporated herein by reference in their entirety for all purposes. In some embodiments, a complexity index is interpreted from one or more of the following: detected conduction patterns; surface charge density and / or voltage electrograms; detected activation times; calculated conduction velocities and / or relative conduction velocity changes; other physiological measurements related to cardiac rhythm; and combinations of one or more of these. In some embodiments, two or more electrical characteristics (or patterns of characteristics) detected at one or more unique locations of cardiac tissue dynamically engage (also referred to herein as "couple") with one another, for example, when engaged simultaneously and / or sequentially in time.

[0137] In step 660, based on the location of the measurement device (e.g., recording electrode array), it is determined that cardiac information measured from any location has some degree of influence and / or impact on the processed cardiac electrical activity. Among the processed cardiac electrical activity data calculated from measurements made from electrode location L1 (a location on or within the ventricular cavity), electrical activity data at one or more locations on the ventricular cavity (the same or different locations as L1) may be preferentially or non-preferentially included, excluded, and / or weighted. For example, if the catheter is near a central and / or balanced location within the heart chamber, the cardiac electrical information may be uniformly weighted to a moderate value (e.g., 0.5 on a scale of 0 to 1). If the catheter is very close to the heart chamber wall, closer tissue areas (e.g., at a distance less than 40 mm) may be weighted more highly (e.g., 0.9 on a scale of 0 to 1), while more distant tissue areas (e.g., at a distance greater than 40 mm) may be weighted less highly (e.g., 0 or 0.1). The weighting can include a gradient as a function of distance. This weighting can be used to emphasize data recorded from closer locations (where the inverse solution can have improved accuracy). Another criterion for determining the weighting coefficients can be the solid angle relative to the tissue region (e.g., more frontal is weighted higher, while more oblique is weighted lower). The weights can be binary (0 or 1), such as using a strict inclusion-versus-exclusion model. The weighting algorithm can be applied automatically by system 10. Alternatively or additionally, the weights can be manually adjusted. In some embodiments, the impact of the processed data is visually indicated to the operator during and / or after step 660, in step 693, described below.

[0138] In step 670, system 10 may be configured to display a "live" view of the calculated electrical activity data (e.g., in real time, or at least near real time, as the data is calculated). If system 10 is configured to display live data, step 695 is performed similarly to step 680. If system 10 is not configured to display live data, process 600 continues with step 680 only.

[0139] If the operator continues to record data in step 680, then process 600 continues to step 610, where process 600 is repeated while data is being recorded. If no more data is to be recorded, then process 600 continues to step 6501.

[0140] In step 6501, cardiac electrical data is calculated using cardiac electrical activity information determined throughout one or more iterations of process 600. The cardiac electrical data calculated in step 6501 includes composite data based on data recorded from one or more locations over one or more recording time periods. After 6501, process 600 continues to step 695.

[0141] In step 695, the composite cardiac electrical data determined during process 600 is displayed to the operator. In some embodiments, steps 691-695 (the "display steps" as shown) are performed simultaneously with one or more of steps 610-690, for example, as referenced above.

[0142] In step 691, one or more locations of one or more recording devices are displayed to the operator with respect to the model of the cardiac anatomy (eg, the location of the recording device during recording).

[0143] In step 692, the quality and / or quantity of recorded information may be visually displayed to the operator. For example, for a model of cardiac anatomy, one or more indicators may be used to inform the operator of sufficient recording in one or more regions. When sufficient recording has occurred in all desired regions, a "go / no-go" indicator (e.g., a colored light, etc.) may be displayed to the operator.

[0144] In step 693, the weighted composite data is displayed to the operator. The display can summarize all data after completion of all or at least a portion of the data collection. Alternatively or additionally, the display can continuously (e.g., dynamically) display the weighted data as it is recorded and calculated. The display can also include additional visual information and / or overlay to assist the operator in optimally collecting data and / or steering the recording device. For example, the display can indicate that data is of insufficient quantity and / or quality in an area, so that steering the catheter toward that area can improve the quantity or quality of collected data. In some embodiments, the weighted composite data can be displayed in its original form, i.e., using a modified weighted composite value, but with the same visual characteristics and behavior. In some embodiments, the weighted composite data is processed through a separate algorithm to summarize the data and show, for example, a summary map of the electrical properties or conduction patterns of different regions of the ventricular cavity, or the interactions (e.g., connectivity) between regions, e.g., by visually highlighting areas that consistently and stably exhibit particular signal characteristics and / or conduction patterns.

[0145] In step 694, the visual feedback display also provides a temporary visual indication that new data has been added to the composite map and from which location the measurement was taken. In some embodiments, points at different locations have altered coloring or other changes in appearance. In some embodiments, a second visual element provides a visual accent to the data at a given location (e.g., a white ring around the location point).

[0146] Referring now to FIG. 7, a flowchart of a method for recording and modeling a patient's electrical activity consistent with the inventive concepts is illustrated. Method 700 of FIG. 7 may be implemented using various components of system 5000 of FIG. 1, as described above. Method 700 of FIG. 7 can be similar to method 100 of FIG. 2, as described above. In some embodiments, method 700 is performed during a clinical procedure, for example, where cardiac electrical activity is calculated and displayed to an operator of system 10 in real time or very near real time. Alternatively or additionally, one or more steps of method 700 may be performed after completion of a clinical procedure (during which biopotential and location data were recorded).

[0147] In some embodiments, the steps of method 700 may be performed in the order shown. Additionally or alternatively, the steps of method 700 may be performed in any other order suitable for modeling and displaying cardiac electrical activity, for example, as described herein. In some embodiments, one or more beats are grouped based on the beat's morphology. Then, each group is filtered based on cycle length (e.g., one or more beats are removed from the group). Finally, two groups may be merged if they contain the same cycle length and similar morphology.

[0148] In this description of an exemplary flow of a method 700 for recording and modeling a patient's electrical activity (particularly cardiac electrical activity), the description first follows a linear process and then returns to decision boxes (dashed diamond shapes) to fill in the description of exemplary alternative flows. The method represents a computer process that records and processes biopotential data or other cardiac or biometric data and generates one or more outputs (including display and / or electronic signals and transmissions).

[0149] In this exemplary embodiment, the process begins in step 702, where the system 5000 acquires cardiac biopotential data over one or more cardiac cycles. As explained in the discussion relating to Figure 2, data may be recorded when recording elements are in contact or not in contact with cardiac tissue, or when a combination of contacting and not in contact recording elements (e.g., electrodes) is present. Other data acquisition options are described in more detail with reference to Figure 2.

[0150] After acquiring the biopotential data in step 702, the process moves to step 704, during which the biopotential data is filtered. As explained in more detail in the discussion relating to FIG. 2, the system 5000 may, by way of example, use any of a variety of filtering techniques (e.g., a V-wave filter, etc.).

[0151] In step 706, cardiac cycles (e.g., beats) are identified and the cycle length (CL) is calculated. After calculating the cycle length, in step 708, the system 5000 determines whether the calculated cycle length is consistent with any current beat group. If the cycle length falls within the threshold for the current beat group, the process proceeds to step 710, during which cardiac biopotential data is evaluated based on secondary attributes, such as timing patterns or signal morphology / shape over one or more recording elements (e.g., one or more of the electrodes described herein).

[0152] Then, in step 712, the system 5000 can use a data clustering method to determine, for example, whether secondary characteristics match existing beat groups. In step 714, the biopotential data can be segmented based on beat cycle length, and in step 716, the system can filter or exclude unwanted data from the set of beat biopotential data acquired for each channel / electrode. Unwanted, discarded data can include, for example, noise, artifacts, erroneous measurements, or disconnections. Such unwanted data can be detected, filtered out, and discarded using any of a variety of filtering techniques.

[0153] After filtering in step 716, the system 5000 can assign the current beat to a beat group in step 718, and from there proceed to step 720, during which the system 5000 determines whether to update the cardiac map data "on-the-fly," for example, in real time or near real time, and then proceed to step 722, during which the system 5000 determines whether to continue acquiring biopotential data.

[0154] From step 722, the process proceeds to step 724, during which the system 5000 time-aligns all beats in the beat group to generate an aggregated / accumulated measurement set for multiple cardiac locations over a single cardiac cycle. In step 726, the system 5000 models cardiac electrical activity based on the aggregated / accumulated measurement set.

[0155] Returning to steps 708 and 712, the system may start a new beat group in step 728 if the beat cycle length is inconsistent with the current beat group (decision box 708) or if secondary characteristics do not match the current beat group (decision box 712). From step 728, the process proceeds to step 730, during which the biopotential data is segmented based on the beat cycle length, and then to step 732, during which unwanted data is filtered from the set of beat biopotential data. The process then proceeds to step 734, during which the system 5000 generates a model of characteristics to which future beats can be "matched." The method then proceeds to step 718, during which the process proceeds as previously described.

[0156] Returning to step 720, if the system 5000 updates the cardiac map on-the-fly, the process proceeds to step 736, during which the system 5000 time-aligns all beats in a beat group and generates an aggregated / accumulated measurement set from multiple locations over a single cardiac cycle. The process then proceeds to step 738, during which the system models the cardiac electrical activity based on the aggregated / accumulated measurement set, and from there proceeds to step 748 to display the cardiac electrical activity and, optionally, any related data and / or information. The process can return to step 702, where it can continue to acquire biopotential and location data as previously described, so that the cardiac activity and any related data and / or information can then be further processed and displayed.

[0157] Returning to step 722, the process returns to step 702, during which it continues to acquire biopotential and location data if the system determines that it is continuing to acquire data, as previously described.

[0158] From step 702, the system may proceed to step 740, during which it models cardiac electrical activity based on the aggregated / accumulated measurement set when sufficient data has been acquired. This step may prepare for generating a cardiac activity display, or for further processing of the electrical cardiac data, or both.

[0159] From step 712 or from step 740, the system may proceed to step 742, during which the system may generate one or more displays of quality and distribution summaries of all previous acquisition locations of beats assigned to the current beat group. From step 742, the process proceeds to step 744, during which the system updates the quality and distribution summary display to include the current beat. In step 746, the system may temporarily display one or more locations of new data added to the beat group by highlighting the associated electrode, for example, by flashing the electrode color, displaying a ring around the electrode, sounding a tone, displaying a callout, or using other highlighting or differentiation methods. In step 748, the system displays cardiac electrical activity, which may include a graphical representation of at least one chamber of the heart with cardiac activity data overlaid thereon.

[0160] From step 718, the system can proceed to step 744, during which the system updates the display of the quality and distribution summary to include the current beat, as previously described from step 744.

[0161] From steps 726 and 738, the process can proceed to step 748, where cardiac electrical activity is displayed.

[0162] 8A-8C, various displays of cardiac activity maps consistent with the concepts of the present invention are illustrated. The maps and displays may be generated by system 5000 of FIG.

[0163] 8A-8C illustrate, among other things, conduction isthmus visualization that may be provided by systems and methods in accordance with the principles of the inventive concepts to assist physicians in, for example, cardiac ablation procedures to treat or terminate arrhythmias. Complex atrial tachycardias (ATs) can be difficult to map and ablate, at least in part, because ablation-induced scarring can result in low-amplitude electrical activity, thus making it more difficult to detect and map atrial activity within and around the ablation-induced scarring. Because areas of low-amplitude activity often contain sites where ablation is used to terminate arrhythmias (critical isthmuses), it is crucial to provide physicians with a clear, detailed view of activity despite reduced, low-amplitude signal levels. In an exemplary embodiment, a system in accordance with the principles of the inventive concepts may use any one or any combination of active area plots, streamline plots, or AutoPath plots.

[0164] Active Area Plot

[0165] Active area (AA) plots can be used to help identify conduction isthmuses and can be constructed by plotting the normalized cycle length of a cardiac rhythm on the X-axis and the normalized amount of depolarized tissue on the Y-axis. For each portion of the rhythm cycle length, the percentage of depolarized atrial tissue can be determined by counting all vertices of an anatomical structure with local activation times (within a + / - time window, such as 20 ms) around a time point in the rhythm cycle length and dividing by the total number of vertices of the anatomical structure. The vertices referred to can be vertices of a polygonal shape projection onto the epicardial surface that forms a mesh, and system 10 computes dipole densities at all vertices. In some embodiments, the projection of such a polygonal shape can be in the form of a series of abutting triangles that form a mesh on the cardiac surface. For example, for a rhythm with a 300 ms cycle length, at time point 0, all vertices with activation between 0-20 ms and 0+20 ms can be counted and divided by the total number of vertices. Peaks active between valleys in the AA map often coincide with isthmic conduction.

[0166] Figure 8A shows an example of an AA plot. In an exemplary embodiment, activation times calculated from electrograms are used directly to calculate depolarized tissue areas using charge density or unipolar voltage directly. The method shown here is similar to that described in "Automatic Identification of Reentry Mechanisms and Critical Sites during Atrial Tachycardia by Analyzing Areas of Activity," IEEE Trans. Biomed. Eng., no. February 2018, T.G. Oesterlein, A. Loewe, G. Lenis, A. Luik, C. Schmitt, and O. Doessel, which is incorporated herein by reference. However, an important difference is that in Oesterlein et al., the authors threshold a function based on raw electrograms to determine the amount of tissue depolarized, whereas in this approach, activation times computed from electrograms are used directly in computing the area depolarized. Additionally, the approach in Oesterlein et al. uses filtered bipolar electrograms for determination of the area of ​​depolarization, whereas this approach uses direct unipolar voltage or charge density.

[0167] In the lower half of FIG. 8A (labeled B), an AA plot shows the amount of depolarized tissue at each instant during the AT cycle length. A shaded vertical band overlay 802 highlights the valleys in the AA plot. In the upper half of the figure (labeled A) is a local activation time (LAT) map with streamlines (SL) showing conduction flow. The highlighted areas in the 3D map correspond to the valleys in the AA plot, shown with the shaded overlay. SL begins outward-type conduction after leaving isthmus-type conduction. In this example, the patient has a gap in the previous CTI line, which corresponds to the highlighted area in the map.

[0168] Streamline Plot

[0169] Streamline plots are a method according to the principles of the inventive concept to help visualize conduction flow. Areas of converging streamlines can indicate isthmus conduction. Streamlines indicate the path that conduction takes through the anatomy (as shown in Figure 8B). Vector fields

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[0170] This forward and background propagation occurs when either 1) a line of block (defined as a large time difference between successive steps along a streamline or a rapid change in path direction along a streamline (e.g., a direction change of greater than 90 degrees)) or 2) a streamline passes across an "early meets late" activation timing.

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[0171] Once found, streamlines can be animated over the anatomical structure to show the conduction flow. The animation can be set by initializing a time t, as illustrated in FIG. 8B, and then individually progressing each streamline with a vector or particle. In an exemplary embodiment, animated objects along the streamlines can have variations in the object along the streamline to show specific characteristics of the anatomical structure / substrate. For example, the velocity of the animated object can change along regions of lower conduction, or the size of the object can change to show the relative electrical mass (amplitude) of the tissue. Arrows, lines, particles, or other objects can be animated to progress along the streamlines. Animating the streamlines gives the appearance of flow through the anatomical structure, enhancing visualization for the physician.

[0172] Auto Route

[0173] Auto-pathway is an exemplary embodiment method according to the principles of the inventive concepts for automatically illustrating possible pathways for reentry on cardiac anatomy given a patient's LAT map (or propagation history). Reentry may be defined as an abnormal electrical impulse that continues to self-sustain instead of disappearing, dissipating, or decaying by colliding with non-excitable tissue. Reentry may be visually identified by finding pathways of electrical activity (usually in the form of LAT time) that form closed loops. For example, if a reentrant arrhythmia has a cycle length of 300 ms, any pathway that can be traced from 0 ms to 300 ms and then back to 0 ms while maintaining normal physiological conduction characteristics (e.g., minimum and maximum conduction velocity) may be considered a possible reentrant pathway.

[0174] A shortest path algorithm can be used to find candidate reentrant pathways, which can then be further evaluated to determine whether the pathways maintain normal physiological conduction. Given an anatomical mesh and an LAT on the mesh, edge weights can be computed to indicate the cost of traveling from one vertex to another. Edges from one vertex to another that follow physiological conduction given the LAT on the vertices can be given a low weight, while less physiological or specific conduction from one vertex to another can be given a higher weight.

[0175] Candidate pathways between two vertices can be found by minimizing the total edge weight for traveling between the two vertices. This is because more physiological conduction will have a lower edge weight. Several algorithms are known for efficiently finding pathways that minimize the cost of traveling between them, and can include using Dijkstra's algorithm. Edge weights between adjacent vertices can be constructed based on the angle between the conduction velocity and the distance vector between the two adjacent nodes, as well as the LAT time difference between the adjacent nodes. One such formation can be:

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[0176] The algorithm sequence for automatically determining possible reentrant pathways over cardiac anatomy given a LAT or propagation history map can be summarized as follows:

[0177] Algorithm - To automatically discover reentrant pathways, Input: number of seeded reentrant pathways, r (typically 80-1000), mesh (vertices and faces), LAT on top of the mesh. a. Construct an edge weight matrix using the anatomical mesh and LAT on the mesh b. For all r's: i. Randomize two vertices v1 and v2 on the mesh ii. Find a path p1 to minimize the edge weight between v1 and v2 iii. Find path p2 to minimize the edge weight between v2 and v1 iv. p1 and p2 are connected to form a candidate path, CandidatePath n Form c. All CandidatePaths n Regarding i. CandidatePath that does not meet some criteria (e.g., have a minimum path length, contain a minimum cycle length, maintain conduction velocity within a physiological range) n to form the set of confirmedReentrantPaths. ii. Clustering / grouping similar confirmedReentrantPaths based on features such as Frechet distance, and other features such as shape descriptors may also be used in combination with unsupervised learning techniques. iii. Determine a representative confirmedReentrantPath from each of the clusters / groups found above. iv. Filter / smooth the representative confirmedReentrantPaths. Filtering can include mean / median filtering, active snake / contour filtering, and / or other filtering-based techniques.

[0178] Once pathways are found, one or more of the pathways may be displayed over or along with the anatomical structure.

[0179] FIG. 8C illustrates exemplary output of the automatic pathway annotation algorithm on two cases superimposed on an LAT map in accordance with the principles of the inventive concepts. Reentry pathways are identified by gray lines 804 on the anatomical structures. In the upper part of FIG. 8C (labeled "A"), three potential pathways of reentry are automatically identified for the left atrial anatomy. In the lower part of FIG. 8C (labeled "B"), three potential pathways of reentry are automatically identified within the right atrium.

[0180] 9A-9D, there are illustrated sequential data acquisition, cluster samples, fuzzy membership functions, and biodata for beats in a given group consistent with the inventive concepts. In an exemplary embodiment, real-time clustering is used to assist physicians in visualizing cardiac structure and activity. Biodata (e.g., CS or ECG) may be grouped in real time or near real time; that is, each incoming beat may be assigned to an existing group or to a new group. Such real-time clustering may be implemented in systems and methods according to the principles of the inventive concepts as follows: a. Buffering: The steps described here are similar to those in offline clustering. i. The initial step is to obtain activation on the CS channel while omitting activation above the QRS (QRS blanking) and to remove the CS and ECG channels with artifacts. ii. Segment the CS channel around activation. This segment is usually smaller than the cycle length. It usually covers only the "active CS time" (narrowband segmentation). iii. Cluster the segments using a current wavelet-based clustering method and pick a representative sample from each group as the average template. The representative sample may be selected as the sample with the highest cross-correlation of that group to the average sample. b. Loop until enough data is acquired i. Obtain new biodata (it can be set to a fixed or variable length. For example, it can be set as 1.2 times larger than the minimum cycle length of the existing group) ii. Detect activation on CS channels and estimate QRS width and cycle length on new biodata iii. Grouping / Clustering 1. For a new beat, check if the cross-correlation of any group against the template is greater than a threshold, then assign the group label (to which the new beat has the highest cross-correlation (cross-correlation can be normalized 2D cross-correlation, CS channel X samples) or statistical metric (e.g., mean, min, or max of 1D cross-correlation of the respective channels, etc.)). 2. If the cross-correlation is lower than a threshold, perform wavelet decomposition and keep the k coefficients, which can be the k largest coefficients or they are selected from a fixed index. a. Use a hierarchical clustering method on the wavelet coefficients to generate superclusters. b. If any supercluster has a sufficient number of samples, assign a new group label to the samples and mark the representative sample as the average template. 3. Add two cycles of basket potential data to the basket potential data of the corresponding group while removing bad electrodes (high amplitude outward movements). c. Further clarifying basket potential data with Gaussian mixture model (artifacts, high amplitudes)

[0181] In an exemplary embodiment, the method described in the clustering step (b)(iii) can also be implemented by other methods. For example, cross-correlation-based matching can be replaced by wavelet transform-based matching. Selecting the k coefficients in the wavelet transform can be replaced by dimensionality reduction methods, such as approximated T-distributed Stochastic Neighbor Embedding (TSNE), principal component analysis, etc.

[0182] In an exemplary embodiment, visual feedback can also be provided for each cluster during acquisition. Visual feedback can be shown for each cluster's ventricular cavity coverage (distance to the nearest electrode), number of beats, CS signal, and cycle length. An example of visual feedback is shown in FIG. 9A , which illustrates sequential data acquisition for sinus CS proximal and distal pacing. Each subfigure displays a horizontal bar plot showing the coverage over the anatomical structure for the selected cluster and the number of clusters and beats for each cluster. The reference CS channel for each cluster is also shown along the bar plot. The subfigures are as follows: subfigure (a) after the buffering step; subfigure (b) during sinus rhythm acquisition; subfigure (c) proximal CS pacing (coverage is for cluster 1); and subfigure (d) distal CS pacing (coverage is for cluster 3).

[0183] Figure 9B illustrates the corresponding CS channels with representative samples from each cluster. The subfigures are as follows: subfigure (a) sinus rhythm, subfigure (b) proximal CS pacing, and subfigure (c) distal CS pacing.

[0184] In the above grouping algorithm, only morphological features are considered. In accordance with the principles of the inventive concept, other features (e.g., period length, activation time sequence / pattern, etc.) may also be considered. Multiple features may be applied in a sequential manner, as explained above in the discussion related to FIG. 7, or may be combined in a single step using fuzzy membership functions.

[0185] A fuzzy membership function converts any real-valued function into a probability value function (1=pass, 0=fail, other values=fuzzy (uncertainty) zone). For multiple attributes, the total cost in a fuzzy membership function can be written as: F(λ)=λμ CL +λ2μ morpth +λ3μ ActTime where λ1+λ2+λ3=1, {λ1,λ2,λ3}∈

[0001] , and λ=[λ1,λ2,λ3] controls the influence of each membership function (μ).

[0186] In an exemplary embodiment, period length and morphology may be combined for a given use as follows:

[0187] In various embodiments, the specifications can be as follows: (a) the average cycle length is 200 ms, the allowable cycle length change is 10%, and a change in cycle length of more than 20% is considered a different cluster; (b) the cross-correlation threshold is 0.8, and when the cross-correlation value is 0.6, there is a 50% confidence that they belong to the same cluster. With these specifications, a fuzzy membership function can be calculated as shown in FIG. 9C. Here, for each incoming beat, the total cost F(λ)=λμCL+λμmorpth is calculated for a given value of λ, and then it is checked whether the cost value exceeds the threshold. FIG. 9C illustrates the fuzzy membership function as follows: subfigure (a) for cross-correlation (μmorpth), and subfigure (b) for cycle length F(λ)=(μCL).

[0188] Automated Segmentation

[0189] For a given group, one cycle length of EGM data can be automatically segmented as follows: For each beat of a given group, two cycle lengths of EGM, ECG, and CS data are collected and stacked together. Data is collected one cycle length before and one cycle length after each reference CS channel activation (see FIG. 9D). Within the two cycle lengths of data, the automatic segmentation provides two endpoints, separated by one cycle length, such that the number of beats with QRS and T waves within the one cycle length data is as low as possible (see the black dotted lines in FIG. 9D).

[0190] In Figure 9D, two cycle lengths of biodata for all beats in a given group are stacked together. The black dotted lines are estimated by the automated segmentation method. The lines are one cycle length apart, and the segments within them have the lowest possible QRS and T waves.

[0191] In addition to the above constraints, in an exemplary embodiment, an additional constraint may be added, such as a constraint that the endpoints should have either low or high EGM (envelope) energy values. This constraint may be implemented by the following steps: a. Estimate the EGM envelope and calculate the energy (voltage squared) b. Estimate the onset and offset for the T wave and QRS for each beat. c.Find potential endpoints with low EGM energy. d. For each possible endpoint pair, calculate the cost - how many beats have T waves and QRS and what percentage of the cycle length they cover. The cost calculation can be performed with different weights for QRS and T waves. e. Pick the endpoint with the lowest cost. f. (Optionally) Discard segments with non-zero cost if they have enough beats with zero cost.

[0192] It should be understood that the above-described embodiments serve as examples for illustrative purposes only. Additional embodiments are contemplated. Any feature described herein with respect to any one embodiment may be used alone or in combination with other features described, and may also be used in combination with one or more features of any other of the embodiments, or any combination of any other of the embodiments. Moreover, equivalents and modifications not described above may also be used without departing from the scope of the invention, as defined in the appended claims.

Claims

[Claim 1] 1. A system for modeling cardiac electrical activity data of a patient, the system including at least one diagnostic catheter for insertion into the patient's heart, the at least one diagnostic catheter including at least one recording element configured to record patient data over a plurality of cardiac cycles, the patient data being: Biopotential data and; stored and / or received localization data including the location of said at least one recording element; and Including, The system also includes a processing unit including a clustering routine, the clustering routine comprising: configured to receive the recorded patient data; configured to segment the recorded patient data by cardiac cycle to produce segmented patient data comprising the segments; configured to group the segments based on one or more characteristics of the segments to create segmented data groups; configured to combine the segmented patient data within each segmented data group to create one or more composite records; The system is configured to generate one or more models of the patient's cardiac electrical activity based on the one or more composite recordings.

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