Tissue Treatment System

Abstract

The cardiac information dynamic display system includes one or more electrodes and a cardiac information console. The one or more electrodes record sets of electrical potential data representative of cardiac activity at a plurality of time intervals. The cardiac information console includes a signal processor and a user interface module. The signal processor uses the sets of recorded electrical potential data to calculate sets of cardiac activity data at the plurality of time intervals. The cardiac activity data is associated with surface locations of one or more heart chambers. The user interface module displays information related to the cardiac activity data. The information is presented with respect to a graphical representation of the surface of the one or more heart chambers.

Description

[Technical field]

[0001] The present concepts relate generally to systems, devices and methods for ablating tissue, and more particularly, to systems, devices and methods for ablating cardiac tissue of a patient. [Background technology]

[0002] Related Applications This application claims priority to U.S. Provisional Patent Application No. 63 / 203,606, entitled "TISSUE TREATMENT SYSTEM," filed on July 27, 2021, which is incorporated herein by reference.

[0003] This application claims priority to U.S. Provisional Patent Application No. 63 / 335,939, entitled "TISSUE TREATMENT SYSTEM," filed April 28, 2022, which is incorporated herein by reference.

[0004] This application does not claim priority to, but may be related to, U.S. Provisional Patent Application No. 63 / 226,040, entitled “Energy Delivery Systems With Lesion Index,” filed July 27, 2021, which is incorporated herein by reference.

[0005] This application does not claim priority to, but may be related to, U.S. Provisional Patent Application No. 63 / 260,234, entitled "Intravascular Atrial Fibrillation Treatment," filed on August 13, 2021, which is incorporated herein by reference.

[0006] This application does not claim priority to, but may be related to, a U.S. national phase entry application of Patent Cooperation Treaty Application No. PCT / US2022 / 016722, entitled “Energy Delivery Systems With Ablation Index,” filed February 17, 2022, which claims priority to U.S. Provisional Patent Application No. 63 / 150,555, entitled “Energy Delivery Systems With Ablation Index,” filed February 17, 2021, each of which is incorporated herein by reference.

[0007] This application does not claim priority to, but may be related to, U.S. application Ser. 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 PCT / US2017 / 056064, entitled "Ablation System with Force Control," filed October 11, 2017, which claims priority to U.S. Provisional Patent Application Ser. No. 62 / 406,748, entitled "Ablation System with Force Control," filed October 11, 2016, and U.S. Provisional Patent Application Ser. No. 62 / 504,139, entitled "Ablation System with Force Control," filed May 10, 2017, each of which is incorporated herein by reference.

[0008] This application does not claim priority to, but may be related to, U.S. application Ser. No. 16 / 097,955, filed Oct. 31, 2018, entitled “Cardiac Information Dynamic Display System and Method,” which is a national stage application under 35 U.S.C. § 371 of Patent Cooperation Treaty application PCT / US2017 / 030915, filed May 3, 2017, entitled “Cardiac Information Dynamic Display System and Method,” which claims priority to U.S. Provisional Patent Application Ser. No. 62 / 331,351, filed May 3, 2016, entitled “Cardiac Information Dynamic Display System and Method,” each of which is incorporated herein by reference.

[0009] This application does not claim priority to, but may be related to, U.S. Patent Application No. 16 / 861,814, entitled “Catheter System and Methods of Medical Uses of Same, Including Diagnostic and Treatment Uses for the Heart,” filed April 29, 2020, which is a continuation of U.S. Patent No. 10,667,753, entitled “Catheter System and Methods of Medical Uses of Same, Including Diagnostic and Treatment Uses for the Heart,” filed June 19, 2018, which is a continuation of U.S. Patent No. 10,004,459, 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 continuation of U.S. Patent No. 10,004,459, entitled “Catheter System and Methods of Medical Uses of Same, Including Diagnostic and Treatment Uses for the Heart,” filed August 30, 2013. This application is a national stage application under 35 U.S.C. 371 of Patent Cooperation Treaty application PCT / US2013 / 057579, entitled "System and Method for Diagnosing and Treating Heart Tissue," which claims priority to U.S. Provisional Patent Application No. 61 / 695,535, filed August 31, 2012, entitled "System and Method for Diagnosing and Treating Heart Tissue," each of which is incorporated herein by reference.

[0010] This application does not claim priority to, but may be related to, U.S. patent application Ser. No. 16 / 242,810, entitled “Expandable Catheter Assembly with Flexible Printed Circuit Board (PCB) Electrical Pathways,” filed Jan. 8, 2019, which is a continuation of U.S. Patent No. 10,201,311, 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 PCT / US2014 / 015261, entitled “Expandable Catheter Assembly with Flexible Printed Circuit Board (PCB) Electrical Pathways,” filed Feb. 7, 2014, which is a continuation of U.S. Patent No. 10,201,311, entitled “Expandable Catheter Assembly with Flexible Printed Circuit Board (PCB) Electrical Pathways,” filed Feb. 8, 2013 ... This application claims priority to U.S. Provisional Patent Application No. 61 / 762,363, entitled "Microelectronics for Integrated Circuits and Microelectronics Electrical Pathways," each of which is incorporated herein by reference.

[0011] This application does not claim priority to, but may be related to, U.S. patent application Ser. No. 16 / 533,028, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls," filed Aug. 6, 2019, which is a continuation of U.S. Patent No. 10,413,206, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls," filed June 21, 2018, which is a continuation of U.S. Patent No. 10,376,171, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls," filed Feb. 17, 2017, which is a continuation of U.S. Patent No. 10,376,171, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls," filed Sep. 25, 2015. No. 9,610,024, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls," filed on November 19, 2014, 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 on February 25, 2014.No. 8,700,119, entitled “Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls,” filed on April 8, 2013, which is a continuation of U.S. Pat. No. 8,417,313, entitled “Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls,” filed on February 3, 2009, which is a continuation of U.S. Pat. No. 8,417,313, entitled “Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls,” filed on August 3, 2007. This application is a national stage application under 35 U.S.C. 371 of PCT application PCT / CH2007 / 000380 entitled "Integrated Circuit Walls," which claims priority to Swiss Patent Application No. 1251 / 06, filed August 3, 2006, each of which is incorporated herein by reference.

[0012] This application does not claim priority to, but may be related to, U.S. Patent No. 11,116,438, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," filed September 12, 2019, which is a continuation of U.S. Patent No. 10,463,267, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," filed January 29, 2018, which is a continuation of U.S. Patent No. 9,913,589, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," filed October 25, 2016, which is a continuation of U.S. Patent No. 9,913,589, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," filed October 19, 2015. No. 9,504,395, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," which is a continuation of U.S. Patent 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 No. 8,512,318, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," filed on July 16, 2010.255, which is a national stage application under 35 U.S.C. 371 of Patent Cooperation Treaty Application No. PCT / IB2009 / 000071, filed January 16, 2009, entitled "A Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," which claims priority to Swiss Patent Application No. 00068 / 08, filed January 17, 2008, each of which is incorporated herein by reference.

[0013] This application does not claim priority to, but may be related to, U.S. patent application Ser. No. 17 / 673,995, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," filed February 17, 2022, which is a continuation of U.S. Patent No. 11,278,209, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," filed April 19, 2019, which is a continuation of U.S. Patent No. 10,314,497, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," filed March 20, 2018, which is a continuation of U.S. Patent No. 10,314,497, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," filed August 8, 2017. No. 9,968,268, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," filed on September 6, 2013, which is a continuation of U.S. Patent No. 9,757,044, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," filed on March 9, 2012, which is a national stage application under 35 U.S.C. 371 of Patent Cooperation Treaty application PCT / US2012 / 028593, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," filed on March 10, 2011.This application claims priority to US Pat. No. 3,357, each of which is incorporated herein by reference.

[0014] This application does not claim priority to, but may be related to, U.S. Design Patent No. 29 / 681,827, entitled "Set of Transducer-Electrode Pairs for a Catheter," filed on February 28, 2019, which is a divisional application of U.S. Design Patent No. D851,774, entitled "Set of Transducer-Electrode Pairs for a Catheter," filed on February 6, 2017, which is a divisional application of U.S. Design Patent No. D782,686, entitled "Transducer-Electrode Pair for a Catheter," filed on December 2, 2013, which is a divisional application of U.S. Design Patent No. D782,686, entitled "Catheter System and Methods of Medical Uses of Same, Including Diagnostic and Treatment Uses for the Same," filed on August 30, 2013. This application is a national stage application under 35 U.S.C. 371 of Patent Cooperation Treaty application PCT / US2013 / 057579, entitled "System and Method for Diagnosing and Treating Heart Tissue," which claims priority to U.S. Provisional Patent Application No. 61 / 695,535, filed August 31, 2012, entitled "System and Method for Diagnosing and Treating Heart Tissue," each of which is incorporated herein by reference.

[0015] This application does not claim priority to, but may be related to, U.S. patent application Ser. No. 16 / 111,538, entitled "Gas-Elimination Patient Access Device," filed Aug. 24, 2018, which is a continuation of U.S. Patent No. 10,071,227, entitled "Gas-Elimination Patient Access Device," filed July 14, 2016, which is a national stage application under 35 U.S.C. § 371 of Patent Cooperation Treaty application PCT / US2015 / 011312, entitled "Gas-Elimination Patient Access Device," filed Jan. 14, 2015, which claims priority to U.S. Provisional Patent Application Ser. No. 61 / 928,704, entitled "Gas-Elimination Patient Access Device," filed Jan. 17, 2014, each of which is incorporated herein by reference.

[0016] This application does not claim priority to, but may be related to, U.S. patent application Ser. No. 17 / 578,522, entitled "Cardiac Analysis User Interface System and Method," filed Jan. 19, 2022, which is a continuation of U.S. Patent No. 11,278,231, entitled "Cardiac Analysis User Interface System and Method," filed Sep. 23, 2016, which is a national stage application under 35 U.S.C. 371 of Patent Cooperation Treaty application PCT / US2015 / 022187, entitled "Cardiac Analysis User Interface System and Method," filed Mar. 24, 2015, which claims priority to U.S. Provisional Patent Application Ser. No. 61 / 970,027, entitled "Cardiac Analysis User Interface System and Method," filed Mar. 25, 2014, each of which is incorporated herein by reference.

[0017] This application does not claim priority to, but may be related to, U.S. Patent Application No. 17 / 063,901, filed October 6, 2020, entitled "Devices and Methods for Determination of Electrical Dipole Densities on a Cardiac Surface," which is a continuation of U.S. Patent No. 10,828,011, filed March 2, 2016, entitled "Devices and Methods for Determination of Electrical Dipole Densities on a Cardiac Surface," which is a national stage application under 35 U.S.C. 371 of Patent Cooperation Treaty Application No. PCT / US2014 / 054942, filed September 10, 2014, entitled "Devices and Methods for Determination of Electrical Dipole Densities on a Cardiac Surface," which is a continuation of U.S. Patent No. 10,828,01 ... This application claims priority to U.S. Provisional Patent Application No. 61 / 877,617, entitled "Dipole Densities on a Cardiac Surface," each of which is incorporated herein by reference.

[0018] This application does not claim priority to, but may be related to, U.S. patent application Ser. No. 16 / 849,045, filed April 15, 2020, entitled "Localization System and Method Useful in the Acquisition and Analysis of Cardiac Information," which is a continuation of U.S. Patent No. 10,653,318, filed October 26, 2017, entitled "Localization System and Method Useful in the Acquisition and Analysis of Cardiac Information," which is a national stage application under 35 U.S.C. 371 of Patent Cooperation Treaty application PCT / US2016 / 032420, filed May 13, 2016, entitled "Localization System and Method Useful in the Acquisition and Analysis of Cardiac Information," which is a continuation of U.S. Patent No. 10,653,318 ...7, entitled "Localization System and Method Useful in the Acquisition and Analysis of Cardiac Information," which is a continuation of U.S. Patent No. 10,653,318, filed May 13, 2016, entitled "Localization System and Method Useful in the Acquisition and Analysis of Cardiac Information," which is a continuation of U.S. Patent No. 10,653,318, filed May 13, 2015, entitled "Localization System and Method Useful in the Acquisition and Analysis of Cardiac Information," which is a continuation of U.S. Patent No. 10,653,318, filed October 26, 2017, entitled "Localization System and Method Useful in the This application claims priority to U.S. Provisional Patent Application No. 62 / 161,213, entitled "Microdevice and Systemic Medical Information," each of which is incorporated herein by reference.

[0019] This application does not claim priority to, but may be related to, U.S. patent application Ser. No. 15 / 569,231, filed Oct. 25, 2017, entitled “Cardiac Virtualization Test Tank and Testing System and Method,” which is a national stage application under 35 U.S.C. § 371 of Patent Cooperation Treaty application PCT / US2016 / 031823, filed May 11, 2016, which claims priority to U.S. Provisional Patent Application Ser. No. 62 / 160,501, filed May 12, 2015, entitled “Cardiac Virtualization Test Tank and Testing System and Method,” each of which is incorporated herein by reference.

[0020] This application does not claim priority to, but may be related to, U.S. patent application Ser. No. 17 / 735,285, entitled "Ultrasound Sequencing System and Method," filed May 3, 2022, which is a continuation of U.S. patent application Ser. No. 15 / 569,185, entitled "Ultrasound Sequencing System and Method," filed October 25, 2017, which is a national stage application under 35 U.S.C. § 371 of Patent Cooperation Treaty application PCT / US2016 / 032017, filed May 12, 2016, which claims priority to U.S. Provisional Patent Application Ser. No. 62 / 160,529, entitled "Ultrasound Sequencing System and Method," filed May 12, 2015, each of which is incorporated herein by reference.

[0021] This application does not claim priority to, but may be related to, U.S. patent application Ser. No. 17 / 858,174, entitled "Cardiac Mapping System with Efficiency Algorithm," filed July 6, 2022, which is a continuation of U.S. patent application Ser. No. 16 / 097,959, entitled "Cardiac Mapping System with Efficiency Algorithm," filed October 31, 2018, which is a national stage application under 35 U.S.C. 371 of Patent Cooperation Treaty application PCT / US2017 / 030922, entitled "Cardiac Mapping System with Efficiency Algorithm," filed May 3, 2017, which is a continuation of U.S. patent application Ser. No. 16 / 097,959 ... October 26, 2016, which is a continuation of U.S. patent application Ser. No. 62 / 413,104, filed May 3, 2016, entitled "Cardiac Mapping System with Efficiency Algorithm," and U.S. Provisional Patent Application No. 62 / 331,364, filed May 3, 2016, entitled "Cardiac Mapping System with Efficiency Algorithm," each of which is incorporated herein by reference.

[0022] This application does not claim priority to, but may be related to, U.S. patent application Ser. No. 16 / 961,809, filed July 13, 2020, entitled "System for Identifying Cardiac Conduction Patterns," which is a national stage application under 35 U.S.C. 371 of Patent Cooperation Treaty application No. PCT / US2019 / 014498, filed January 22, 2019, entitled "System for Identifying Cardiac Conduction Patterns," which is a continuation of U.S. Provisional Patent Application No. 62 / 619,897, filed January 21, 2018, entitled "System for Recognizing Cardiac Conduction Patterns," and U.S. Provisional Patent Application No. 62 / 619,897, filed May 8, 2018, entitled "System for Identifying Cardiac Conduction Patterns," which is a continuation of U.S. Provisional Patent Application No. This application claims priority to U.S. Provisional Patent Application No. 62 / 668,647, entitled "Polymer Patterns," each of which is incorporated herein by reference.

[0023] This application does not claim priority to, but may be related to, U.S. patent application Ser. No. 17 / 048,151, entitled "Cardiac Information Processing System," filed on October 16, 2020, which is a national stage application under 35 U.S.C. § 371 of Patent Cooperation Treaty application PCT / US2019 / 031131, entitled "Cardiac Information Processing System," filed on May 7, 2019, which claims priority to U.S. Provisional Patent Application Ser. No. 62 / 668,659, entitled "Cardiac Information Processing System," filed on May 8, 2018, and U.S. Provisional Patent Application Ser. No. 62 / 811,735, entitled "Cardiac Information Processing System," filed on February 28, 2019, each of which is incorporated herein by reference.

[0024] This application does not claim priority to, but may be related to, Patent Cooperation Treaty Application No. PCT / US2019 / 060433, entitled "Systems and Methods for Calculating Patient Information," filed November 8, 2019, which claims priority to U.S. Provisional Patent Application No. 62 / 757,961, entitled "Systems and Methods for Calculating Patient Information," filed November 9, 2018, each of which is incorporated herein by reference.

[0025] This application does not claim priority to, but may be related to, U.S. patent application Ser. No. 17 / 601,661, entitled "System for Creating a Composite Map," filed on October 5, 2021, which is a national stage application under 35 U.S.C. § 371 of Patent Cooperation Treaty application PCT / US2020 / 028779, entitled "System for Creating a Composite Map," filed on April 17, 2020, which claims priority to U.S. Provisional Patent Application Ser. No. 62 / 835,538, entitled "System for Creating a Composite Map," filed on April 18, 2019, and U.S. Provisional Patent Application Ser. No. 62 / 925,030, entitled "System for Creating a Composite Map," filed on October 23, 2019, each of which is incorporated herein by reference.

[0026] This application does not claim priority to, but may be related to, U.S. patent application Ser. No. 17 / 613,249, entitled "Systems And Methods For Performing Localization Within A Body," filed November 22, 2021, which is a national stage application under 35 U.S.C. 371 of Patent Cooperation Treaty application PCT / US2020 / 036110, entitled "Systems and Methods for Performing Localization Within a Body," filed June 4, 2020, which claims priority to U.S. Provisional Patent Application Ser. No. 62 / 857,055, entitled "Systems and Methods for Performing Localization Within a Body," filed June 4, 2019, each of which is incorporated by reference into this application.

[0027] This application does not claim priority to, but may be related to, U.S. patent application Ser. No. 17 / 777,104, entitled "Tissue Treatment Systems, Devices, and Methods," filed May 16, 2022, which is a national stage application under 35 U.S.C. § 371 of Patent Cooperation Treaty application PCT / US2020 / 061458, entitled "Tissue Treatment Systems, Evices, and Methods," filed November 20, 2020, which claims priority to U.S. Provisional Patent Application Ser. No. 62 / 939,412, entitled "Tissue Treatment Systems, Devices, and Methods," filed November 22, 2019, and U.S. Provisional Patent Application Ser. No. 63 / 075,280, entitled "Tissue Treatment Systems, Devices, and Methods," filed September 7, 2020, each of which is incorporated by reference into this application.

[0028] Many medical procedures involve the delivery of energy for mapping or otherwise diagnosing tissue, or for ablatating or otherwise treating tissue. Achieving the desired specificity and efficacy in diagnosing and treating tissue can be difficult, and the inability to do so can result in less than desired results. [Prior art documents] [Patent documents]

[0029] [Patent Document 1] US Patent Application Publication No. 2019 / 0246930 Summary of the Invention [Problem to be solved by the invention]

[0030] What is needed are systems, methods, and devices that achieve improved tissue treatment through the delivery of energy. [Means for solving the problem]

[0031] In accordance with one aspect of the inventive concept, a cardiac information dynamic display system includes one or more electrodes and a cardiac information console. The one or more electrodes are configured to record sets of electrical potential data representative of cardiac activity at a plurality of time intervals. The cardiac information console includes a signal processor and a user interface module. The signal processor is configured to use the sets of recorded electrical potential data to calculate sets of cardiac activity data at the plurality of time intervals. The cardiac activity data is associated with surface locations of one or more heart chambers. The user interface module displays information related to the cardiac activity data. The information is presented with respect to a graphical representation of a surface of the one or more heart chambers.

[0032] In accordance with another aspect of the inventive concept, a method for dynamically displaying cardiac information includes recording sets of electrical potential data representative of cardiac activity at a plurality of time intervals using one or more electrodes. The method also includes using a cardiac information console comprising a signal processor and a user interface module to calculate sets of cardiac activity data at the plurality of time intervals using the recorded sets of electrical potential data, where the cardiac activity data is associated with surface locations of one or more heart chambers, and displaying information related to the cardiac activity data, where the information is presented with respect to a graphical representation of the surface of the one or more heart chambers.

[0033] The technology described herein, together with its attributes and attendant advantages, will best be appreciated and understood in consideration of the following detailed description taken in conjunction with the accompanying drawings in which exemplary embodiments are set forth by way of example.

[0034] Incorporation by Reference All publications, patents, and patent applications mentioned herein are incorporated by reference into this application to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. The contents of all publications, patents, and patent applications mentioned herein are incorporated by reference in their entirety into this application for all purposes. [Brief description of the drawings]

[0035] [Figure 1] 1 is a schematic diagram of one embodiment of a system configured to enable the performance of a medical procedure on a patient consistent with the concepts of the present invention. [Diagram 2] FIG. 1 illustrates an anatomical model representing tissue surfaces consistent with the concepts of the present invention. [Diagram 3] FIG. 1 illustrates an example of a graphical user interface for displaying cardiac mapping data consistent with the concepts of the present invention. [Figure 4A]1A-1C show an example of a graphical user interface including an anatomical model and an anatomical model including various markers, respectively, consistent with the concepts of the present invention. [Figure 4B] 1A-1C show an example of a graphical user interface including an anatomical model and an anatomical model including various markers, respectively, consistent with the concepts of the present invention. [Figure 5A] 1A-1C are a flow diagram illustrating one embodiment of a machine learning or other artificial intelligence based method of rhythm classification and a representation of cardiac activity data, respectively, consistent with the concepts of the present invention. [Figure 5B] 1A-1C are a flow diagram illustrating one embodiment of a machine learning or other artificial intelligence based method of rhythm classification and a representation of cardiac activity data, respectively, consistent with the concepts of the present invention. [Figure 6] 1A-1C illustrate various embodiments of an anatomical model on which a cardiac activity map is displayed, consistent with the concepts of the present invention. [Figure 7] 1A-1C illustrate various embodiments of an anatomical model on which a map of cardiac activity is displayed, consistent with the concepts of the present invention. [Figure 8A] FIG. 1 illustrates one embodiment of a graph of a spatiotemporal representation of cardiac activity consistent with the concepts of the present invention. [Figure 8B] FIG. 1 illustrates one embodiment of a color-coded graph of cardiac activity consistent with the concepts of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0036] 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, this specification is not intended to limit the disclosure to the particular embodiments, but should be construed as including various modifications, equivalents, and / or alternatives to the embodiments described herein.

[0037] As used herein, 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") will be understood to 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 thereof.

[0038] In this specification, terms such as first, second, third, etc. may be used to describe various limitations, elements, components, regions, layers, and / or sections, but it will be further understood that these limitations, elements, components, regions, layers, and / or sections should not 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, without departing from the teachings of this application, a first limitation, element, component, region, layer, or section described below may be referred to as a second limitation, element, component, region, layer, or section.

[0039] It will be further understood that when an element is referred to as being "on" or "attached," "connected," or "coupled" to another element, the element may be directly on or above the other element, or may be connected or coupled to the other element, or there may 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 similarly (e.g., "directly between" as opposed to "between," "directly adjacent" as opposed to "adjacent," etc.).

[0040] It will be further understood that when a first element is referred to as being "in," "on," and / or "inside" a second element, the first element can be positioned within an 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 and / or interior surface of the second element, and a combination of one or more of these.

[0041] 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 in, on, and / or within the second component or location. For example, a component positioned proximate to an anatomical site (e.g., the location of a target tissue) is intended to include a component positioned near the anatomical site, as well as a component positioned in, on, and / or within the anatomical site.

[0042] Spatially relative terms such as "below," "below," "lower," "above," and the like may be used to describe the relationship of one element and / or feature to another element and / or feature, for example, as shown in the figures. It will be further understood that the spatially relative terms are intended to encompass different orientations of the device during use and / or operation in addition to the orientation shown in the figures. For example, if the device in the figures were flipped over, elements described as "below" and / or "below" the other element or feature would then be oriented "above" the other element or feature. The device may be otherwise oriented (e.g., rotated 90 degrees or oriented in other directions) and the spatially relative descriptors used herein may be interpreted accordingly.

[0043] The terms "reduce", "reducing", "reduction" and the like, as used herein, are intended to include a reduction in an 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 "reducing", "reducing", and "reducing", respectively.

[0044] The term "and / or" as used herein should be considered to specifically disclose each of the two specified features or components with or without the other. For example, "A and / or B" should be considered to specifically disclose each of (i) A, (ii) B, and (iii) A and B, as if each were individually set forth herein.

[0045] 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.

[0046] As used herein, the terms "and combinations thereof" and "and combinations thereof" may be used following a list of items that are to be included, either singly or collectively. For example, components, processes, and / or other items selected from the group consisting of A, B, C, and combinations thereof, are intended to include a set of one or more components that include one, two, three or more items A, one, two, three or more items B, and / or one, two, three or more items C.

[0047] In this specification, "and" can mean "or," and vice versa, unless expressly stated otherwise. 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.

[0048] 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 values ​​greater than 1 and less than 10.

[0049] As used in this disclosure, the phrase "configured (or configured) for" may be used synonymously with, for example, "suitable for," "capable of," "designed for," "adapted for," "made for," and "capable of," depending on the context. The phrase "configured for" does not mean only "specially designed for" in hardware. Alternatively, depending on the context, the phrase "device configured for" may mean that the device is "capable of" operating in conjunction with another device or component.

[0050] 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 undesired 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, such as to cause a desired effect (e.g., effective treatment) and / or to 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 a safety margin to account for patient variability, system variability, tolerances, and the like. As used herein, "above a threshold" relates to a parameter being above a maximum threshold, below a minimum threshold, within a threshold range, and / or outside a threshold range.

[0051] As used herein, "room pressure" is intended to mean the pressure of the environment surrounding the systems and devices of the inventive concept. Positive pressure includes pressure above room pressure, or simply a pressure greater than another pressure, such as a positive pressure differential across a fluid path component such as a valve. Negative pressure includes pressure below room pressure, or a pressure less than another pressure, such as a negative pressure differential across a fluid component path such as a valve. Negative pressure can include a vacuum, but does not mean a pressure below a vacuum. As used herein, the term "vacuum" can be used to refer to a full vacuum or a partial vacuum, or any negative pressure as described above.

[0052] The term "diameter" when used herein to describe a non-circular shape shall be considered as the diameter of an imaginary circle that approximates the shape being described. For example, when describing a cross-section, such as a cross-section of a component, the term "diameter" shall be interpreted as representing the diameter of an imaginary circle having the same cross-sectional area as the cross-section of the component being described.

[0053] As used herein, the terms "major axis" and "minor axis" of a component are the length and diameter, respectively, of an imaginary cylinder of smallest volume that can completely enclose the component.

[0054] 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 may 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 treatment 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 a patient physiological parameter, a patient anatomical parameter (e.g., a tissue shape parameter), a patient environmental parameter, and / or a system parameter. In some embodiments, a sensor or other functional element is configured to perform a diagnostic function (e.g., to collect data used to perform a diagnosis). In some embodiments, a functional element is configured to perform a therapeutic function (e.g., to deliver a therapeutic energy and / or a therapeutic agent). In some embodiments, the functional elements include 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 a combination of one or more of the foregoing. The functional elements may include a fluid and / or a fluid delivery system. The functional elements may include a reservoir, such as an expandable balloon or other fluid-holding 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 expandable assembly. The functional assembly may include one or more functional elements.

[0055] The term "transducer" as used herein should be construed to include any component or combination of components that receives energy or any input and generates 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 any output, the output being, for example, 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 ultrasonic energy), pressure (e.g., an applied pressure or force), thermal energy, cryogenic energy, chemical energy, mechanical energy (e.g., a transducer including a motor or a solenoid), magnetic energy, and / or a different (e.g., different from the input signal to the transducer) electrical signal. Alternatively or additionally, a transducer can convert a physical quantity (e.g., a variation in a physical quantity) into an electrical signal. The transducer can include any component that delivers energy and / or agents to tissue, such as a transducer configured to deliver electrical energy to tissue (e.g., a transducer including one or more electrodes), 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), mechanical energy to tissue (e.g., a transducer including an element that manipulates tissue), acoustic energy to tissue (e.g., a transducer including a piezoelectric crystal), chemical energy, electromagnetic energy, magnetic energy, and one or more combinations thereof.

[0056] As used herein, the term "fluid" can refer to a liquid, gas, gel, or any flowable material, for example, a material that can be forced into a cavity and / or orifice.

[0057] As used herein, the term "material" can refer to a single material or a combination of two, three, four or more materials.

[0058] It will be appreciated that certain features of the inventive concepts that are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, for brevity, various features of the inventive concepts that are 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 recited in any of the claims (whether independent or dependent) may be combined in any desired manner.

[0059] It should be understood that at least some of the diagrams and descriptions of the inventive concepts have been simplified to focus on elements relevant to a clear understanding of the inventive concepts, but for clarity have excluded other elements that one of ordinary skill in the art would understand may also form part of the inventive concepts, however, because such elements are well known in the art and because such elements do not necessarily facilitate a better understanding of the inventive concepts, a description of such elements is not provided herein.

[0060] The terms defined in this disclosure are used only to describe certain embodiments of the disclosure and are not intended to limit the scope of the disclosure. Terms provided in the singular form are intended to include the plural form unless otherwise clearly indicated in the context. All terms used herein, including technical or scientific terms, have the same meaning as commonly understood by those skilled in the relevant art, unless otherwise defined herein. Terms defined in commonly used dictionaries should be interpreted as having the same or similar meaning in the context of the relevant art, and should not be interpreted as having an ideal or exaggerated meaning unless explicitly defined herein. In some cases, the terms defined in this disclosure should not be interpreted to exclude embodiments of the disclosure.

[0061] Provided herein are systems, devices, and methods for treating a target tissue of a patient to provide a therapeutic benefit to the patient. An energy delivery console can be configured to deliver various "doses" of energy delivered by one or more energy delivery devices to cauterize, necrotize, and / or otherwise therapeutically alter the target tissue. The one or more energy delivery devices can include catheters and / or surgical instruments including electrodes and / or other energy delivery elements. In some embodiments, multiple interdependent energy doses are delivered to a common tissue location, such as to provide an improved therapeutic benefit to the patient. An initial dose can be configured to warm the tissue, such as delivery of radio frequency (RF), heat, and / or other energy. Subsequent doses can include doses of energy configured to irreversibly electroporate the pre-warmed tissue, such as while the tissue is in a hyperthermic (e.g., above body temperature) state.

[0062] Referring now to FIG. 1, there is shown a schematic diagram of one embodiment of a system configured to enable the performance of a medical procedure on a patient (e.g., a human or other living mammal) consistent with the concepts of the present invention. The medical procedure may include a diagnostic procedure, a therapeutic procedure, or a combined diagnostic and therapeutic procedure that may be performed by a clinician and / or other user (herein an "operator" or "user"). The system 10 includes a console 100 that includes one or more separate assemblies (e.g., individual boxes) that connect to various components of the system to provide energy to the system 10, record information from the system 10, and / or otherwise enable one or more functions of the system 10 as described herein. The system 10 also includes one or more diagnostic catheters, namely, the illustrated mapping catheter 200. In some embodiments, system 10 includes one or more treatment catheters, i.e., treatment catheter 310, one or more functional catheters, i.e., functional catheters 320, one or more additional diagnostic catheters, i.e., diagnostic catheters 330, one or more patient patches, i.e., patches 340, one or more patient leads, i.e., EKG leads 350, and / or one or more delivery devices, i.e., sheaths 360. Console 100 is operably attached (e.g., electrically, mechanically, fluidically, acoustically, and / or optically attached) to one or more catheters or other devices 200, 310, 320, 330, 340, 350, and / or 360.

[0063] The mapping catheter 200 can include an array of elements, i.e., array 210, a handle 202, and an elongated filament, i.e., shaft 201 therebetween. The array 210 can include a radially expandable array, such as an array that is resiliently biased in a radially expanded configuration. In some embodiments, the array 210 can include a plurality of radially expandable arms, i.e., splines 213. Alternatively or additionally, the array 210 can include a balloon, a radially expandable cage, or other expandable structure. The array 210 can include one or more functional elements, such as one or more electrodes, i.e., electrodes 211, one or more ultrasound transducers, i.e., USTs 212, and / or one or more other functional elements, i.e., functional elements 219.

[0064] The mapping catheter 200 may be constructed and arranged similarly to similar components described in the applicant's co-pending U.S. patent application Ser. No. 16 / 861,814, entitled "Catheter, System and Methods of Medical Uses of Same, including Diagnostic and Treatment Uses for the Heart," filed April 29, 2020, U.S. patent application Ser. No. 16 / 242,810, entitled "Expandable Catheter Assembly with Flexible Printed Circuit Board (PCB) Electrical Pathways," filed January 8, 2019, and U.S. patent application Ser. No. 17 / 735,285, entitled "Cardiac Virtualization Test Tank and Testing System and Method," filed May 3, 2022.

[0065] The treatment catheter 310 can include an elongated filament, or shaft 311, having a handle 312 at a proximal end. The treatment catheter 310 can include one or more functional elements, or functional elements 319, positioned at a distal portion of the shaft 311 (e.g., at least one functional element 319 positioned at a distal end of the shaft 311). In some embodiments, the functional element 319 includes one or more electrodes configured to deliver electrical energy (e.g., RF energy) to tissue to thermally ablate the tissue. Additionally or alternatively, the functional element 319 can include one or more electrodes configured to generate an electric field therebetween, e.g., to electroporate tissue within the electric field (e.g., to irreversibly electroporate tissue).

[0066] The treatment catheter 310 may be of similar construction and arrangement to similar components described in the applicant's co-pending U.S. application Ser. No. 16 / 335,893, entitled "Ablation System with Force Control," filed March 22, 2019, U.S. application Ser. No. 17 / 777,104, entitled "Tissue Treatment Systems, Devices, and Methods," filed May 16, 2022, and Patent Cooperation Treaty application PCT / US2022 / 016722, entitled "Energy Delivery Systems with Ablation Index," filed February 17, 2022.

[0067] The functional catheter 320 can include an elongated filament, i.e., shaft 321, having a handle 322 at a proximal end. The functional catheter 320 can include one or more functional elements, i.e., functional elements 329, positioned at a distal portion of the shaft 321 (e.g., an array of at least 10 functional elements 329 positioned at a distal portion of the shaft 321, such as 13 elements as shown). In some embodiments, the functional elements 329 include one or more electrodes, such as one or more electrodes configured to record biopotential and / or other electrical signals from cardiac tissue.

[0068] Diagnostic catheter 330 can include a catheter that includes one or more functional elements, i.e., functional element 339. In some embodiments, diagnostic catheter 330 includes a coronary sinus (CS) mapping catheter that is constructed and arranged for positioning within the CS of the heart (e.g., to position functional element 339 within the CS). Functional element 339 can include one or more electrodes configured to record biopotential and / or other electrical signals from cardiac tissue.

[0069] The patch 340 may include one or more patches configured to be applied onto the patient's skin (e.g., onto the patient's torso). The patch 340 may include one or more functional elements, i.e., functional elements 349. The functional elements 349 may include electrodes configured to generate an electric field within the patient (e.g., an electric field generated between at least two patches 340). The system 10 may be configured to localize its one or more devices within and / or on the patient by measuring the electric field generated between the patches 340 (e.g., via impedance-based localization as described herein).

[0070] The EKG leads 350 may include one or more patient patches configured to record electrical signals (e.g., cardiac electrical signals) from the patient. The multiple EKG leads 350 may be positioned around the patient's torso as shown.

[0071] The sheath 360 may comprise an elongated tube or shaft 361 including at least one lumen or lumen 363 therethrough. The sheath 360 may include at least one functional element or functional element 369 as shown. The sheath 360 may be constructed and arranged to be advanced intravascularly into a cavity of the heart (e.g., into the left atrium of the heart via a transseptal puncture). The lumen 363 of the sheath 360 may slidably receive one or more devices of the system 10, e.g., a distal portion of the mapping catheter 200 (e.g., when the array 210 is in a radially collapsed configuration), such that the device may be advanced into the cavity of the heart from a distal end of the lumen 363. For example, the array 210 of the mapping catheter 200 may be advanced through the lumen 363 of the sheath 360 in a radially collapsed configuration, exit the lumen 363 into the left atrium of the heart, and transition to a radially expanded configuration. In some embodiments, the lumen 363 includes two or more lumens, each configured to slidably receive a device of the system 10, and / or the lumen 363 is constructed and arranged to simultaneously receive multiple devices (e.g., the mapping catheter 200, the treatment catheter 310, and / or the functional catheter 320) to enable multiple devices to be inserted into the left atrium through a single transseptal puncture.

[0072] As used herein, devices 310 , 320 , 330 , 340 , 350 , and / or 360 may be referred to singly or collectively as patient devices 300 .

[0073] The console 100 can include a patient interface module 101 configured to operably attach one or more patient devices (e.g., one or more catheters or other devices described herein) to one or more components of the console 100. The patient interface module 101 can include circuitry configured to protect the patient from unwanted electrical shocks, such as those caused by the console 100, and / or to protect components of the console 100 from electrical shocks, such as those caused by a defibrillation pulse or other energy delivered to the patient.

[0074] The console 100 may include a processing unit 110. The processing unit 110 may include at least one microprocessor, computer, and / or another electronic controller, i.e., processor 111. The processing unit 110 may also include one, two, or more algorithms, i.e., algorithm 115 as shown. The processing unit 110 may include a memory 112 for storing instructions for executing the algorithm 115. The processor 111, via the algorithm 115, may perform one or more of the processes described herein, such as processes performed in response to one or more commands entered by a user into the system 10 (e.g., via a user interface 120 as described herein). The processing unit 110 may receive signals, such as signals from one, two, or more functional elements of the devices 200 and / or 300 (e.g., signals from one, two, or more sensor-based functional elements of these devices). The processing unit 110 may be configured to perform one or more mathematical operations based on the received signals to generate a result that correlates to a physiological parameter of the patient and / or an operational parameter for at least one device of the system 10.

[0075] The console 100 may include an interface, or user interface 120, for providing information to and / or receiving information from a user of the system 10. The user interface 120 may include one, two, or more user input and / or user output components. For example, the user interface 120 may include a joystick, a keyboard, a mouse, a microphone, a touch screen, and / or other input devices. Additionally or alternatively, the user interface 120 may include a speaker, a haptic feedback device, indicator lights, and / or other output devices. In some embodiments, the user interface 120 includes one or more displays, such as a touch screen or other display, for providing graphical visual information to the user. The processing unit 110 may provide a graphical user interface, or GUI 125, that is presented to the user via the user interface 120.

[0076] System 10 may include one or more modules that generate output signals (e.g., signals to deliver to devices 200 and / or 300), receive data (e.g., one or more recorded signals from devices 200 and / or 300), process the received data (e.g., via algorithm 115), and / or generate output data based at least in part on the processed data. For example, system 10 may include a biopotential module 130, a localization module 140, an anatomy module 150, an imaging module 160, a mapping module 170, and / or a treatment module 180.

[0077] The biopotential module 130 can generate one or more outputs related to the patient's electrical activity, for example, dipole density information, surface charge information, and / or voltage information related to the patient's cardiac activity. The biopotential module 130 may be constructed and arranged similarly to similar components described in the applicant's U.S. Patent No. 11,013,444, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls," filed August 6, 2019, U.S. Patent No. 11,116,438, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," filed September 12, 2019, U.S. Design Patent No. D954,970, entitled "Set of Transducer-Electrode Pairs for a Catheter," filed February 28, 2019, and co-pending U.S. patent application Ser. No. 16 / 097,959, entitled "Cardiac Mapping System with Efficiency Algorithm," filed October 31, 2018.

[0078] The localization module 140 may generate one or more outputs related to the location of one or more components of the system 10 relative to the patient P, such as with respect to a coordinate system established by the localization module 140. The localization module 140 may be constructed and arranged similarly to similar components described in Applicant's co-pending U.S. patent application Ser. No. 16 / 849,045, filed April 15, 2020, and entitled "Localization System and Method Useful in the Acquisition and Analysis of Cardiac Information."

[0079] The anatomy module 150 may generate one or more outputs related to the anatomy of the patient P, such as the size, shape, and / or structure of at least a portion (e.g., a cavity) of the patient P's heart H. The anatomy module 150 may be constructed and arranged similarly to similar components described in Applicant's U.S. Design Patent No. D954970, entitled "Set of Transducer-Electrode Pairs for a Catheter," filed on February 28, 2019, and co-pending U.S. patent application Ser. No. 17 / 735,285, entitled "Cardiac Virtualization Test Tank and Testing System and Method," filed on May 3, 2022.

[0080] The imaging module 160 can provide, generate, acquire, update, store, and maintain at least one image of the heart or at least one chamber of the heart H. The imaging module 160 can include at least one imaging device configured to record image data. For example, the imaging module 160 can include an imaging device selected from the group consisting of a computed tomography (CT) scanner, a fluoroscope, an X-ray imager, an MRI scanner, an ultrasound imager, and combinations thereof. In some embodiments, the imaging module 160 receives and / or stores image information from an imaging device. Alternatively or additionally, the imaging module 160 can be configured to receive image data from an imaging device separate from the system 10. In some embodiments, the imaging module 160 and the anatomy module 150 are configured to provide, generate, and / or update an anatomical model, such as an anatomical model of at least a portion of the patient's heart, based on image data from the imaging device.

[0081] The mapping module 170 can receive cardiac activity information (e.g., information recorded from devices 200 and / or 300 by biopotential module 130) and generate one or more maps of cardiac electrical activity. For example, mapping module 170 can generate one or more dipole density, surface charge, and / or voltage maps based on the recorded cardiac electrical activity. Mapping module 170 can be of similar construction and arrangement to similar components described in applicant's co-pending U.S. patent application Ser. No. 17 / 673,995, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," filed Feb. 17, 2022, and U.S. application Ser. No. 16 / 097,955, entitled "Cardiac Information Dynamic Display System and Method," filed Oct. 31, 2018.

[0082] The treatment module 180 can be configured to cause or drive devices of the system 10 (e.g., treatment catheter 310) to deliver treatment energy to one or more locations of the patient's heart. In some embodiments, the treatment module 180 provides closed-loop energy delivery based on mapping and / or other information generated by the system 10. The treatment module 180 can be constructed and arranged similarly to similar components described in Applicant's co-pending U.S. patent application Ser. No. 16 / 861,814, entitled "Catheter, System and Methods of Medical Uses of Same, including Diagnostic and Treatment Uses for the Heart," filed April 29, 2020, U.S. patent application Ser. No. 16 / 335,893, entitled "Ablation System with Force Control," filed March 22, 2019, and U.S. patent application Ser. No. 17 / 777,104, entitled "Tissue Treatment Systems, Devices, and Methods," filed May 16, 2022.

[0083] In some embodiments, system 10 is configured to generate (e.g., via mapping module 170) one or more maps of cardiac activity based on non-contact collected information (e.g., information recorded from one or more electrodes positioned within a heart chamber without contacting the heart wall, herein "non-contact data"). Alternatively or additionally, system 10 may be configured to generate one or more maps of cardiac activity based on contact collected information (e.g., information recorded from one or more electrodes positioned in contact with the heart wall, herein "contact data"). In some embodiments, system 10 is configured to generate one or more "hybrid" maps of cardiac activity based on both contact and non-contact data.

[0084] The localization module 140 can provide signals to and / or record signals from one or more devices of the system 10. For example, the localization module 140 can provide one or more signals to the patch 340 to establish an electric field within the patient's body. The localization module 140 can record signals from one or more devices of the system 10 associated with the established electric field to determine a position and / or orientation of the device within the electric field ("locate" the device) via an impedance-based method. Alternatively or additionally, the localization module 140 can locate one or more devices of the system 10 via a magnetic-based method, such as when the localization module 140 is constructed and arranged to establish a magnetic field within at least a portion of the patient (e.g., via one or more permanent magnets and / or electromagnets) and one or more functional elements of the system 10 include a magnetic coil or other element configured to detect the magnetic field.

[0085] In some embodiments, the localization module 140 is configured to locate one or more devices of the system 10 that are positioned in a stationary position where expected further intentional movement (e.g., by an operator) is minimal, e.g., the diagnostic catheter 330 when positioned within the CS. The localization module 140 can be configured to track the location of this static device and utilize this device as a "physical fiducial" (e.g., a locatable physical point within the patient's body that is unlikely to move relative to the heart, as described below). In some embodiments, the localization module 140 can locate one or more other devices of the system 10 (e.g., any or all other devices present within the patient's body, such as in or proximate to the heart) based, at least in part, on the relative position of the physical fiducial to the additional devices whose locations have been determined.

[0086] The system 10 can be configured to navigate (e.g., provide navigation information and / or automatically navigate) one or more catheters and / or other devices of the system 10. The system 10 can perform navigation of the device using impedance measurements recorded by the system 10 (e.g., by the localization module 140) as described herein. The system 10 can perform impedance measurements in many different ways. In some embodiments, the system 10 uses a set of multiple (e.g., at least three) pairs of patches (e.g., three pairs of patches 340) to deliver current through the body. The three patch pairs can deliver unique frequencies (e.g., simultaneously). Sensors (e.g., electrodes) within the body can measure electrical potentials at each of the three unique localization frequencies relative to a reference measurement measured elsewhere in the body (e.g., a patch on the body surface low on the torso).

[0087] The system 10 can be configured to perform impedance-based device navigation while reducing the effects of motion artifacts and / or other artifacts, such as through the use of “adaptive referencing.” In some embodiments, the system 10 tracks a set of designated electrodes (e.g., from one or more catheters and / or other devices of the system 10) that are likely (e.g., highly likely) to be placed in a “rest position,” such as when positioned in a position where further expected movement is minimal (e.g., where further manipulation by the operator is minimal, such as when placed in the coronary sinus or other intracardiac location where no further manipulation is required and / or desired). The system 10 can be configured to track the positions of those electrodes and use them as “physical references.” For example, the system 10 can track the other device (e.g., a catheter being manipulated) by using the physical reference as an “anchor,” such as by subtracting the real-time position of the physical reference. The system 10 can be configured to compensate for physical displacement of any electrodes used as a physical reference, where such displacement causes a shift in the coordinate system. In some embodiments, system 10 uses a dynamic adaptive learning model to train a set of measurements from body surface sensors (e.g., patches 340) to create a set of virtual intracardiac measurements that quantitatively reconstruct signals equivalent to physical devices (herein "virtual references"). For example, system 10 can track other devices (e.g., catheters) by using the virtual references as anchors, such as by subtracting the real-time position of the virtual references.These virtual references of the system 10 are not susceptible to the type of physical motion that may occur when using a device inserted into a patient's body (e.g., an intracardiac catheter as described above), but are susceptible to disturbances associated with the body surface sensor itself, disturbances associated with the localization metric (e.g., reference patch electrodes), and / or electrical disturbances introduced (e.g., associated) with a single device (e.g., a single catheter), multiple devices (e.g., multiple catheters), and / or the entire system. In some embodiments, the system 10 can be configured to store (e.g., in a memory) a time history of all tracked positions (e.g., device positions, physical reference positions, and / or virtual reference positions) to recall (e.g., further use) a previous position of any device (e.g., used in localization and / or localization compensation of one or more devices of the system 10). This stored information can be useful in detecting the difference between system-wide disturbances and disturbances specific to a single device (e.g., a single catheter). For example, electrical disturbances may affect a single device, multiple devices, and / or the entire system, with device motion being unique to each individual device. Each of these above methods can be computed simultaneously and / or sequentially.

[0088] In some embodiments, system 10 is configured to perform impedance-based device (e.g., catheter) navigation methods using a “position agreement algorithm” (e.g., one of algorithms 115) configured to account for motion artifacts and / or other measurement artifacts. If system 10 utilizes more than one device tracking method (e.g., two or more of those described above), system 10 can implement a position agreement algorithm that cross-compares solutions from each of the methods to determine one or more subsets of solutions that “match.” Alternatively or additionally, the position agreement algorithm of system 10 can determine one or more subsets of solutions that are indicative of deviations. The position agreement algorithm can bias system 10 to prioritize one or more of these methods and / or to deprioritize (e.g., ignore) one or more of these methods based on determining the match and / or finding indications of deviations. In some embodiments, the priority raising and / or deprioritization can be performed by assigning a weighting factor (e.g., a quantitative weighting factor) to each of these location methods. In some embodiments, the position alignment algorithm can specify when a deprioritized method (e.g., a method not currently being used) should be reinstated (e.g., used again). The position alignment algorithm can use a predefined set of logic rules to determine the likely root cause of the disturbance and, for example, make appropriate adjustments to mitigate the disturbance. If the physical reference, virtual reference, and time history of the location all fall within a specified distance (e.g., a geofence), the system 10 can determine that all tracking methods are aligned. However, if the physical and virtual references are aligned but the most recent stored positions in the time history are not aligned, it is likely that a sudden electrical disturbance has affected the position of at least the specified reference device.Comparing the positions of other devices (e.g., catheters) within the body with their time history positions can help distinguish between catheter-specific disturbances and system-wide disturbances. By correcting the tracked positions of the physical and virtual fiducials to match the most recent valid time history positions, the overall quantitative effect of disturbances on position can be mitigated. For example, if the physical fiducial and the time history of the physical fiducial align but the virtual fiducial does not, the virtual fiducial may be deviating and can be ignored by the system 10 (e.g., ignored until it returns to alignment with the physical fiducial and the time history of the physical fiducial, and then can be returned).

[0089] In some embodiments, system 10 is configured to perform an impedance-based device (e.g., catheter) navigation method using a “location determination algorithm” (e.g., one of algorithms 115), an algorithm configured to utilize, for example, the results of the tracking and position matching algorithms described above, to determine (e.g., and further display) the location of one or more (e.g., all) of the devices of system 10 being used (e.g., the location of one or more electrodes of one or more catheters and / or other devices positioned in and / or on the patient). In some embodiments, the location determination algorithm is configured to generate a “probability score” that includes a confidence and / or other probability measure (e.g., quantitative output) regarding the determined location of one or more devices. The probability score may be displayed to the user via a displayed alphanumeric value and / or by modifying (e.g., enhancing) the manner in which the displayed device is presented (e.g., hash marks, color changes, brightness changes, and / or other visual distinctions of the body and / or other parts of the associated catheter and / or other device). In some embodiments, the system 10 includes various thresholds (eg, quantitative thresholds) that are used by the algorithm 115 to classify the predicted location of the device by comparing the probability score to one or more thresholds.

[0090] In some embodiments, system 10 includes a "data quality routine" configured to evaluate the quality of data from one or more connected measurement devices of system 10. The data quality routine can be configured to determine the "status" of a device of system 10, such as determining whether the device is electrically disconnected, removed from the body, not deployed (e.g., not fully deployed), and / or has a degraded sensor. In some embodiments, the data quality routine of system 10 (e.g., as implemented by algorithm 115) determines the status of the device by comparing measured and / or calculated parameters associated with the status (e.g., as determined by sensors of system 10) to system 10 threshold values ​​associated with the status.

[0091] In some embodiments, the system 10 (e.g., algorithm 115) is configured to perform "dynamically constrained adaptive impedance tracking" with monitored localization criteria, such as using one or more of the methods described above (e.g., using one or more electrodes to calculate a virtual reference). In some embodiments, the system 10 uses one or more sensors (e.g., electrodes) that are positioned within the body and designated as monitored localization criteria. A physical device may be expected to be left in a stationary position for a period of time (e.g., a period of time that exceeds a minimum time). The system 10 may monitor the device to determine whether the device has been physically displaced and / or whether an electrical disturbance has affected the device (e.g., the device alone), one or more other devices (e.g., multiple devices), or the system as a whole. The system 10 may be configured to determine whether any additional devices other than the monitored localization criteria are positioned within the body (e.g., currently located). If more than one device is available within the body, the system 10 may determine whether any of the devices have been moved by user manipulation and / or are independently affected by an electrical disturbance. The system 10 can be configured to track the historical location of the monitored localization reference (e.g., further devices), such as when no electrical disturbances are occurring. The system 10 can create a set of virtual measurements (e.g., virtual intracardiac measurements) that quantitatively reconstruct a signal equivalent to the monitored localization reference. The system 10 can use a full set of body surface measurements (e.g., the functional elements of the system 10 with a 12-lead ECG) to construct this virtual reference. The system 10 can (e.g., alternatively) construct the virtual reference using a set of body surface pairs (less than the full set), where the set of body surface pairs is selected to most closely (quantitatively) constrain the position data of the monitored localization reference device. This approach can be more accurate and resilient to large disturbances to the catheter's position.System 10 can track the monitored localization reference devices, the virtual references, and / or any other devices that are positioned (e.g., at least partially positioned) in a coordinate system used by system 10. System 10 can adaptively mitigate physical and electrical disturbances. For example, if a monitored localization reference device is physically displaced, the virtual reference can be recalculated to match, and the time history data can receive a valid updated position of the monitored localization reference device. Alternatively or additionally, if an electrical disturbance affects the tracked position of the virtual reference and / or the monitored localization reference, the virtual reference can be recalculated (e.g., by algorithm 115) and the tracked position of the monitored localization reference can be computationally re-matched to the last known good position in the time history, thereby mitigating position displacement caused by the disturbance.

[0092] In some embodiments, the system 10 is configured to perform an impedance-based device (e.g., catheter) navigation method in which compensation is performed to account for the patient's respiration. As described herein, the system 10 can create a "physical respiration baseline" using the device's electrodes in a stationary position. The system 10 can be configured to filter associated device motion to a range of frequencies related to respiration, such as frequencies below 1.0 Hz, 0.5 Hz, and / or 0.3 Hz. The system 10 can use this measured respiration signal to compensate for respiratory motion, such as by subtracting it from the raw localization signal at any electrode. Alternatively or additionally, the system 10 can use one or both of a "gating method" and / or a "dynamic learning model" to account for the patient's respiration. For example, the system 10 can use a gating method that uses measurements from the body surface, measurements from within the patient's body, or both to establish a consistent duration of the respiratory cycle. Gating methods can simplify data collection by using only data acquired during a gated range of the respiratory cycle, such as for discrete acquisitions similar to collecting contact mapping points (e.g., anatomical "shells" and / or EGMs), and / or by performing a sample-and-hold of device position from one gated period to the next for time-continuous measurements (e.g., employing interpolation between gated positions). System 10 can use a dynamic learning model (e.g., an adaptive model) to train a set of body surface measurements (e.g., via patch 340) and create a set of virtual internal (e.g., intracardiac) respiratory measurements that preserve the respiratory component of the motion (e.g., using filtering as a technique to preserve only the respiratory component). System 10 can use this measured respiratory signal to compensate for respiratory motion, such as by subtracting it from the raw localization signal at any electrode. System 10 can be configured to perform two or more of the above respiratory compensation methods simultaneously and / or sequentially.In some embodiments, system 10 implements multiple respiratory compensation methods (e.g., as described herein) and each method is assigned a weighting factor that is used by system 10 to prioritize one method over another and / or apply different levels of importance and / or influence to other methods ("prioritize" herein).

[0093] In some embodiments, system 10 is configured to track, monitor, analyze, and / or compensate for a patient's breathing using a "respiratory tracking matching algorithm" (e.g., one of algorithms 115). When two or more respiratory compensation methods described above are employed by system 10, system 10 may use a respiratory tracking matching algorithm to compare the solutions of each method to one another to determine one or more subsets of solutions that are consistent and / or to determine one or more subsets of solutions that exhibit deviations. This respiratory tracking matching algorithm may specify which methods to temporarily "ignore" and determine when the "ignored" methods should be included again, as described herein.

[0094] In some embodiments, the system 10 is configured to compensate for the patient's breathing using a "respiratory compensation algorithm" (e.g., one of the algorithms 115), an algorithm configured to determine (e.g., and display) the respiratory compensated positions of one or more (e.g., all) of the devices of the system 10 being used (e.g., the positions of one or more electrodes of one or more catheters and / or other devices positioned in and / or on the patient), e.g., using the results of the respiratory compensation method and the respiratory tracking matching algorithm described above. The system 10 can perform the compensation using a direct subtraction of the tracked respiratory motion from the positions of all relevant devices (e.g., electrodes). The system 10 can perform the compensation by tracking the respiratory motion differently at different positions of the anatomy and by subtracting the local respiratory motion from the positions of the displayed devices (e.g., electrodes) at the relevant positions. The different anatomical positions can be organized on a regular spatial grid (e.g., using voxels). In some embodiments, the respiratory compensation algorithm is configured to generate a “probability score” that includes a confidence and / or other probability measure (e.g., a quantitative score) regarding the compensation applied in determining a determined location of one or more devices (e.g., a location determined using respiratory compensation). For example, the probability score can be based on the probability that a disturbance will occur and / or on the extent of residual respiratory motion that remains uncompensated. The probability score can be displayed to the user via a displayed alphanumeric value and / or by altering (e.g., enhancing) the way in which the displayed device is presented (e.g., hash marks, color changes, brightness changes, and / or other visual distinctions of the body and / or other portions of the associated catheter and / or other device). In some embodiments, the respiratory compensation algorithm of system 10 (e.g., as implemented by algorithm 115) compares the probability score to a system 10 threshold associated with the respiratory compensation algorithm (e.g., to classify the compensation and / or to alter the way in which the displayed device is presented).

[0095] In some embodiments, the system 10 is configured to compensate for the patient's respiration using an "Impedance Navigation Accuracy Optimization and Scaling Algorithm" (e.g., one of the algorithms 115). Impedance variations of the body's structure and composition can affect the accuracy of the navigation performed by the system 10. These variations can be computationally compensated for by tracking their effect on measured distances at different locations in the body. The process performed by the system 10 to estimate the relationship at a given location between a set of impedance measurements and a corresponding set of known distances (e.g., physical spacing between electrodes) can be referred to as "scaling." By tracking these variations and computationally adjusting them with variable scaling at different locations in the body, impedance navigation becomes more accurate. Initially, when measurements of a device (e.g., a catheter) are first made in the body, there is limited impedance data available. This limited data can be used to initially estimate the in-body scaling. As the device is maneuvered in the body, the scaling information becomes more discretely measured and the scaling information becomes more finely refined. This process of the system 10 is referred to as "dynamic scaling." The impedance navigation accuracy optimization and scaling algorithm may include an "impedance scaling optimization algorithm" (e.g., one of algorithms 115) that processes a set of discrete scaling measurements to create a cohesive coordinate space in which the measured impedance data can be directly mapped to unique coordinate locations. As more measurements are taken, the impedance scaling optimization algorithm may be run iteratively (e.g., at 1 second or 5 second intervals) to provide more accurate navigation of the device. As navigation accuracy improves, previously collected location information may be retroactively updated.Any data stored and / or calculated by the system 10 that is determined using position information can be recalculated (e.g., “refactored”) accordingly. This data includes anatomical measurements, electrical measurements, and / or marked positions from device positions. Some information that is not directly derived from device positions can also be recalculated by the system 10. For example, marked positions placed in coordinate space (e.g., by placing a marker with a mouse over an anatomical structure) that are not created from measured device positions can be stored with a corresponding “equivalent impedance” based on the scaling information available at the time. When the position data is recalculated, the positions of these markers can be recalculated using the equivalent impedance, which can then be displayed (e.g., in a new position on the screen).

[0096] In some embodiments, the system 10 comprises one or more components configured to provide magnetic data (e.g., data recorded by and / or derived from a magnetic component) as used in a hybrid localization system. The system 10 can utilize a magnetic navigation system to simultaneously track a device within the body using a second (e.g., different) measurement modality (e.g., different from the one described above). The system 10 (e.g., algorithm 115) can be configured to perform hybrid disturbance mitigation. The system 10 can utilize measurement redundancy to mitigate disturbances to the impedance tracking subsystem and / or disturbances to the magnetic tracking subsystem. A monitoring algorithm (e.g., one or more algorithms of algorithm 115) can be used for each modality to determine (e.g., repeatedly and / or relatively continuously) whether a disturbance affecting either modality is occurring. When a disturbance is detected by the algorithm 115, the affected subsystem can be temporarily prevented from affecting the display of the tracked device (e.g., a catheter) until the system determines that the associated disturbance has been mitigated and / or is no longer occurring. The system 10 can be configured to perform hybrid navigation accuracy optimization and scaling. When used with an impedance navigation subsystem, the accuracy of the magnetic subsystem is less affected by variations in body structure and composition. Thus, the magnetic navigation subsystem can provide (e.g., more rapidly) accurate navigation data that can be used to (e.g., more quickly) build impedance scaling data as the device is maneuvered through the body. System 10 can utilize the magnetic data as a computational backbone for impedance scaling optimization by replacing the use of "known distances" (e.g., physical spacing between electrodes) to indirectly map impedance variations with a directly measured correspondence between precise locations in space and impedance measurements.The system 10 can navigate devices that include only impedance sensors, only magnetic sensors, or both. Devices equipped with both types of sensors can be used to build an impedance correspondence map directly in the displayed coordinate system. In areas where both magnetic and impedance data are directly measured, impedance scaling optimization may not be necessary.

[0097] The system 10 can be configured to "reconstruct" and store portions of the patient's anatomy to provide a reconstruction of one or more portions of the patient's heart. The stored anatomy can be used as input to data computation algorithms (e.g., one or more algorithms of algorithms 115 configured to perform inverse solution computations), processing and display algorithms (e.g., one or more algorithms of algorithms 115 configured to compute and / or otherwise determine contact point acceptance criteria, closest surface location and / or orientation, and / or estimates of therapy delivery to tissue), and / or as a visual "canvas" on which many forms of data can be displayed. The anatomical information can include one or more anatomical components, such as distinct anatomical structures that distinguish different cavities, veins, arteries, and / or appendages of the heart. The stored anatomical information may include point location data, surface (e.g., shell) data, volume data, data from direct measurements of anatomical structure properties (e.g., density, thickness, tissue type, tissue composition), data from calculations and / or estimations of anatomical structure properties (e.g., normal direction from a surface, angle of incidence to an object, conduction properties such as fiber orientation, scar heterogeneity, preferential pathways or connections to other structures, etc.). The system 10 may be configured to record, store, process, and / or display image data to gather anatomical information. The stored anatomical information may include data recorded by the system 10 and / or data provided to the system 10. In some embodiments, the image data is determined by locating an object within the body. The object's location may be determined using one or more points at a time by determining imaging points, which may be processed to form an imaged surface and / or an imaged volume.

[0098] In some embodiments, the system 10 includes one or more devices (e.g., catheters and / or external devices) that transmit and / or receive ultrasound signals such that the resulting ultrasound data can be converted into image data, such as an anatomical "shell" of the patient's heart wall tissue and / or other tissues (e.g., non-blood tissues) of the patient. From the ultrasound reflection data, an imaging point can be created. In some embodiments, the ultrasound data includes reflection data from one or more transducers. The location of the point where the ultrasound is reflected can be used to determine the location of an object within the heart, such as the heart wall (the "imaging point"). The system 10 can be configured to determine the point where the ultrasound is reflected by determining three basic elements: the point of origin (the transducer location), the direction (vector) of transmission and detection, and the range to the target.

[0099] The system 10 can be configured to update and / or recalculate (e.g., refactor) one or more imaging data points. The system 10 can track each imaging point (calculated ultrasound point) and its three basic elements. In some embodiments, the system 10 can determine updated information (such as more accurate location information) that can be used retroactively to modify the basic elements used to calculate each imaging point. The system 10 can then recalculate updated imaging points from the updated basic elements. For example, if one or more device navigation algorithms (e.g., algorithm 115) of the system 10 provide an updated device position, the origin and / or transmission and detection directions of each imaging point can be updated accordingly. The system 10 can then recalculate the anatomical surface from the updated imaging points. Subsequent calculations performed by the system 10, such as those described herein, can be updated based on the new anatomical surface and / or based on the imaging point information.

[0100] The system 10 can be configured to create imaging points from various types of image data. The system 10 can utilize various image data from ultrasound imaging devices (e.g., B-mode ultrasound imaging devices), CT scanners, X-ray imaging devices, and / or MRI imaging devices to determine and integrate imaging points. Such image data includes data that can distinguish objects based on intensity, color, brightness, and / or other quantitative values ​​within a 2D plane or 3D volume. This image data can be converted to imaging points by determining the orientation and alignment of the image data within a coordinate space tracked by the system 10. The system 10 can select one or more ranges of values ​​within the image data that represent objects of interest, determine corresponding imaging points within the system coordinate space, and integrate new imaging points. In some embodiments, the system 10 utilizes data from two or more of the imaging devices described herein. In these embodiments, weighting factors can be applied to apply different importance and / or influence to data obtained from one imaging device versus another imaging device.

[0101] The system 10 can be configured to organize the imaging points into a data structure. The system 10 can track position information in a coordinate space, for example, a three-dimensional rectilinear coordinate system and / or a Cartesian coordinate system. Within the coordinate space, the system 10 can organize the imaging point data into a data structure, such as a 3D voxel space, where, for example, each voxel designates a unique extent of a volumetric space in the coordinate system, and each voxel can include zero imaging points, a single imaging point, or multiple imaging points. The voxels can be uniform in each direction (e.g., a portion of a 0.5 mm x 0.5 mm x 0.5 mm volume, and / or a 1 mm x 1 mm x 1 mm volume). The size of the voxels can be constant and / or the size of the voxels can vary. The voxels can be adaptively merged and / or split to efficiently organize and process the data they contain. The merging and / or splitting can be based on the data contained within the voxels, such as the location of the data within each voxel and / or the number or density of data within each voxel. The data structures created by system 10 can include octree data structures and / or other highly efficient data architectures to enable efficient data retrieval and / or data processing algorithms (e.g., one or more of algorithms 115). Additionally or alternatively, the data structures can take the form of structured computational meshes (e.g., rectilinear grids) and / or unstructured computational meshes (e.g., tetrahedral meshes), for example, where the manifold neighborhood is explicit in the mesh definition used by system 10 and / or must be determined by system 10 after creation of the mesh.

[0102] The system 10 can be configured to determine attributes and / or quantitative metrics that can be used to analyze the imaging points. The data structures created by the system 10 can track attributes and / or quantitative metrics of each voxel to enable processing efficiency. In some embodiments, each voxel has attributes (e.g., tracks associated attributes) related to whether it is filled or empty, the number of imaging points in the voxel, and / or the density of points in the voxel. Each voxel can track the geometric center of the voxel. Each voxel can track the geometric centroid of the points contained within the voxel. As imaging data is added, removed, and / or modified, the attributes and / or quantitative metrics can be updated by the system 10 accordingly. The system 10 can track multiple parallel data structures such that points can be assigned to different data structures to track independent sets of points. For example, the system 10 can assign points designated to be points from the left ventricle of the heart to a first data structure and points designated to be points from the right ventricle of the heart to a second data structure. Alternatively, the system 10 may use a single data structure to organize all points and track such designations using voxel and / or individual point attributes.

[0103] The system 10 can be configured to identify imaging points that are artifacts. Imaging points may appear as artifacts within anatomical structures. In some embodiments, imaging points that are located within a threshold distance from any previous location of an intracardiac device can be excluded from further calculations. In some embodiments, the system 10 includes multiple threshold distances (e.g., multiple thresholds that correlate to different device types or other distinctions) that are used to exclude one or more devices.

[0104] The system 10 can be configured to generate surfaces (e.g., shells or portions of shells), such as surfaces representing the patient's heart wall and / or other tissue surfaces. The system 10 can create the surface from a set of imaged points. In some embodiments, the surface is calculated from the set of points using a Poisson surface. In some embodiments, a voxel data structure is used by the system 10 to organize the set of imaged points. The surfaces are generated using the "filled" voxels. Because the data density of a set of raw imaged points can far exceed the necessary resolution required to create a surface, organizing the imaged points into voxels can improve the efficiency of the surface calculation. In some embodiments, the locations of the filled voxels (e.g., the center point of each voxel) are used by the system 10 (e.g., algorithm 115 of the system 10) as surface guide points. Each surface guide point is associated with a vector whose direction is estimated to be normal to the desired calculated surface. In some embodiments, the system 10 approximates the normal vector using a normalized radial projection from the center point of the coordinate system to the center of each voxel. The center point is preferably in and near the center of the expected closed surface. In anatomical applications, it is appropriate to select the center point of the anatomical structure. The center point may be reassigned by the system 10 as desired and / or necessary. In some embodiments, the surface may be calculated by the system 10 by first establishing a set of Gaussian basis functions, with the surface location being located approximately at each surface derived point. The Gaussian basis functions, together with the normal vectors, provide a solution to the Poisson equation (below) that defines the surface:

number

[0105] The surface may be represented as a triangular mesh. The resulting surface is a closed surface that follows the inductive points as closely as possible. In regions with no or limited inductive points, the calculated surface may be less accurate and may be removed from the triangular mesh. In some embodiments, the system 10 does not calculate the surface until a sufficient number of inductive points (e.g., 48, 64, or 100) distributed in the coordinate system are collected. In some embodiments, a "sufficiency algorithm" of the system 10 (e.g., one or more algorithms of algorithm 115) may determine when the number and distribution of inductive points is sufficient (e.g., when a threshold is exceeded) to start calculating the surface. In some embodiments, the calculated surface is biased towards the inside or outside of the set of surface inductive points. A "scaling algorithm" of the system 10 (e.g., one or more algorithms of algorithm 115) may be applied that estimates the signed offset of each surface inductive point relative to the calculated surface and repositions each vertex of the calculated surface so that the average offset vanishes. In some embodiments, the surface calculation process of the system 10 generates isolated segments of the surface, and algorithms 115, including an isolated segment removal algorithm, remove these structures.

[0106] In some embodiments, the system 10 can create a visualization of a surface from the imaging points, for example as described herein with reference to FIG. 2. The system 10 can be configured to iteratively calculate and display the imaged surface as imaging point data is continuously collected. The system 10 can display the raw imaging points, the surface derived points, the surface mesh, or any combination thereof. In some embodiments, the surface mesh can be displayed in a single color. In some embodiments, the surface mesh can be visualized as differentiated (e.g., via variations in color, transparency, intensity, etc.) based on attributes and / or quantitative data of nearby surface derived points. For example, the surface can be colored and / or otherwise graphically differentiated based on the density of imaging points in nearby voxels. This visual differentiation allows a user to observe areas where sufficient data has been collected and / or areas where data collection is limited, and the user can adjust data collection accordingly. The opacity of portions (e.g., triangles) of the anatomical model mesh can also be based on the density of imaging points in nearby voxels. During live collection of data and / or iterative calculation of the surface, the visualized surface may have an initial appearance that facilitates user interpretation of the data collection process and / or optimizes computational efficiency and processing speed. When not actively collecting, the visualized surface provided by the system 10 may have an optimized appearance. In some embodiments, the recalculation by the system 10 and updates to the display are restarted at regular intervals, such as at intervals of 0.5 seconds, 1 second, and / or 2 seconds or less. In some embodiments, the recalculation and updates to the display are restarted asynchronously with the acquisition of additional imaging points. In some embodiments, the "restart update algorithm" of the system 10 (e.g., one or more algorithms of algorithm 115) is used to estimate and / or calculate the degree of change that newly acquired data will bring, and the algorithm may determine when the acquisition of new data exceeds a threshold that will trigger a recalculation of the surface and updates to the corresponding display.In some embodiments, system 10 uses a combination of restart methods (e.g., where each restart method is assigned a weighting factor to apply distinguishing importance to each method). In some embodiments, system 10 uses a shorter interval of any one of the combination of restart methods.

[0107] The system 10 can be configured to create anatomical reconstructions, including volumetric reconstructions. In some applications, anatomical structures can be used as volumetric structures rather than surface structures. If a surface structure has already been created, the system 10 can compute the volumetric structure by filling the interior volume of the surface with points and generating a tetrahedral volume mesh that represents the volumetric object. In some embodiments, the set of interior points can be determined (e.g., strictly determined) by testing each point on a regular 3D grid to determine whether it is inside or outside the surface. In some embodiments, an "interior point placement algorithm" of the system 10 (e.g., one or more algorithms of algorithm 115) can efficiently search for points that are inside the surface and can determine a set of interior points that are dense enough to create a tetrahedral volume mesh. In some embodiments, the interior point placement algorithm can use a first seed point that is inside the enclosed boundary of the surface, build a large tetrahedral segment on a portion of the surface (e.g., of uniform size or other similar size), and iteratively subdivide the tetrahedron to smaller sizes until a threshold (such as a desired number of interior points, interior point density, average tetrahedral edge length or volume) is met. Alternatively, the interior point placement algorithm may perform the search using a partition refinement to test points on a regular 3D grid, where the data space is divided into large partitions and each boundary point of the partition is tested and classified as being inside or outside the surface. A partition with one or more boundary points inside is refined and the process is repeated for each subdivision of the previous partition. In some embodiments, previously tested boundary points are not retested. This refinement process may continue until a threshold (such as a desired number of interior points, interior point density, and / or average tetrahedral edge length or volume) is met. In some embodiments, the system 10 includes a number of these thresholds, which may be used to classify (e.g., quantify or qualify) the quality (e.g., resolution, accuracy, etc.) of the output of the process.

[0108] In some embodiments, multiple tetrahedral meshes (e.g., low resolution meshes and / or high resolution meshes) are precomputed, and when the anatomical shell is completed by system 10, the bounding box of the meshes can be estimated (e.g., by algorithms 115 of system 10), and the precomputed meshes can be converted to a bounding box of the anatomical structure. In some embodiments, following conversion of the precomputed meshes, each node of the mesh can be evaluated (e.g., by algorithms 115 of system 10) and classified as being inside or outside the anatomical mesh.

[0109] In some embodiments, the pre-computed mesh includes a range of resolutions, with dense tetrahedrons near the center of the bounding box and sparse tetrahedrons toward the outside of the bounding box. This parameterization of resolution can be determined (e.g., by algorithm 115 of system 10) based on population-level average shapes and metrics, such as distance functions, calculated therefrom.

[0110] System 10 can be configured to create anatomical reconstructions by tracing the positions of catheters and / or other devices to create anatomical data. System 10 can create anatomical data by tracking the positions of navigated devices and determining the outer boundaries of volumes "traced out" by various positions of the device. System 10 (e.g., algorithm 115) can use the tracked positions of the devices, collectively, to form a representation of the anatomical volume.

[0111] System 10 may be configured to create anatomical data by integrating anatomical data collected from multiple methods (e.g., two or more of the methods described herein or other methods). In some embodiments, system 10 integrates various anatomical data (e.g., all or part of the anatomical data) collected (e.g., by system 10) using multiple methods. In some embodiments, imaged surface data is converted to volumetric data by system 10. Data traced by the device may also be represented as volumetric data (e.g., volumetric data including structured and / or unstructured elements). Integration of two data sets may be performed by merging the volumetric data in the same coordinate space (e.g., by algorithm 115) and treating the union of both data sets as a cohesive volume.

[0112] The system 10 may be configured to edit the anatomical structures (e.g., allowing editing by an operator and / or automatically editing). The system 10 may be configured to allow a user to add, remove, and / or edit the anatomical structures. In some embodiments, surface or volume data may be removed and / or modified (e.g., "scraped"). Surface or volume data may be reassigned to different structures. Surface data may be modified by drilling holes in the surface mesh. In some embodiments, user-selectable geometric shapes (e.g., predefined geometric shapes stored in a library of the system 10) are used to facilitate the creation of the anatomical structures. For example, one, two, three, or more of each of a sphere, an ellipse, a teardrop, and / or other shapes may be provided by the system 10 and used to facilitate the initial creation of the anatomical structures.

[0113] The system 10 can be configured to perform optimization of cardiac signals (e.g., EGMs) (e.g., via algorithm 115). For example, the system 10 can be configured to perform far-field compensation. In some applications, such as by finding an inverse solution for atrial activation, the far-field effects of activation in a chamber other than the atrium being diagnosed and / or treated, such as the ventricle or the contralateral atrium, can be disruptive. It may be advantageous to separate the relevant components of the EGM from the chamber of interest before computing the inverse solution map. As an example, separating and excluding the ventricular component (QRS) from the atrial component can greatly benefit the quality of the data generated by the system 10 (e.g., mapping data generated by the system 10). The system 10 can perform various signal processing functions, such as processing the ECG to inversely model the QRST or PVC (e.g., in a self-optimizing configuration). In some embodiments, the system 10 can use a surface ECG to create a crude model of ventricular depolarization and repolarization. In some embodiments, the system 10 (e.g., via algorithm 115) can create this model using an inverse solution that dynamically detects ventricular activity (e.g., QRS) on the body surface leads, calculates an inverse solution estimate of the ventricular activity (as measured at the intracardiac catheter location) for each ventricular beat, and subtracts the calculated ventricular activity from each corresponding beat of the intracardiac signal. The ventricular template can be identified automatically (e.g., by algorithm 115) and / or the template can be user-determined based on the intracardiac signal in addition to and / or instead of ECG-based parameterization. If the ventricular component is inversely calculated by the system 10 independently of the atrial component, the system 10 (e.g., algorithm 115) can estimate the forward matrix using subject-specific anatomy, rule-based averaging, and / or geometric primitives. This combination of estimation and subtraction is intended to preserve as much of the atrial component of the intracardiac signal as is practical and remove as much of the ventricular component as is practical.The "atrial only" EGM can then be processed (e.g., via algorithm 115) using an inverse solution of whole-chamber non-contact mapping to result in a map with minimal ventricular artifacts or misannotations due to the presence of ventricular artifacts. The ventricular estimation performed by system 10 can also be self-optimized by evaluating the residuals of the removed ventricular signal and then updating the estimation accordingly to minimize the residuals. Additionally, the estimate can be refined by system 10 (e.g., via algorithm 115) by first applying grouping, clustering, and / or classification to ensure that the collected heart beats (also referred to herein as "cardiac beats" or "beats") used for the estimation are similar and sufficiently consistent with the beats to which the estimation is applied. Beats with different characteristics will be classified to be in different groups and therefore have different estimates applied.

[0114] The system 10 can be configured to perform measurements (e.g., direct measurements) in an arrangement where artifacts introduced by far-field activity are reduced. The system 10 can perform measurements from a first electrode (e.g., of a device of the system 10 inserted into a heart chamber) and use a second electrode (e.g., of the same or a different device of the system 10) as a near-field reference. The first electrode can be configured to touch the tissue to perform the measurement (e.g., configured to perform a contact measurement). The second electrode can be configured to not touch the tissue when performing the measurement (e.g., configured to perform a non-contact measurement). By subtracting the signal of the second electrode from the signal of the first electrode, the far-field components measured by both are suppressed, but the local signal measured only by the electrode in contact is preserved (e.g., not significantly suppressed).

[0115] The system 10 can include one or more catheters or other devices that include stacked electrodes (e.g., stacked microfabricated electrodes). For example, in some embodiments, the first and second electrodes are configured in a stacked orientation, with the stacked electrodes separated by a small separation distance. Electrodes in this configuration can be printed on a flexible circuit and deployed on a steerable catheter where in most deployed configurations, only the first electrode contacts tissue.

[0116] System 10 can be configured to perform measurements from far-field contributors, such as in the ventricles when treating and / or diagnosing the atria. In some embodiments, the ventricular estimate creation and subtraction process of system 10 can utilize one or more direct measurements from the opposing chambers. The direct measurements can be used in place of the inverse solution template, or these measurements can be used to match the inverse solution template.

[0117] The system 10 can include algorithms 115 including one or more machine learning, neural net, and / or other artificial intelligence algorithms (herein "AI algorithms"), and the system 10 can include machine learning or other AI algorithms (e.g., one or more of the algorithms 115) for determining EGM fiducials, categories, and other parameters. The system 10 can be configured to store, share, and / or learn from user interventions (e.g., user intervention of an automated process). The system 10 can also be configured to adaptively self-optimize the automated process in response to learning. The system 10 can maintain a self-updating data set of any user inputs that override the automatic detection or measurements made by the system. The user selections and the corresponding raw data can be processed and stored as labeled data. Some examples of such labels include, but are not limited to, measurement channels to be excluded, threshold changes to be made, time annotation changes to be made, beats to be included and / or excluded, points to be included and / or excluded, etc. The stored database of labeled data can be used to locally "learn" (e.g., identify or evaluate) underlying features within the data that result in more accurate automated results. System 10 can also be configured to automatically share and / or transfer this database to a master repository that can be shared with one or more units of other system 10 (e.g., at different locations, such as when shared via a secure wired or wireless connection arrangement).

[0118] As described herein, the system 10 can be configured to perform various forms of cardiac electrical mapping. The system 10 can process cardiac signals to detect and analyze cardiac events, such as heart beats and / or cardiac cycles. The system 10 can be configured to perform "rhythm tracking", such as where the heart beats are classified. The system 10 (e.g., via the algorithm 115) can automatically distinguish and / or classify each independent heart beat based on their having similar characteristics, either in real-time or near real-time (herein "real-time") and / or in a post-processing step. Groups can be used for mapping modes that utilize multiple heart beats to sequentially aggregate data. Some mapping modes can utilize beats that do not fit into existing groups, and the beat grouping feature helps to automate the identification of these unique beats (e.g., via the "trigger mapping mode" of the system 10 described herein). The system 10 can perform heart beat detection, annotating a reference beat with respect to an initial time point, i.e., time point T0. In some mapping modes, the system 10 time-aligns the heart beats, such as on a time fiducial. The system 10 can detect each beat by looking for signal features of one or more cardiac signals recorded by the system 10. The cardiac signals analyzed by the system 10 for beat detection can be unipolar, bipolar, omnipolar, Laplacian, and / or any mathematical combination of one or more signals. The analyzed signals can also be derivatives, envelopes, energy functions, histograms, and / or other results of mathematical operations performed on the measured cardiac signals. The system 10 can obtain these signals from one or more devices of the system 10. A signal (e.g., a processed signal) can be a mathematical composite of multiple signals. The system 10 can analyze a combination of two or more of the above signals (including, for example, a processed signal), for example, when the system 10 analyzes both unipolar and bipolar (e.g., simultaneously or sequentially analyzing unipolar and bipolar).The signal features identified by the system 10 may include one or more features selected from the group consisting of exceeding a threshold (positive, negative, or absolute value), a local maximum or minimum peak (positive, negative, or absolute value), a local maximum slope (positive or upslope and / or negative or downslope). The system 10 may use the time of the detected feature for T0 alignment. The system 10 may optionally apply a time offset from the detected feature. The system 10 may use two or more of the above signal features in combination, for example, a feature with a cardiac event exceeding a threshold and having a sufficiently large local maximum negative slope. If the system 10 is using two or more signal types (e.g., simultaneously), the signal features for each signal type may be the same or different for each signal type. In some embodiments, the system 10 may require that the positive peak of the filtered bipolar signal be large enough to establish independent beats and to establish a T0 reference time. Alternatively or additionally, the system 10 may establish the T0 reference time using the mean, median, and / or maximum of an envelope or energy function of one or more bipolar signals.

[0119] In some embodiments, conduction velocity is estimated as a global optimization function, whereby the global velocity is regularized and conditioned by system 10 (e.g., by algorithm 115) based on physiological ranges and a loss function defined based on deviations from average physiological values ​​and / or stratified based on a set of average values ​​compiled from pathological populations.

[0120] In some embodiments, the solution is regularized by the system 10 (e.g., by algorithm 115), such as by spatial filtering on the graph and / or mesh using techniques such as arithmetic mean, arithmetic median, and / or projection onto graph-based basis functions. In the case of conduction velocity, filtering can be applied to the coordinates of the velocity vector in their canonical form and / or their quaternion form. Filtering can also be applied via nonlinear techniques, such as via neural networks and / or local nonlinear filters such as median.

[0121] The system 10 can be configured to perform heartbeat classification in which reference channel exclusion is performed. The system 10 can perform reference channel exclusion selected from the group consisting of excluding (e.g., automatically excluding) one or more channels, disabling (e.g., automatically disabling) one or more channels from being used as a T0 reference time, disabling (e.g., automatically disabling) one or more channels from being used in detecting and / or classifying heartbeats, and combinations thereof. These different channel exclusions can be performed due to electrical disconnection and / or lack of detectable features in those channels, such as due to poor performance when used as a T0 reference time and / or when classifying beats. The system 10 can automatically disconnect one or more channels if a sufficient (e.g., significant) amount of a particular frequency (e.g., 60 Hz) is present on that channel, as this condition is likely to indicate that the sensor (e.g., electrode) is electrically disconnected and / or is "noisy" (e.g., electrical interference or other signal noise is occurring). The system 10 can be configured to automatically exclude channels from use if the signal amplitude is below a threshold (e.g., if the sensor is in a suboptimal position to measure the cardiac signal) and / or if the signal amplitude is above a threshold (e.g., if pacing results in a large pacing artifact that is much larger than the underlying cardiac signal). The system 10 can be configured to exclude sensors (e.g., electrodes) if their position is unclear, inconsistent, and / or abnormal, as this may indicate a poor quality electrical connection and / or the influence of an external system that may degrade performance. The system 10 can be configured to automatically exclude channels based on statistical analysis, data mining, and / or machine learning or predictive analysis to analyze the measured signal of the channel against a model and / or library of previously allowed and / or excluded signals (e.g., resulting in a determination of a set of channels to be excluded).In some embodiments, the models and / or libraries used by the machine learning and / or predictive analytics algorithms of system 10 (e.g., the AI ​​algorithms of algorithms 115) are composed of accepted and / or excluded labeled signals based on an operator's decision.

[0122] The system 10 can be configured to perform "optimal reference channel selection." The system 10 can automatically select and / or suggest the use of an optimal signal or set of signals to use for T0 reference and / or for heartbeat classification. The system 10 can use ranges and / or other thresholds for stability and / or consistency of amplitude and / or timing interval (e.g., cycle length). The system 10 can utilize confidence metrics used to evaluate multiple signal characteristics including, but not limited to, cycle length stability, amplitude, signal morphology (e.g., degree of segmentation, or the presence of certain signal components such as a certain number of waveform deflections, or a particular shape such as "RS" morphology), and / or signal confidence score (e.g., using only signals with sufficient confidence scores).

[0123] The system 10 can be configured to compare various features of the recorded signals. The system 10 can be configured to use one or more features to distinguish or classify heart beats, such as distinction based on features selected from the group consisting of stability and / or consistency of timing intervals (e.g., cycle length), signal morphology (e.g., unipolar signal morphology), envelope and / or energy function (e.g., bipolar signal envelope), timing sequence and / or pattern across multiple signals (e.g., pattern of annotated time fiducials across a set of unipolar and / or bipolar signals from sensors at different locations in the heart), and combinations thereof. The system 10 can be configured to use wavelet decomposition to isolate unique signal components from the signal morphology and, for example, quantitatively compare these components with components of other signals. The system 10 can perform cross-correlation between signals and / or wavelet components to quantify the degree of agreement between two or more signals. The system 10 can be configured to perform statistical analysis on cycle length (interval between beats) to characterize the beats. Similar cycle lengths may indicate the same rhythm or cardiac circuit, and differences in cycle lengths may indicate changes in the rhythm or cardiac circuit. The system 10 may be configured to use an envelope and / or energy function of one or more signals. The signals may be unipolar, bipolar, omnipolar, Laplacian, and / or any other mathematical combination of one or more signals. When multiple signals are used by the system 10, the envelope and / or energy function may be evaluated for each individual signal and / or may be evaluated for an ensemble or composite of a combined set of signals. For example, the envelope and / or energy function of each signal in a set of multiple bipolar signals from one or more locations of the heart may be used as a template against which the same signals from other heartbeats can be compared. The system 10 may be configured to "match" (e.g., like fitting a key into a lock) a set of comparison signals that fit the template.Alternatively or additionally, the system 10 can stack, aggregate, and / or otherwise create a composite of multiple signals and then determine an envelope and / or energy function of the composite to create a template. A similar composite and envelope and / or energy function can then be constructed for each comparison beat and compared to the envelope / energy function of the template. In some embodiments, the system 10 can quantitatively compare beats using cross-correlation of the envelope and / or energy function of the template with the envelope and / or energy function from the comparison beat. The system 10 can be configured to match beats that have a sufficient correlation score. In some embodiments, the system 10 can distinguish beats using a combination of multiple features.

[0124] The system 10 can be configured to separate the heart beats into groups. For example, the system 10 can be configured to automatically create unique groups and classify individual beats into those groups. The system 10 can perform multiple forms of clustering and / or classification techniques, including, but not limited to, linear or quadratic discriminant analysis, correlation analysis, principal component analysis, k-means (e.g., connectivity or centroid-based) clustering, support vector machines, kernel methods, neural networks, spectral clustering, hierarchical clustering, distribution-based clustering, density-based clustering, and / or grid-based clustering. These techniques can be configured to identify similar beats and / or classify the beats into groups. In some embodiments, the system 10 processes the set of wavelet decomposed signals using k-means clustering to determine a set of clustered groups of beats. The system 10 can use a combined and / or weighted score of multiple signal features to determine an overall group classification. For example, the system 10 may group beats based on a combined weighted score using both cycle length and k-means clustering of the wavelet decomposed signal to generate (e.g., identify) multiple groups. By utilizing a weighting technique, the system 10 may be configured to allow a user to select the relative weighting assigned to each individual score. For example, a user may prefer to weight the importance of cycle length changes less in the clustering / classification while increasing the relative weighting provided to the morphology score (or vice versa). The system 10 may be configured to perform live (e.g., real-time) and / or post-processing calculations. For example, the system 10 may process beat detection and classification as a post-processing step on recorded data or iteratively "on the fly" (e.g., in real-time, live) each time a new beat occurs.

[0125] The system 10 can be configured for a "trigger mapping mode," which is a mode configured to perform, for example, a "unique beat detection and rapid mapping routine," in which the routine detects unique beats and / or performs rapid mapping (e.g., based on PACs, PVCs, and / or other triggers). The system 10 can be configured to utilize (e.g., for inclusion in one or more analyses) beats that do not fit into existing groups to identify unique beats and / or less repetitive beats. The system 10 can establish one or more beat groups and identify beats that do not match the established groups. The system 10 can be configured to visually designate (e.g., via graphical distinctions as described herein) and / or otherwise identify (e.g., via a display of the system 10) these non-matching beats. Once these unique beats are detected, the system 10 may employ a number of mapping methods to identify signal derived values ​​(e.g., derived cardiac data) from these beats, including, but not limited to, activation time, peak-to-peak amplitude, timing of beats or other inter-beat metric waves (e.g., cycle length, ST segment), and / or other data derived from the cardiac signal. The cardiac data may be calculated directly from signals measured at one or more devices (e.g., catheters), and the data may be displayed as a visualization (e.g., a 3D visualization) on top of an anatomical shell. The derivation of cardiac data from the cardiac signal (e.g., from individual or multiple catheters) to generate a visualization may be performed by the system 10 in a variety of ways, including, but not limited to, interpolation and / or direct assignment of values ​​based on the proximity of electrodes to the shell, solving an inverse solution, and combinations thereof. The system 10 may be configured to distinguish (e.g., colorize or otherwise graphically distinguish) the sensors, objects (shells, markers, volumes of space) in the vicinity of the sensors, and / or combinations thereof on the display. The displayed distinction may be based on activation timing and / or amplitude data.

[0126] The system 10 can be configured to perform a "rhythm classification routine." For example, the system 10 can utilize the detected and classified heart beats to make automated "suggestions" of rhythm types (e.g., as presented to an operator on a display of the system 10). The system 10 can base suggestions on fixed metrics such as cycle length range or RR interval between QRS (or any other beat-to-beat metric). The system 10 can base suggestions on statistical analysis, data mining, machine learning or other AI algorithms, and / or predictive analysis that analyzes the signal against a model and / or library of previously classified rhythms, and combinations thereof. For example, cardiac signals such as those used to detect and classify individual beats (e.g., a set of reference EGMs from a catheter placed in the coronary sinus) can be processed by the system 10 (e.g., via algorithm 115) using a wavelet transform that decomposes each waveform into different frequency bands while still preserving temporal information to generate a wavelet scalogram image. This processing of the cardiac signal can be performed by the system 10 in an unsupervised workflow as well. Image-based features in the scalograms of the comparison beats can be evaluated by system 10 using a convolutional neural network or other AI algorithm trained on a library of labeled scalogram images of known rhythm types. Some classifiable rhythms of system 10 include, but are not limited to, atrial flutter, atrial tachycardia, atrial fibrillation (AF), sinus rhythm, paced rhythm, or ventricular tachycardia. Rhythm classification can also be performed using any duration of data (not just a single detected and classified beat) and / or any set of signals acquired by system 10 (e.g., as acquired by a reference catheter, a mapping catheter, body surface electrodes, etc.).

[0127] System 10 can be configured to perform mapping (e.g., cardiac mapping) of the concepts of the present invention using various forms of data collection and data analysis. System 10 can be configured to display electrical events and / or activity (e.g., cardiac activity) in the form of an electroanatomical map (EAM) based on data originating from electrical signals (e.g., electrograms or EGMs), where the data is visualized on top of a display of anatomical structures. The EAM of system 10 can show activity data (AD) in a single location, region, cavity, the entire heart, and / or any other body volume (e.g., tissue volume). The AD can include activation time, amplitude, conduction velocity, segmentation, complexity index, pattern detection, sequence detection, causality index, recurrence index, dispersion index, refractory (e.g., angle between subsequent activations) metrics, and / or any calculations from the signals (e.g., cardiac signals and / or imaging signals). Similarly, integrated metrics, such as metrics using activation sequence along with refraction metrics, can be applied by system 10 (e.g., by algorithm 115) to determine spatiotemporal initiators of refractory events. The EAM generated by system 10 may also include AD based on signals from an inner surface (endocardial surface), an outer surface (epicardial surface), and / or tissue between the two surfaces (mid-myocardial or trans-myocardial tissue). AD may be based on signals acquired by electrodes in contact with the tissue (contact data), and / or AD data may be from calculated signals in the tissue derived from electrode measurements not in contact with the tissue (non-contact data). AD may be based on signals that may be unipolar, bipolar, omnipolar, Laplacian, and / or any other mathematical combination of one or more signals, and / or from derivatives, envelopes, energy functions, or other results of mathematical signal operations. AD may be based on signals that may be acquired from one, two, or more devices of system 10 (e.g., one, two, or more catheters, patches, and / or other components of system 10).Both contact mapping and non-contact mapping based on an inverse solution are described herein. AD can be based on contact measurement signals and / or non-contact calculated signals. EAM can include combination, aggregation, integration, and / or fusion of one or more types of map data, for example, unipolar and bipolar AD simultaneously, and / or contact and non-contact AD simultaneously. System 10 can also include a "data fusion algorithm" (e.g., one or more algorithms of algorithm 115) that can be configured to cohesively combine data of different forms and / or different signal origins. For example, the data fusion algorithm can calculate activation times from both bipolar and corresponding unipolar signals, and / or from contact and non-contact signals. The data fusion algorithm can determine whether either, both, or neither is feasible, and if both are feasible, determine the activation times to be used in EAM. If the activation times do not match, the data fusion algorithm can use a rule set that includes different signal characteristics such as amplitude, slope, width, morphology, energy, etc. to select the optimal activation time to use and / or calculate an intermediate value to use. In these embodiments, the system 10 includes one or more thresholds that are used to evaluate the viability of the signal or to perform another data evaluation. Alternatively or additionally, the data fusion algorithm can include a learning model configured to determine the optimal activation time to use based on historical data (e.g., labeled data) and / or through the use of an unsupervised workflow (e.g., a workflow that uses unlabeled, non-fiducialized data). In some embodiments, the data fusion algorithm can combine contact bipolar amplitudes with non-contact unipolar amplitudes. In other embodiments, the data fusion algorithm can combine activation times from contact signals (e.g., contact bipolar signals) and non-contact signals (e.g., non-contact unipolar signals).Data fusion algorithms can establish relationships between data of two different types and / or different signal origins and provide the data for display in a cohesive unit of measurement, such as a normalized percentage or equivalent unit of measurement.

[0128] System 10 can be configured (e.g., via a data fusion algorithm) to perform a "live scan" (e.g., scan in real time) by performing a mapping directly from the measurements. System 10 can be configured to directly calculate electrical activity information from the measured signals and display them (e.g., rapidly display them) on the anatomical structure (e.g., a shell or other image of the anatomical structure provided on a display by system 10 as described herein). In some embodiments, the data is assigned or projected onto an anatomical surface on a display of system 10. If the electrical activity information was collected in close proximity to the anatomical structure (e.g., at a distance of less than 5 mm), it can be displayed on the anatomical structure. System 10 can calculate (e.g., directly calculate) electrical activity information from electrograms at a distance (e.g., a threshold distance of at least 5 mm) and project it onto the anatomical structure as a sparsely sampled, low-density map of electrical activity information. In some embodiments, system 10 includes an "interpolation algorithm" (e.g., one or more of algorithms 115) configured to calculate display data in areas where measurements are not available. A data fusion algorithm can cohesively integrate AD collected in close proximity with AD measured at a distance (e.g., above a threshold). In some embodiments, unipolar signals from a device (e.g., a catheter) that is not in contact with tissue (e.g., electrodes are not in contact with tissue) are measured and directly annotated for local activation times, e.g., using the steepest negative slope. These activation times are projected onto the nearest surface, or onto the nearest surface along a vector (e.g., a vector perpendicular to the electrode orientation). If the device (e.g., a set of associated electrodes) is positioned approximately in the center of the cavity, the activation times can be projected around the entire cavity. If the device (e.g., a set of associated electrodes) is closer to the wall of the cavity, the activation times can be projected onto the near wall.Activation times may be calculated by the system 10 for all detected beats and / or these times may be calculated only for detected unique beats (e.g., as described herein with reference to the trigger mapping mode and / or the unique beat detection and rapid mapping routines). Once the AD is calculated by the system 10, as another visualization, images of the measurement sensors (e.g., electrodes) may be differentiated (e.g., colored or otherwise differentiated on the display) to indicate the relative relationship between each sensor. For example, if the AD is a local activation time, the earliest detecting sensor may be colored red to designate the "early" part of the circuit and the latest detecting electrode may be colored purple to designate the "late" part of the signal. Alternatively or additionally, the AD may be shown on top of the anatomical structure and may be color coded similarly. Other variations of visual differentiation are within the spirit and scope of the present application.

[0129] The system 10 can be configured to automatically place "markers of interest" on a display of information provided by the system 10 (e.g., mapping and / or other information related to a clinical procedure performed using the system 10). The system 10 can be configured to place markers of interest (also referred to herein as "markers") in a displayed coordinate system and / or on an anatomical structure, such as placing them at locations of interest based on the AD. The markers can have visual attributes that specify the confidence of the marker (e.g., the confidence associated with the location of interest). For example, if the AD is a local activation time, the system 10 can display a large marker on the anatomical shell at the "earliest" location. For each detected heart beat, a new large marker can be placed on the anatomical shell. The operator can interact with the markers (e.g., via the user interface 120 of the system 10), such as to indicate (e.g., add) relevant information about the corresponding beat. Over a number of consecutively detected beats, the system 10 can provide (e.g., visually provide) multiple markers, such as where these markers indicate spatial consistency of the marked locations.

[0130] The system 10 can be configured to adjust the resolution of a map (e.g., EAM), such as changing (e.g., upgrading) from a low-resolution map to a high-resolution map. When used with the unique beat detection and rapid mapping routines described herein, the map and markers of each unique beat can be used to indicate the earliest activation site. The map data, if directly calculated, can be somewhat coarse. However, any directly calculated map of a detected beat can be further processed into a high-resolution map using an inverse solution method. The corresponding displayed color map and large markers will be more detailed, and the EAM will be calculated at a higher resolution, with the markers smaller and more accurately positioned. The direct calculation of AD to form a map, automatic placement of markers, and further processing into a high-resolution inverse solution map can be performed by the system 10 for any beat specified by the operator.

[0131] The system 10 can be configured to provide a confidence score associated with the cavity of origin. The system 10 can be configured (e.g., via algorithm 115) to execute a "chamber of origin routine" that provides a confidence score that the origin (e.g., if present) of a given beat is within the cavity being mapped or an adjacent cavity. The chamber of origin routine can use relative timing information between intracardiac and body surface measurements to calculate the confidence score. The routine (e.g., algorithm 115) can also use one or more morphological analyses to automatically identify characteristic morphological features in the EGM of the earliest site of activation, e.g., a slight positive hump in the rS pattern, that the system 10 uses to determine the confidence score.

[0132] The system 10 may include an algorithm 115 configured to perform a "regional inverse mapping routine," such as a routine in which an inverse calculation of cardiac activity within a region is performed. The EAM calculated by the system 10 may be an inverse solution map of the entire cavity. In some embodiments, the inverse solution is applied to solve EGMs that apply to only a portion of the cavity. The calculated EGMs within the region may be used by the system 10 to determine ADs within the region, including activation times, signal amplitudes, and / or scar regions. The forward matrix may be adapted (e.g., by algorithm 115) to solve for the entire cavity, such as when the entire cavity is devoid of certain structures (e.g., devoid of a left atrial appendage), and / or to solve for those individual structures themselves. Using the forward matrix, the inverse solution may be derived (e.g., by algorithm 115) via direct inverse solutions, by solving simultaneous equations, via one or more neural networks, and / or via iterative solutions of regularized optimization problems. These optimizations may include residual terms that enforce consistency with the measurements and regularization terms (e.g., further impose regularities or prior knowledge on the inverse solution). The residual terms may use various loss metrics, including least squares, sum of absolute differences, and / or iteratively weighted least squares. The residual terms may also be used by the system 10 to constrain the solution to satisfy certain constraints, such as being non-decreasing or non-increasing in time, or being within a predefined subspace (e.g., a subspace derived from the data via linear or non-linear methods, and / or a subspace derived from the domain in which the solution is defined, such as graph-based basis functions). The regularization terms used by the system 10 may be in the form of zero, first order, and / or second order Tikhonov regularization. Alternatively or additionally, other regularization methods may be used, such as graph-derived basis functions, dictionary-based approaches, median filtering, plug-and-play methods, and / or neural network derived regularization.

[0133] In some embodiments, system 10 can directly estimate the relevant AD information without first explicitly deriving the EAM. This estimation can be performed by system 10 through projection and interpolation of the AD from catheter-acquired signals (e.g., signals acquired by one or more catheters as described herein) to the cavity (and / or its regions) and / or through nonlinear iterative optimization to directly solve for the AD given a predefined model for the electrical activity in the region of interest.

[0134] Post-processing techniques can be applied by system 10 (e.g., by algorithm 115) to both the EAM and AD solutions using a variety of methods, including mean and median filtering, graph-based filtering methods, and / or neural network methods.

[0135] The system 10 may include an algorithm 115 configured to execute a “supermap routine,” such as a routine configured to generate an activity map derived from signals produced and recorded over time from an electrode array that is repositioned during a recording process.

[0136] The system 10 can be configured to perform cardiac information analysis. The system 10 can process activity data (AD) to calculate additional metrics that can be used to analyze the electrical activity of the patient's tissue. Various analyses can be performed by the system 10 (e.g., by the algorithm 115) to identify one, two, or more clinical sites of interest. One analysis that the system 10 can perform is the identification by the system 10 (e.g., by the algorithm 115) of conduction patterns using one or more of various pathfinding techniques, such as streamline and / or other techniques that identify clinically relevant paths, a process of identification referred to herein as "autopathing." The system 10 can perform autopathing by taking a set of streamlines and clustering their traversals using AD (e.g., electrophysiological and / or biophysical data) to determine a descriptive path of electrical propagation across a given heart chamber (e.g., the atrial body). This process is an extension of streamline-based techniques that determine nearly all paths (e.g., given enough starting locations). Some analyses performed by the system 10 (e.g., by the algorithm 115) can be configured to quantify the spatial distribution and / or temporal occurrence of one or more features of interest to characterize one or more clinical sites of interest. The automated pathfinding performed by the system 10 can operate in a modified coordinate system (e.g., in addition to Cartesian coordinates). The system 10 can represent the data in a 2D conformal data space and / or in an anatomically determined data space (e.g., universal atrial coordinates) created from the subject's specific anatomy. Some analyses performed by the system 10 can quantify the spatial distribution and / or temporal occurrence of features of interest to characterize the clinical sites of interest. Feature identification includes, but is not limited to, block, isolation, isthmus, breakthrough, and / or epicardial bridge.A "block" feature can represent the disappearance of activity at a location without coherence with the apposed site. An "isolation" feature can represent a location of contiguous tissue being electrically isolated from another region of tissue (such as the pulmonary vein being electrically isolated from the body of the left atrium). An "isthmus" feature can represent a region of tissue where apposed, but not necessarily contiguous, pathological tissue is present and prone to reentrant phenomena. A "breakthrough" feature can represent a surface location (e.g., a surface such as the endocardium) where the origin of electrical activity does not originate from that surface location (e.g., the earliest site of activity on the surface but not within the structure). An "epicardial bridge" feature can represent a conductive pathway of tissue proximal to tissue that may be rendered dysfunctional or ablated.

[0137] The system 10 can be configured to identify various conduction patterns, such as localized irregular activation (LIA), localized regional activation (LRA), and / or focal patterns. The system 10 can be configured to detect and count occurrences of spatiotemporal conduction patterns anywhere within a chamber (e.g., a heart chamber such as the left atrium) by analyzing the spatiotemporal activation sequences present in the activation map. At every location (vertex on the mesh) within the chamber, each activation at that location can be analyzed by the system 10 in relation to neighboring activations within a small surrounding region (at least 5 mm or 10 mm in diameter, and / or no more than 25 mm or 15 mm in diameter). Conduction velocity can be calculated from activation times within this region. The activation sequence and conduction direction of every beat can be evaluated against one or more sets of rules to classify the local conduction pattern. The incidence of each pattern type at each location can be quantified and displayed as a histogram (e.g., a color-differentiated histogram) on top of an anatomical model (e.g., a shell), where a higher incidence at the same location can be visualized with differentiated visual characteristics (e.g., greater opacity and color intensity). Visualizations of the incidence of multiple pattern types can be displayed simultaneously by system 10.

[0138] The system 10 can be configured to determine various conduction characteristics, such as when activation data (AD) is processed to quantify different conduction characteristics. The system 10 can be configured to determine conduction velocity. Conduction velocity through tissue is a highly relevant metric of tissue activity. Using spatially and / or temporally distributed local activation times, the system 10 can calculate local conduction velocity. In some embodiments, the local activation times of the 3D shell surface mesh can be projected onto a plane, and the spatial gradient of activation in the projected plane can be used to approximate the conduction velocity. Similarly, conduction velocity can be calculated by the system 10 (e.g., by algorithm 115) using similar operations (e.g., gradient estimation in the computational elements) in an element-by-element arrangement to selectively enhance performance in some regions (e.g., increase sensitivity to small spatial structures). The gradient operation can be performed by the system 10 using triangulation and / or finite difference type techniques to estimate the gradient. The conduction velocity can be color mapped (e.g., data is presented in a color-coded or other graphically differentiated arrangement) and / or the velocity can be displayed directly. The deceleration can be calculated and displayed as a conduction metric by calculating the gradient of the conduction velocity. The system 10 can determine a relative conduction velocity, such as a conduction velocity determined (e.g., provided to an operator) as a percentage change (e.g., decrease and / or increase) and / or as a normalized value. The relative conduction velocity can be an advantageous metric to account for potential inter-patient or inter-map variability in conduction velocity. In many cases, identifying the region of greatest acceleration (e.g., where the velocity is most decreased or increased) can be more clinically relevant and valuable in diagnosing arrhythmias (e.g., AF) than exceeding a certain threshold of velocity. The relative conduction velocity can be calculated as a percentage decrease or increase, and / or the velocity can be normalized to the fastest percentage of the velocity in the map. The system 10 (e.g., the algorithm 115) can also estimate the refraction of the wavefront using the estimated conduction velocity through the cavity.The refraction can be based on the angle between subsequent activations of a region of tissue. Using any of these parameters along with the activation sequence, the system 10 (e.g., the algorithm 115) can estimate the location of the refraction map. The system 10 can be configured to determine the signal amplitude. For example, the system 10 can use the amplitude of the local signal as a metric of conduction. In some embodiments, the peak-to-peak amplitude of the local bipolar signal can be color mapped (e.g., a map that differentiates by varying color and / or other graphical characteristics). In some embodiments, the negative peak amplitude of the local unipolar signal can be color mapped. In some embodiments, the omnipolar or Laplacian amplitude can be color mapped. In some embodiments, the amplitude of the non-contact calculated charge density signal can be correlated with the amplitude of the contact voltage signal for the same location. This correlation can be used to define a representative relationship between charge density units and voltage units for that map. In some embodiments, this relationship can be used to create amplitude maps of both charge density and voltage data types. A representative relationship between charge density calculations and millivolt equivalents can be calibrated by system 10 (e.g., by algorithm 115) using a back-calculated potential (e.g., a potential that is a signal sampled directly near the anatomical body in the blood pool).

[0139] The system 10 can be configured to perform spatio-temporal analysis. Multiple forms of spatio-temporal analysis can be performed by the system 10. The map data of the system 10 can include a set of spatially linked time-varying signals (electrograms) and / or temporal events (e.g., regional activations). The system 10 can determine multi-dimensional activation sequences or patterns. In some embodiments, the spatial distribution of temporal events can be plotted (e.g., further displayed) by the system 10 as a multi-dimensional image, where time is the first dimension and the spatial distribution of anatomical locations can be three additional dimensions, or can be reduced to a smaller number of dimensions (e.g., through dimensionality reduction by projection into a 2D parameterized space, or mapping to universal common coordinates, universal atrial coordinates for atrial EAMs). The reduced data space can also be calculated by the system 10 (e.g., by algorithm 115) in a conformal data space in which the mitral valve is used as the point of unwrapping. Thus, the time-varying nature of electrical activity can be captured as a spatiotemporally-representative static image (SRSI), where the size of the image is largely limited by the duration of the map data. This technique therefore allows the system 10 to process highly complex multi-dimensional data sets using image analysis and / or comparison techniques. In some embodiments, the SRSI can be analyzed by the system 10 by searching for kernel patterns of a given window size in the time dimension that are repeated in other parts of the image. These recurrences may be clinically relevant in characterizing activity and targeting treatment. The SRSI can be reprocessed multiple times, varying window sizes from very small to very large, to search for kernels of possible different sizes. In some embodiments, the SRSI can be analyzed by the system 10 for spatiotemporal coupling relationships between different regions of the anatomy.In SRSI, activation sequences between two regions of a cavity that have a strong coupling relationship follow a common and consistent vector that can be detected by a number of pattern detection techniques, including machine learning, deep learning, and / or other AI algorithms. In some embodiments, coupling is determined by system 10 (e.g., by algorithm 115) through analysis of temporal variations in spatial correlation.

[0140] The system 10 can be configured to perform spatiotemporal analysis by performing network analysis. In some embodiments, the spatiotemporal sequence of activations can be analyzed as a network analysis by the system 10. The network can be formed by interconnected nodes of an anatomical structure, with adjacent nodes being adjacent locations on the anatomical structure and nodes further apart being further apart along the surface of the anatomical structure. For any activation of a node, the upstream and downstream activations encode the connectivity between regions of the anatomical structure, and "bottlenecks" where downstream activations permeate a wide region of the anatomical structure may be efficient therapeutic targets for altering or eliminating perpetuating rhythms. In some embodiments, each activation at each node can be evaluated within a window (e.g., at least a 25 ms or 50 ms window, and / or a 250 ms or 100 ms window or less) to evaluate the region of downstream influence of the activation at each node. Regions with greater downstream influence may be more effective in perpetuating the arrhythmia.

[0141] The system 10 can be configured to analyze the spatiotemporal sequence of activation by visualizing time-referenced activation regions. The activation time data of the EAM can be divided by the system 10 (e.g., by the algorithm 115) into time intervals. The time-referenced activation regions can be calculated as the regions corresponding to the parts of the EAM that have activation times within each time interval. Alternatively or additionally, the system 10 can use the number of measurements and / or points. The time-referenced activation regions can be provided (e.g., visualized) as plots, e.g., histograms, where one axis represents time and the other axis represents the regions of activation. In some embodiments, the system 10 can display two or more visualizations at once using the same time axis. Each visualization can show data from a different EAM. Each visualization can alternatively show data from the same EAM but in different data type forms, e.g., when displaying (e.g., further graphically distinguishing) non-contact data, contact data, unipolar data, and / or bipolar data.

[0142] Similarly, the system 10 can be configured to analyze spatiotemporal sequences of activation via amplitude-referenced activation area visualizations. The activation time data of the EAM can be divided into amplitude ranges. The amplitude-referenced activation areas can be calculated as areas corresponding to portions of the EAM that have activation times within each time amplitude range. Alternatively or additionally, measurements and / or number of points can be used. The system 10 can display the amplitude-referenced activation areas using visualizations similar to those described above.

[0143] The system 10 can be configured to perform cardiac information analysis, including data aggregation and statistical analysis. A single data set can be vulnerable to false positives and false negatives, especially if the selected metric can be biased solely from the measurement of the metric itself. In some embodiments, the system 10 includes a bias (e.g., a user-configurable bias) that causes the analysis to have a tendency toward and / or away from false positives and / or false negatives. The amplitude of a bipolar (one unipolar signal subtracted from another unipolar signal) signal is commonly used as a proxy to measure tissue anomalies and is typically measured only once. However, the measurement orientation, wavefront direction, and tissue rate response all affect the amplitude of the bipolar signal, making it a non-specific metric for tissue anomalies. In some embodiments, the system 10 is configured to overcome one or more of these limitations by performing multiple measurements, varying the wavefront direction and tissue rate response to remove potential inherent biases. When these multiple measurements are made (e.g., using the system 10), the specificity of detecting anomalies is improved by understanding the spatial consistency of any metric of anomaly. System 10 can be configured to create a composite from multiple measurements. For example, system 10 can perform multiple measurements (activations in one or more maps) under various conditions. Each measurement can include activity data (AD) at a common set of anatomical locations (vertices of a mesh). With multiple measurements (activations in one or more maps), each anatomical location has a composite set of data samples that can be statistically analyzed by system 10. A composite data set is an aggregation of multiple data sets into a generic analyzable structure. In some embodiments, the structure is a mesh of anatomical vertices. In some embodiments, the AD evaluated as a composite is conduction velocity. In some embodiments, the AD evaluated as a composite is signal amplitude. The data of the composite map can be statistically analyzed or evaluated using a threshold.For example, the combined data set may be used to visualize the minimum, average, maximum, and / or median conduction velocity and / or amplitude at all locations within the heart chamber. The system 10 may be configured to perform a consistency analysis. For example, the system 10 may combine the combined data by applying a threshold. For example, if a threshold value for conduction velocity (e.g., a threshold value of 0.3 m / s) is used as a threshold for abnormal conduction (typically, slower velocities are more abnormal), the combined data may be evaluated by counting conduction velocities (CVs) in the combined data set that are less than the threshold as abnormal and / or counting CVs that are greater than the threshold as normal. A consistency map may then be displayed that shows in a visually distinct manner (e.g., via a color-coded visualization) areas with consistently abnormal CVs, consistently normal CVs, or inconsistently abnormal CVs. In some embodiments, the system 10 thresholds the CVs to form the consistency map. In some embodiments, the system 10 thresholds the signal amplitudes to form the consistency map. In some embodiments, the system 10 combines a threshold value for abnormal activity between the CVs and the signal amplitudes to form the consistency map. In some embodiments, multiple metrics or thresholded metrics may be combined by the system 10 into a score for each activation, which may then be displayed in a composite map.

[0144] As described above, the system 10 can be configured to perform one or more cardiac activation analyses, such as when performing a "fusion of clinical measurements and / or computational modeling." The system 10 can be configured to perform "anatomical data co-registration," such as through a "universal anatomical model" and / or "landmark co-registration" (skeleton). The anatomical data co-registration performed by the system 10 can include the system 10 (e.g., algorithm 115) assuming a fairly consistent juxtaposition of the four chambers of the heart, and in scenarios where a subject-specific (i.e., patient-specific) orientation cannot be determined, the relative positioning of the various chambers can be determined via a population-level average (e.g., average from a sample of human subjects). The universal anatomical model of the system 10 can utilize a global cardiac positioning system to juxtapose the chambers of the heart relative to one another. The landmark co-registration of the system 10 can utilize a calculated skeleton that traces the relationships between the four chambers of the heart in 3D space, and / or a series of 2D cross sections to determine the relative orientation and positioning between the relative chambers. System 10 can be configured to perform "CV aberration / divergence modeling," such as when system 10 makes measurements (e.g., clinical pacing measurements) from one or more locations (e.g., using the supermap routine and / or single location routine described herein). Activation can be mapped and analyzed to find areas of block (e.g., isolation). System 10 can use the same cavity anatomy (e.g., as previously calculated and / or presented) and computationally apply areas of block. A "restitution score" determined by system 10 can include a score determined by analyzing conduction velocity at a wide range of sites across a range of pacing rates such that subject-specific restitution information can be developed to parameterize subject-specific simulations or to compare the degree of change associated with restitution to a population average as an index of restitution (e.g., restitution score).The system 10 can use a model of the simulated propagation to calculate the activation sequence throughout the cavity. The model can be isotropic. In some embodiments, the model is parameterized by the system 10 (e.g., by the algorithm 115) based on the measured activation, so that the system 10 can start the simulation from various points of the measured activation. For example, the system 10 can use the first 10% of the measured activation and compare this to the remaining 90% of the measured activation for the simulation. This process can bring out many properties of the propagation that show divergence, such as anisotropy, which may be due to fiber orientation or substrate-related changes. Alternatively or additionally, the model can be anisotropic (e.g., with heterogeneous properties) and / or the model can be determined based on population averages and / or population atlases. The heterogeneity of the model can be based on standard models and / or data from one or more measurements, e.g., data obtained from CT and / or MRI. For example, the system 10 can use a fibrosis score and / or an arrhythmogenic score based on spatial organization and / or substrate-specific changes to intensity in the CT and / or MRI data. These intensity changes can be derived via contrast agents or through standard imaging regimes and / or analysis of clinical maps, e.g., composite maps of conduction velocity. System 10 can use a series of conduction velocity maps to estimate fiber orientation across a heart chamber (e.g., atrial body), and then use these estimates to generate anisotropic simulations that account for fiber orientation. In some embodiments, differences between anisotropic simulations and measurements tend to indicate substrate-related differences that are not accounted for by the simulation framework of system 10. System 10 can compare clinical pacing maps to simulated propagation to determine differences in conduction behavior. System 10 can calculate directional divergence between clinical and simulated maps to indicate preferential conduction directions (e.g., directions that may exist from anisotropic fiber orientation) or substrate-related changes.As described above, the estimates provided by the simulation framework of system 10 (itself parameterized by EAM or ensemble average) can be compared to the measurements themselves to determine areas of maximum divergence that may indicate abnormal tissue. System 10 can calculate anomalies in the clinical map (e.g., anomalies that differ from the simulation framework in terms of velocity, recovery, onset, breakthrough, and / or other propagation-related phenomena). System 10 can perform simulated pace mapping to track the delivered therapy and all therapy parameters, model the effect of the delivered therapy on local conduction (e.g., local conduction cannot be altered, can be partially / moderately altered, or can be completely removed (no conduction)), and / or find gaps to simulate the onset of activation from one or more regions associated with the delivered therapy. For example, based on one or more system 10 thresholds (e.g., user-defined thresholds), a set of standard settings, or a combination of the two, system 10 can generate electrical propagation simulations (e.g., subject-specific, rule-based, or a combination thereof) to track the delivered therapy. Additionally, system 10 can be configured to model the effect of the delivered therapy on local conduction. Local conduction may be unaltered, partially and / or otherwise moderately altered, or completely eliminated (e.g., no conduction), and thus may be parameterized by system 10 (e.g., by algorithm 115) in physiological simulations as described herein. The parameterization performed by system 10 may take into account default geometric parameters (e.g., tissue thickness), electrophysiological parameters (e.g., restitution curve data), and / or anatomical measurement data.

[0145] The system 10 can include a "data management architecture." The data management architecture (herein, DMA) of the system 10 can include an arrangement in which case information (e.g., information related to clinical procedures captured and / or being performed on a patient) is stored spatiotemporally. Roughly speaking, the DMA can include a regular volumetric grid large enough to encompass the four typical heart chambers. The architecture can be formed as a regular structured rectilinear grid, an unstructured tetrahedral grid, and / or an unstructured hexahedral grid. The data storage can include one or more cross-referenced data spaces, and / or a hierarchy of data spaces (e.g., parent, peer, child, etc.). The system 10 can perform data processing in each data space. The system 10 can recalculate (e.g., refactor) data of a "child" data space, such as recalculating based on a "parent" data space. The system 10 can provide visualization representations of these various data (e.g., measured data, determined data, and / or calculated data).

[0146] Therapeutic and navigational information can be stored by the system 10 on an element-by-element basis, such as for further processing and / or analysis performed by the system 10 and / or an operator using the system 10. One or more cross-referenced data spaces, such as domains within a DMA, can be computationally related to one another to facilitate mapping of the two cavities. In some embodiments, it may be appropriate for the geometry to be more displaced than may be physiologically appropriate, but the system 10 can be configured to constrain the biophysical activity to reflect the true physiological configuration. These data spaces can be provided (e.g., displayed), obscured, highlighted, and / or augmented by the system 10 (e.g., upon request by a user of the system 10).

[0147] There may be a hierarchy of data spaces (e.g., in a parent, peer, child arrangement) within the data space, whereby some independent geometries may contain dependent properties that may be inherited based on inspection in one geometry (e.g., in a portion of the geometry) but not in another geometry. These computational property inheritance mechanisms may be directed and facilitated by the system 10 through the relationships of the various geometries to one another. For example, the left and right atria may have a peer relationship with relatively little inheritance of properties. Alternatively, the left atrial appendage may be classified by the system 10 as a child of the left atrium, and the parent-child relationship may have a much more substantial inheritance. Data processing in each data space may utilize DMA to regularize and constrain data manipulations to facilitate analysis and visualization from the whole-chamber level to the whole-organ level. For example, DMA may be used to constrain a global electrical solution across both atrial geometries, constrain geometric operations to perform "shaving" of the heart chambers (e.g., one or both atrial bodies), and / or solve piecewise inverse problems that optimize for both atria.

[0148] Refactoring data in a "child" data space based on a "parent" data space can include changes based on certain criteria, and system 10 (e.g., by algorithm 115) can delegate certain parameters to peers and / or children of a particular geometry, where the delegation is based on changes in measurements and / or parameters in that geometry and / or anatomical structure.

[0149] The visualization of data by system 10 may include a volumetric grid used to facilitate 3D visualization that may only be partially related to the underlying cardiac mesh. These visual elements may be scalar, vector, matrix, and / or tensor based visualization and / or analysis. Additionally, the visual elements provided by system 10 may utilize variations in color, size, shape, and / or other variable graphical parameters to imply characteristics of tissues, organs, and / or the accuracy and / or confidence of a given metric.

[0150] Establishing a finite element method (FEM) framework for subject-specific or atlas-based simulations can include the use of structured and unstructured meshes including DMA and a series of FEM-based analyses. Within DMA, system 10 can perform bidomain, monodomain, pseudo-bidomain, eikonal, reactive eikonal, and courtemanche type simulations, such as when the simulations are performed by system 10 on one or more of the anatomical cavity models generated by system 10. These computational models can be parameterized by subject-specific ADs and / or the models can be strictly rule-based and / or population-based.

[0151] The system 10 can be configured to perform event-driven user interface control and data presentation. The system 10 can provide "data elements" of information throughout a clinical procedure, which the system 10 can track, such as when tracking one, two, or more data elements selected from the group consisting of recorded signals, user actions, clinical events, EAMs, delivered therapies, rhythm classifications, beat groups, beat characteristics, and combinations thereof. Each data element can be stored with time information (e.g., a timestamp). The data elements can be displayed in a chronological timeline. Data elements of different data types (e.g., as classified by the algorithms 115 of the system 10) can be displayed in a synchronous manner on the timeline. For example, data collections, beat groups, EAMs created, delivered therapies, markers, collected anatomical structures, case events, and / or time calipers can be shown in different parallel tracks in the synchronous timeline. Each track may be graphically differentiated (e.g., color-coded or otherwise graphically differentiated) to correspond to a user interface representation used in and / or for the rest of the system 10. Operation of the system 10 may be initiated via one or more specific user actions (e.g., clicking an icon or pressing a hotkey), which may at least log a time and / or mark a fiducial visualization representation in the timeline as an "event." Further information may be added to the event, such as a text label and / or notes. The event may be modified to other forms of data, including time calipers, EAMs, beat labels, beat groups, anatomical markers, and / or labels. During review, a user (e.g., a clinician or other operator) may return to an event on the timeline and observe the system environment (e.g., system parameter levels, patient physiological data, and / or other information) present and / or otherwise relevant to that moment in the clinical procedure.Actions of the system 10 initiated through a user interface of the system 10 can also be logged as events on the timeline, with corresponding attributes of the action automatically applied to the event. User actions and modifications, including actions and modifications to application settings (e.g., changes), calculation parameters, applied filters, creation of data entities (such as anatomical components, beat groups, text labels, and / or graphical markers), modifications to anatomical data (e.g., trimming, cutting, and / or adding anatomical structures), and the like, can also be stored as events. Event information can also be displayed as a corresponding log or list. In some embodiments, the time series representation of the event data can be used to undo or redo user actions through a user interface, such as a graphical user interface (GUI) of the system 10 as described herein, i.e., GUI 125, such as by dragging an indicator to exclude a previous user action or clicking to delete a previous action. Undoing a previous event can be performed for a consecutive set of events leading up to the current state of the system. Events may be deleted asynchronously, and if deleting an event asynchronously requires also deleting related events, the system 10 may notify the user before deletion and visually designate the related events.

[0152] The following Figures 2-8B show various examples of the inventive concepts discussed above.

[0153] Referring now to FIG. 2, one embodiment of an anatomical model representing a tissue surface is shown, consistent with the concepts of the present invention. The system 10 can be configured to display an anatomical model representing a wall of a heart chamber and / or another tissue surface of a patient. The anatomical model can be displayed to a user (e.g., a patient's clinician) via a user interface, such as via the GUI 125 shown and described herein. The portions of the anatomical model can include different graphical characteristics, such as different color intensities, opacities, or another variable graphical parameter used to differentiate data. The different characteristics can represent variations in the anatomical model, for example, variations in density of data collected to compute the portions of the anatomical model. In some embodiments, the system 10 can display one or more points proximate the anatomical model, such as one or more points representing data collected by the system 10. For example, the system 10 can display points representing ultrasound data points collected using one or more devices configured to transmit and / or receive ultrasound signals (as described herein).

[0154] 3, an example of a graphical user interface for displaying cardiac mapping data consistent with the concepts of the present invention is shown. System 10 can be configured to provide a graphical user interface, such as GUI 125 shown. GUI 125 can include multiple display regions configured to present information collected and / or calculated by system 10 to a user. Information can be displayed to a user via one or more graphical representations, such as representations selected from the group consisting of visual models, such as 2D and / or 3D models, graphs, charts, timelines, overlays, icons, other visual representations of data (e.g., device geometry, orientation, and / or other position data of system 10), and combinations thereof. GUI 125 can display information related to one or more beat groups identified by system 10 as described herein. In some embodiments, beat groups can be graphically differentiated (e.g., color-coded) based on one or more unique characteristics of the beat groups, such as morphology, cycle length, time sequence, energy profile, and / or one or more other characteristics. In some embodiments, each beat group identified by system 10 is indexed (e.g., assigned an integer value). In some embodiments, one or more recorded beats that are not assigned to a beat group may be displayed with a unique graphical characteristic (eg, a unique color and / or pattern).

[0155] In some embodiments, the GUI 125 includes a "timeline view," as shown in FIG. 3. The timeline view can display various data types against a timeline (e.g., parallel to a timeline). For example, the timeline view can indicate when data was collected and / or for what period of time results were calculated. The timeline view can display indicators representing data selected from the group consisting of indicators of time segments during which mapping data was recorded and / or processed by the system 10 (e.g., when beat grouping was performed), indicators of time segments during which cardiac activity maps were created, indicators of when therapy was provided to the patient, indicators of time segments during which anatomical data was collected by the system 10, indicators of when the displayed data was annotated (e.g., annotations made by a clinician via a user interface of the system 10), and combinations thereof.

[0156] In some embodiments, the GUI 125 includes an "activation area" as shown. The activation area may display a histogram representation of a time-referenced activation plot, such as a plot showing the number of points and / or area of ​​a heart chamber that are activating versus time. Alternatively or additionally, the activation display area may display a histogram representation of an amplitude-referenced activation plot, such as a plot showing activation amplitude range versus points and / or area of ​​a heart chamber.

[0157] 4A and 4B, an example of a graphical user interface including an anatomical model and an anatomical model including various markers, respectively, are shown, consistent with the concepts of the present invention. The system 10 can be configured to provide a graphical user interface including an anatomical model, such as the GUI 125 shown. The GUI 125 can include (e.g., provide) graphical data, such as cardiac activity data shown in graphical format, as shown. In FIG. 4A, beats (e.g., heart beats) identified by the system 10 are correspondingly identified (e.g., differentiated) by white borders in the graph data, as shown. The anatomical model can include a map of activation times (e.g., a color map of various grayscales, as shown, and / or a color map of various other color schemes). One or more visual markers can be displayed on the anatomical model, such as a visual marker indicating a point on the anatomical model where the earliest activation was recorded. In FIG. 4B, additional visual markers are displayed. In some embodiments, the size of the visual markers displayed on the anatomical model can be correlated to the precision of the data represented by the markers.

[0158] 5A and 5B, a flow diagram and a representation of cardiac activity data, respectively, are shown illustrating one embodiment of a machine learning and / or other AI-based method of rhythm classification consistent with the concepts of the present invention. Cardiac signals (e.g., unipolar EGM signals recorded from the coronary sinus) can be recorded and classified as representative of various cardiac rhythms to be identified by a "rhythm classification algorithm" (e.g., algorithm 115, including an AI algorithm or other algorithm configured to perform rhythm classification). The cardiac rhythm to be identified can be selected from the group consisting of sinus rhythm, atrial flutter, atrial fibrillation, paced rhythm, and combinations thereof. In some embodiments, the recorded signals are downsampled. The classified group of recorded cardiac data can be converted by system 10 into a wavelet scalogram (e.g., decomposed into different frequency bands while preserving time information). On the wavelet scalogram, an algorithm, such as an AI algorithm including a convolutional neural network (e.g., algorithm 115 described herein), can be trained to identify the cardiac rhythm. In some embodiments, the performance of the AI ​​algorithm can be evaluated using validation data to determine the classification accuracy and / or positive predictive value of the algorithm.

[0159] Referring now to FIG. 6, various embodiments of an anatomical model on which a cardiac activity map is displayed are shown, consistent with the concepts of the present invention. In some embodiments, the system 10 calculates conduction velocity based on cardiac activation time. The lower left portion of FIG. 6 shows a map of cardiac activation time. The lower center portion of FIG. 6 shows a map of conduction velocity. The system 10 can identify spatiotemporal patterns throughout the cardiac tissue. These spatiotemporal patterns can be displayed relative to one or more anatomical models, as shown in the right portion of FIG. 6. In some embodiments, the spatiotemporal patterns are displayed as colorized (e.g., using grayscale variations as shown, or other arrangements of color variations) histograms on the surface of the anatomical model. In the example shown in FIG. 6, lighter and more opaque colors represent a higher prevalence of each pattern displayed. Furthermore, in the illustrated example, the patterns shown include a focal pattern (A), a rotational pattern (B), and a complex circling and reentry pattern (C).

[0160] Referring now to FIG. 7, various embodiments of an anatomical model on which a map of cardiac activity is displayed are shown, consistent with the concepts of the present invention. In some embodiments, the cardiac activity map generated by the system 10 may indicate the average velocity of conduction through cardiac tissue. In FIG. 7, a region of slow conduction (e.g., less than 0.3 m / s) in which 50 activations occurred is identified based on data collected over a 5-7 second period. In some embodiments, multiple cardiac activity maps may be aggregated by the system 10 to display an associated composite map.

[0161]

[0031] Referring now to Figure 8A, an embodiment of a spatiotemporal representation of cardiac activity is shown, consistent with the concepts of the present invention. In Figure 8, the major axis of the graph represents time (e.g., the time axis). In some embodiments, the graph includes a 2D representation of a 3D map of cardiac electrical activity data (e.g., an EAM as described herein). Referring further to Figure 8B, an embodiment of a color-coded graph of cardiac activity is shown, consistent with the concepts of the present invention. In some embodiments, the spatiotemporal representation graph can be analyzed by system 10 for repeating sequences (e.g., repeating sequences of cardiac activity). Identified repeating sequences can be color-coded as shown.

[0162] The above-described embodiments should be understood to serve only as illustrative examples, and further embodiments are envisioned. Any feature described herein in relation to any one embodiment can be used alone or in combination with other features described, and can also be used in combination with one or more features of any other of the embodiments, or in any combination of any other of the embodiments. Moreover, equivalents and modifications not described above can also be employed without departing from the scope of the inventive concept as defined in the appended claims.

Claims

1. An analysis system for cardiac tissue, comprising: an electrode configured to record a dataset of potential data representing cardiac activity at a plurality of time intervals; a processor configured to aggregate the plurality of recorded potential data representing cardiac activity for coherence analysis of the data using conduction velocity data; a display configured to display a visual representation of the coherence analysis to an operator; An analysis system for cardiac tissue, characterized by comprising the above components.

2. The system according to claim 1, wherein the potential data includes measurements taken while varying wavefront direction and tissue rate response related to the cardiac activity. The system is characterized by this.

3. The system according to claim 2, wherein each measurement is activity data in a common set of anatomical structure locations. The system is characterized by this.

4. The system according to claim 3, wherein the processor is configured to form a composite of conduction velocity data from a plurality of measurements in a common set of the patient's anatomical structure locations. The system is characterized by this.

5. The system according to claim 4, wherein the processor is configured to analyze the composite conduction velocity data to depict a minimum conduction velocity, an average conduction velocity, a maximum conduction velocity, or a central conduction velocity. The system is characterized by this.

6. The system according to claim 5, wherein the processor is configured to use a threshold value to evaluate a conduction velocity smaller than the threshold value as abnormal and display a coherence map that displays regions that are consistently abnormal, normal, or abnormally inconsistent. The system is characterized by this.

7. The system according to claim 6, wherein the processor is configured to perform anatomical data registration. The system is characterized by this.

8. The system according to claim 7, wherein the processor is configured to perform conduction velocity divergence / convergence modeling. The system is characterized by this.

9. The system according to claim 8, wherein the processor is configured to determine a recovery score, whereby conduction velocities at a wide range of sites over various pacing rates are analyzed. The system is characterized by this.

10. A method for analyzing cardiac tissue, comprising: Recording, by an electrode, a data set of potential data representing cardiac activity at a plurality of time intervals; Aggregating, by a processor, the plurality of recorded potential data representing cardiac activity for coherence analysis of the data using conduction velocity data; Displaying, by a display, a visual representation of the coherence analysis to an operator; A method for analyzing cardiac tissue, comprising: **Claim 11**: The method according to claim 10, wherein the potential data includes measurements taken while varying a wavefront direction and tissue rate response related to the cardiac activity, characterized in that. **Claim 12**: The method according to claim 11, wherein each measurement is activity data in a common set of anatomical structure locations, characterized in that. **Claim 13**: The method according to claim 12, wherein the processor forms a composite of conduction velocity data from a plurality of measurements in a common set of anatomical structure locations of a patient, characterized in that. **Claim 14**: The method according to claim 13, wherein the processor analyzes the composite conduction velocity data to depict a minimum conduction velocity, an average conduction velocity, a maximum conduction velocity, or a central conduction velocity, characterized in that. **Claim 15**: The method according to claim 14, wherein the processor uses a threshold to evaluate a conduction velocity less than the threshold as abnormal and displays a coherence map that displays regions that are consistently abnormal, normal, or abnormally inconsistent, characterized in that. **Claim 16**: The method according to claim 15, wherein the processor performs anatomical data registration, characterized in that. **Claim 17**: The method according to claim 16, wherein the processor performs conduction velocity divergence / convergence modeling and determines a recovery score, whereby conduction velocities at a wide range of sites over a variety of pacing rates are analyzed, characterized in that. **Claim 18**: A treatment system for a patient's tissue, comprising: A first energy delivery device including a first energy delivery element configured to be positioned proximate to the target tissue of the patient; A second energy delivery element; An energy delivery console configured to provide energy between the first energy delivery element and the second energy delivery element; An electrode configured to record a dataset of potential data representing cardiac activity at a plurality of time intervals, A processor configured to aggregate a plurality of recorded potential data representing cardiac activity for coherence analysis of the data using conduction velocity data, A display configured to display a visual representation of the coherence analysis to an operator, A treatment system for a patient's tissue, characterized by comprising the above.

19. The treatment system according to claim 18, The potential data includes measured values measured while changing the wavefront direction and tissue rate response related to the cardiac activity, Each measured value is activity data in a common set of positions on the anatomical structure, The processor is configured to form a composite of conduction velocity data from a plurality of measured values in a common set of positions on the patient's anatomical structure, A treatment system characterized by this.

20. The treatment system according to claim 19, The processor is configured to analyze the composite conduction velocity data to depict the minimum conduction velocity, average conduction velocity, maximum conduction velocity, or central conduction velocity, The processor is configured to use a threshold value to evaluate a conduction velocity smaller than the threshold value as abnormal and display a coherence map indicating an area with consistent abnormality, a normal area, or an area with inconsistent abnormality, The processor is configured to perform conduction velocity divergence / convergence modeling, and the processor is configured to determine a recovery score, whereby the conduction velocity at a wide range of sites over various pacing rates is analyzed, A treatment system characterized by this.

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