System for identifying cardiac conduction patterns

Abstract

To diagnose an arrhythmia of a patient.SOLUTION: A system 100 for diagnosing an arrhythmia of a patient comprises: a diagnostic catheter 10 for insertion into the heart of the patient, the diagnostic catheter 10 configured to record anatomical and electrical activity data of the patient; and a processing unit. The processing unit is configured to receive the recorded electrical activity data 120, and correlate the electrical activity data 120 with the anatomic data. The processing unit comprises an algorithm configured to analyze the electrical activity at a location correlated with the anatomic data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] This application claims priority under 35 U.S.C. §119(e) to 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 / 668,647, filed May 8, 2018, entitled "System for Identifying Cardiac Conduction Patterns," each of which is incorporated herein by reference in its entirety.

[0002] This application does not claim priority, but may be related to U.S. Provisional Patent Application No. 62 / 757,961, entitled "Systems and Methods for Calculating Patient Information," filed November 9, 2018, which is incorporated herein by reference.

[0003] This application does not claim priority, but may be related to U.S. Provisional Patent Application No. 62 / 668,659, entitled "Cardiac Information Processing System," filed May 8, 2018, which is incorporated herein by reference.

[0004] This application does not claim priority, but may be related to U.S. patent application Ser. No. 16 / 097,959, filed October 31, 2018, entitled "Cardiac Mapping System with Efficiency Algorithm," which is a national stage application under 35 U.S.C. § 371 of Patent Cooperation Treaty Application No. PCT / US2017 / 030922, filed May 3, 2017, entitled "Cardiac Mapping System with Efficiency Algorithm," which is a national stage application under 35 U.S.C. § 371 of Patent Cooperation Treaty Application No. PCT / US2017 / 030922, filed October 26, 2016, entitled "Cardiac Mapping System with Efficiency Algorithm," and U.S. provisional patent application Ser. No. 62 / 413,104, filed October 26, 2016, entitled "Cardiac Mapping System with Efficiency Algorithm," and U.S. provisional patent application Ser. No. 62 / 413,104, filed May 3, 2016, entitled "Cardiac Mapping System with Efficiency Algorithm," which is a national stage application under 35 U.S.C. § 371 of Patent Cooperation Treaty Application No. PCT / US2017 / 030922, filed May 3, 2017 ... This application claims priority to U.S. Provisional Patent Application No. 62 / 331,364, entitled "Primitive Neural Network Algorithm," each of which is incorporated herein by reference.

[0005] This application does not claim priority, but may be related to U.S. Patent Application No. 16 / 097,955, entitled "Cardiac Information Dynamic Display System and Method," filed October 31, 2018, which is a national stage application under 35 U.S.C. § 371 of Patent Cooperation Treaty Application No. PCT / US2017 / 030915, entitled "Cardiac Information Dynamic Display System and Method," filed May 3, 2017, which claims priority to U.S. Provisional Patent Application No. 62 / 331,351, entitled "Cardiac Information Dynamic Display System and Method," filed May 3, 2016, each of which is incorporated herein by reference.

[0006] This application does not claim priority, but may be related to Patent Cooperation Treaty Application No. PCT / US2017 / 056064, entitled "Ablation System with Force Control," filed October 11, 2017, which claims priority to U.S. Provisional Patent Application No. 62 / 406,748, entitled "Ablation System with Force Control," filed October 11, 2016, and U.S. Provisional Patent Application No. 62 / 504,139, entitled "Ablation System with Force Control," filed May 20, 2017, each of which is incorporated herein by reference.

[0007] This application does not claim priority, but may be related to U.S. patent application Ser. No. 15 / 569,457, filed Oct. 26, 2017, entitled "Localization System and Method Useful in the Acquisition and Analysis of Cardiac Information," which is a national stage application under 35 U.S.C. § 371 of Patent Cooperation Treaty Application No. PCT / US2016 / 032420, filed May 13, 2016, entitled "Localization System and Method Useful in the Acquisition and Analysis of Cardiac Information," which claims priority to U.S. Provisional Patent Application Ser. No. 62 / 161,213, filed May 13, 2015, entitled "Localization System and Method Useful in the Acquisition and Analysis of Cardiac Information," each of which is incorporated herein by reference.

[0008] This application does not claim priority, but may be related to U.S. patent application Ser. No. 15 / 569,231, entitled "Cardiac Virtualization Test Tank and Testing System and Method," filed Oct. 25, 2017, which is a national stage application under 35 U.S.C. § 371 of Patent Cooperation Treaty Application No. PCT / US2016 / 031823, entitled "Cardiac Virtualization Test Tank and Testing System and Method," filed May 11, 2016, which claims priority to U.S. Provisional Patent Application Ser. No. 62 / 160,501, entitled "Cardiac Virtualization Test Tank and Testing System and Method," filed May 12, 2015, each of which is incorporated herein by reference.

[0009] This application does not claim priority, but may be related to U.S. patent application Ser. No. 15 / 569,185, entitled "Ultrasound Sequencing System and Method," filed Oct. 25, 2017, which is a national stage application under 35 U.S.C. § 371 of Patent Cooperation Treaty Application No. PCT / US2016 / 032017, entitled "Ultrasound Sequencing System and Method," 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.

[0010] This application does not claim priority, but may be related to U.S. patent application Ser. No. 14 / 916,056, entitled "Devices and Methods for Determination of Electrical Dipole Densities on a Cardiac Surface," filed Sep. 10, 2014, which is a national stage application under 35 U.S.C. § 371 of Patent Cooperation Treaty Application No. PCT / US2014 / 54942, entitled "Devices and Methods for Determination of Electrical Dipole Densities on a Cardiac Surface," filed Sep. 10, 2014, which claims priority to U.S. Provisional Patent Application Ser. No. 61 / 877,617, entitled "Devices and Methods for Determination of Electrical Dipole Densities on a Cardiac Surface," filed Sep. 13, 2013, each of which is incorporated herein by reference.

[0011] This application does not claim priority, but may be related to U.S. patent application Ser. No. 15 / 128,563, 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 No. PCT / US2015 / 22187, entitled "Cardiac Analysis User Interface System and Method," filed Mar. 24, 2015, which claims priority to U.S. Provisional Patent Application No. 61 / 970,027, entitled "Cardiac Analysis User Interface System and Method," filed Mar. 28, 2014, each of which is incorporated herein by reference.

[0012] This application does not claim priority, 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. Patient No. 10,071,227, entitled "Gas-Elimination Patient Access Device," filed Jan. 14, 2015, which is a national stage application under 35 U.S.C. § 371 of Patent Cooperation Treaty Application No. 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.

[0013] This application does not claim priority, but may be related to U.S. patent application Ser. No. 16 / 242,810, filed January 8, 2019, entitled "Expandable Catheter Assembly with Flexible Printed Circuit Board (PCB) Electrical Pathways," which is a continuation of patent application Ser. No. 14 / 762,944, filed July 23, 2015, entitled "Expandable Catheter Assembly with Flexible Printed Circuit Board (PCB) Electrical Pathways," which is a national stage application under 35 U.S.C. § 371 of Patent Cooperation Treaty application Ser. No. PCT / US2014 / 15261, filed February 7, 2014, entitled "Expandable Catheter Assembly with Flexible Printed Circuit Board (PCB) Electrical Pathways," which is a continuation of patent application Ser. No. PCT / US2014 / 15261, filed February 8, 2013, entitled "Expandable Catheter Assembly with Flexible Printed Circuit Board (PCB) Electrical Pathways." This application claims priority to U.S. Provisional Patent Application No. 61 / 762,363, entitled "PCB Electrical Pathways," each of which is incorporated herein by reference.

[0014] This application does not claim priority, but may be related to U.S. patent application Ser. No. 16 / 012,051, 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 No. PCT / US2013 / 057579, entitled "System and Method for Diagnosing and Treating Heart Tissue," published as WO 2014 / 036439, 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, but may be related to U.S. Design Patent Application No. 29 / 593,043, entitled "Set of Transducer-Electrode Pairs for a Catheter," filed February 6, 2017, which is a divisional application of U.S. Design Patent No. D782,686, entitled "Transducer Electrode Arrangement," filed December 2, 2013, which is a continuation-in-part of Patent Cooperation Treaty Application No. PCT / US2013 / 057579, entitled "Catheter System and Methods of Medical Uses of Same, Including Diagnostic and Treatment Uses for the Heart," filed August 30, 2013, which is incorporated herein by reference.

[0016] This application does not claim priority, but may be related to U.S. patent application Ser. No. 15 / 926,187, 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. 9,968,268, 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. 9,757,044, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," filed March 9, 2012. This application is a national stage application under 35 U.S.C. § 371 of Patent Cooperation Treaty Application No. PCT / US2012 / 028593 entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," which claims priority to U.S. Provisional Patent Application No. 61 / 451,357, filed March 10, 2011, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," each of which is incorporated herein by reference.

[0017] This application does not claim priority, but may be related to U.S. patent application Ser. No. 15 / 882,097, 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. 9,504,395, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," filed October 19, 2015, which is a continuation of U.S. Patent No. 9,504,395, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," filed July 19, 2013. No. 9,192,318, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," which issued on August 20, 2013, is a continuation of U.S. Patent No. 8,512, entitled "Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall."This application is a continuation of No. 255, which is a national stage application under 35 U.S.C. 371 of Patent Cooperation Treaty Application No. PCT / IB09 / 00071, entitled "A Device and Method for the Geometric Determination of Electrical Dipole Densities on the Cardiac Wall," filed January 16, 2009, which claims priority to Swiss Patent Application No. 00068 / 08, filed January 17, 2008, each of which is incorporated herein by reference.

[0018] This application does not claim priority, but may be related to U.S. patent application Ser. No. 16 / 014,370, 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 application Ser. No. 15 / 435,763, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls," filed February 17, 2017, which is a continuation of U.S. Patent No. 9,610,024, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls," filed September 25, 2015, which is a continuation of U.S. Patent No. 9,610,024, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls," filed November 19, 2014. No. 9,167,982, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls," which issued on December 23, 2014, is a continuation of U.S. Patent No. 8,918,158, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls," which issued on April 15, 2014.No. 119, which is a continuation of U.S. Patent No. 8,417,313, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls," issued April 9, 2013, which is a national stage application under 35 U.S.C. § 371 of PCT Application No. CH2007 / 000380, entitled "Method and Device for Determining and Presenting Surface Charge and Dipole Densities on Cardiac Walls," filed August 3, 2007, which claims priority to Swiss Patent Application No. 1251 / 06, filed August 3, 2006, each of which is incorporated herein by reference.

[0019] The present invention relates generally to systems and methods that may be useful in the diagnosis and treatment of cardiac arrhythmias or other abnormalities, and in particular, the present invention relates to systems, devices, and methods that are useful for displaying cardiac activity relevant to the diagnosis and treatment of such arrhythmias or other abnormalities. [Background technology]

[0020] Cardiac signals (e.g., charge density, dipole density, voltage, etc.) vary in magnitude across the endocardial surface. The magnitude of these signals depends on several factors, including local tissue characteristics (e.g., healthy vs. diseased / scar / fibrosis / lesion) and regional activation characteristics (e.g., the "electric mass" of active tissue prior to local cell activation). A common approach is to always assign a single threshold to all signals across the surface. The use of a single threshold can lead to the disappearance of low-amplitude activations or the dominance / saturation of high-amplitude activations, leading to confusion in map interpretation. Failure to properly detect activations can lead to inaccurate identification of regions of interest for therapy delivery or incomplete characterization of ablation effectiveness (excess or absence of block).

[0021] Continuous global mapping of atrial fibrillation generates a vast array of temporally and spatially variable activation patterns. Limited, individual sampling of map data may be insufficient to provide a comprehensive picture of arrhythmia drivers, mechanisms, and supporting substrates. Clinician review of AF over time may require effort to piece together and recall the complete "big picture."

[0022] For these and other reasons, there is a general need to algorithmically provide objective analysis of conduction patterns. [Prior art documents] [Patent documents]

[0023] [Patent Document 1] U.S. Patent No. 10,071,227 Summary of the Invention [Problem to be solved by the invention]

[0024] Embodiments of the systems, devices, and methods described herein may be directed to systems, devices, and methods for diagnosing arrhythmias in a patient. [Means for solving the problem]

[0025] In accordance with one aspect of the inventive concept, a system for diagnosing arrhythmia in a patient includes a diagnostic catheter for insertion into the patient's heart and a processing unit. The diagnostic catheter is configured to record anatomical and electrical activity data of the patient. The processing unit is configured to receive the recorded electrical activity data and associate the electrical activity data with the anatomical data. The processing unit includes an algorithm configured to determine conduction velocity of a depolarizing conducted wave at a location associated with the anatomical data.

[0026] In accordance with one aspect of the inventive concept, a system for diagnosing arrhythmia in a patient includes a diagnostic catheter for insertion into the patient's heart, the diagnostic catheter configured to record anatomical and electrical activity data of the patient, the processing unit configured to receive the recorded electrical activity data and associate the electrical activity data with the anatomical data, and the processing unit includes an algorithm configured to identify rotational conduction at locations associated with the anatomical data.

[0027] In accordance with one aspect of the inventive concept, a system for diagnosing arrhythmia in a patient includes a diagnostic catheter for insertion into the patient's heart, the diagnostic catheter configured to record anatomical and electrical activity data of the patient, the processing unit configured to receive the recorded electrical activity data and associate the electrical activity data with the anatomical data, and the processing unit includes an algorithm configured to identify irregular conduction at locations associated with the anatomical data.

[0028] In accordance with one aspect of the inventive concept, a system for diagnosing arrhythmia in a patient includes a diagnostic catheter for insertion into the patient's heart, the diagnostic catheter configured to record anatomical and electrical activity data of the patient, the processing unit configured to receive the recorded electrical activity data and associate the electrical activity data with the anatomical data, and the processing unit includes an algorithm configured to identify focal activation at a location associated with the anatomical data.

[0029] According to one aspect of the inventive concept, a system for generating a diagnostic result related to a cardiac condition of a patient includes a diagnostic catheter for insertion into the patient's heart, the diagnostic catheter configured to record electrical activity data of the patient at a plurality of recording locations, and a processing unit for receiving the recorded electrical activity data. The system further includes an algorithm configured to perform a complexity assessment using the recorded electrical activity data and to generate a diagnostic result based on the complexity assessment.

[0030] In some embodiments, the diagnostic result comprises an assessment of complexity or an assessment of variation in complexity over time and / or space. The diagnostic result may include variation in complexity over time and space.

[0031] In some embodiments, the complexity assessment includes a macro-level complexity assessment.

[0032] In some embodiments, the complexity assessment represents an assessment of a portion of a heart chamber, the plurality of recording locations includes at least three recording locations within the heart chamber, the system determines calculated electrical activity data for at least three apexes of the heart wall, and the calculation is based on the electrical activity data recorded at the at least three recording locations. The at least three recording locations may include at least three locations on the heart wall. The portion of the heart chamber may be within 7 cm of the surface of the heart wall. 2 Below, 4cm 2 Less than or equal to 1 cm 2The at least three recording locations may include at least one location offset from the heart wall.

[0033] In some embodiments, the complexity assessment represents an assessment of a portion of a heart chamber, the plurality of recording locations includes at least 24 recording locations within the heart chamber, and the system determines calculated electrical activity data for at least 64 vertices of the heart wall, the calculation being based on the electrical activity data recorded at the at least 24 recording locations. The at least 24 recording locations can include at least 24 heart wall locations. The at least 24 recording locations can include at least 48 heart wall locations. The at least 24 recording locations can include at least 48 heart wall locations. The at least 24 recording locations can include at least 48 locations within the heart chamber. The at least 24 recording locations can include at least 64 locations within the heart chamber. The at least 64 vertices can include at least 100 vertices. The at least 64 vertices can include at least 500 vertices. The at least 64 vertices can include at least 3000 vertices. The at least 64 vertices can include at least 5000 vertices. A portion of the cardiac chamber is at least 1 cm of the surface of the cardiac wall. 2 , at least 4 cm 2 , and / or at least 7 cm 2 The portion of the heart chamber can include a portion of the atrium.

[0034] In some embodiments, the system determines calculated electrical activity data for a plurality of vertices on the heart wall, the calculation being based on electrical activity data recorded at at least three recording locations. The recorded electrical activity data can include voltage data recorded at a plurality of locations within the patient's heart chamber, the plurality of locations can include at least one location offset from the heart wall. The recorded electrical activity data can include voltage data recorded at a plurality of locations within the patient's heart chamber, the plurality of locations can include at least one location on the heart wall. The recorded electrical activity data can include voltage data recorded at a plurality of locations within the patient's heart chamber, the plurality of locations can include at least one location on the heart wall and at least one location offset from the heart wall. The processing unit can further include a second algorithm, where the recorded electrical activity data can include recorded voltage data, the second algorithm can be configured to calculate surface charge data and / or dipole density data for each of the plurality of vertices based on the recorded voltage data, and the complexity assessment can be based on the surface charge data and / or dipole density data. The processing unit may further include a third algorithm, which may be configured to convert the surface charge data and / or the dipole density data into surface voltage data, and the assessment of complexity may be based on the surface voltage data.

[0035] In some embodiments, the complexity assessment is based on electrical activity data comprising 1-10 activations.

[0036] In some embodiments, the complexity assessment is based on electrical activity data recorded over a period of 0.3 milliseconds to 2000 milliseconds. The complexity assessment can be based on electrical activity data recorded over a period of about 150 milliseconds.

[0037] In some embodiments, the complexity assessment is based on electrical activity data comprising between 3 and 3000 activations. The complexity assessment can be based on electrical activity data comprising between 10 and 600 activations. The complexity assessment can be based on electrical activity data comprising between 25 and 300 activations.

[0038] In some embodiments, the complexity assessment is based on electrical activity data recorded over a period of 0.3 seconds to 500 seconds. The complexity assessment can be based on electrical activity data recorded over a period of 1 second to 90 seconds. The complexity assessment can be based on electrical activity data recorded over a period of 4 seconds to 30 seconds.

[0039] In some embodiments, the complexity assessment is based on electrical activity data comprising between 2,000 and 300,000 activations. The complexity assessment can be based on electrical activity data comprising between 6,000 and 40,000 activations.

[0040] In some embodiments, the complexity assessment is based on electrical activity data recorded over a period of 5 minutes to 8 hours. The complexity assessment can be based on electrical activity data recorded over a period of 15 minutes to 50 minutes.

[0041] In some embodiments, the diagnostic result includes an assessment of complexity at a single heart wall location. The system may further comprise a display, where the system may provide the diagnostic result associated with an image of the patient's anatomy on the display.

[0042] In some embodiments, the diagnostic results include an assessment of complexity at multiple heart wall locations. The system may further comprise a display, where the system may provide the diagnostic results associated with an image of the patient's anatomy on the display.

[0043] In some embodiments, the diagnostic result comprises an assessment of complexity over time. The diagnostic result may comprise an assessment of complexity over a predetermined duration.

[0044] In some embodiments, the diagnostic catheter includes at least one electrode.

[0045] In some embodiments, the diagnostic catheter includes at least three electrodes.

[0046] In some embodiments, the diagnostic catheter includes at least one ultrasound transducer.

[0047] In some embodiments, the diagnostic catheter includes a plurality of splines, each spline including at least one electrode and at least one ultrasound transducer.

[0048] In some embodiments, the cardiac condition comprises arrhythmia. The cardiac condition may comprise atrial fibrillation.

[0049] In some embodiments, the cardiac condition comprises a condition selected from the group consisting of atrial fibrillation, atrial flutter, atrial tachycardia (fast cardiac rhythm), atrial bradycardia, ventricular tachycardia, ventricular bradycardia, ectopy, congestive heart failure, angina pectoris, arterial stenosis, and combinations thereof.

[0050] In some embodiments, the cardiac condition comprises a condition selected from the group consisting of heterogeneous activation, conduction, depolarization, and / or repolarization that vary in time, space, magnitude, and / or state, irregular patterns such as focal, reentry, rotational, circling, directional irregularities, and velocity irregularities, functional block, permanent block, and combinations thereof.

[0051] In some embodiments, the system is further configured to collect additional patient data, and the complexity assessment is further based on the additional patient data. The diagnostic catheter may be configured to record the additional patient data. The diagnostic catheter may include at least one sensor configured to record the additional patient data. The system may include at least one sensor configured to record the additional patient data. The at least one sensor may be configured to be inserted into the patient when recording the additional patient data. The at least one sensor may be configured to be positioned outside the patient when recording the additional patient data. The sensor may include a sensor selected from the group consisting of electrodes or other sensors for recording electrical activity, force sensors, pressure sensors, magnetic sensors, motion sensors, velocity sensors, accelerometers, strain gauges, physiological sensors, glucose sensors, pH sensors, blood sensors, blood gas sensors, blood pressure sensors, flow sensors, optical sensors, spectrometers, interferometers, measurement sensors for measuring, for example, size, distance, and / or thickness, tissue assessment sensors, and combinations thereof. The additional patient data may include mechanical, physiological, and / or functional information of the patient. The additional patient data may include data related to parameters selected from the group consisting of cardiac wall motion, cardiac wall velocity, cardiac tissue strain, magnitude and / or direction of cardiac blood flow, blood vorticity, cardiac valve mechanics, blood pressure, tissue properties such as density, tissue characteristics, and / or tissue properties that are biomarkers of tissue properties such as metabolic activity or drug uptake, tissue composition (e.g., collagen, myocardium, fat, connective tissue), and combinations thereof. The complexity assessment may include assessment of a property selected from the group consisting of tissue electromechanical delay, the ratio of the magnitude of the electrical property to the mechanical property, and combinations thereof.

[0052] In some embodiments, the system is further configured to treat arrhythmia, and the system further includes an ablation catheter for insertion into the patient's heart, the ablation catheter configured to deliver ablation energy to various locations on the heart wall. The algorithm can be configured to determine at least one ablation location, and the at least one ablation location can include one or more heart wall locations for receiving ablation energy from the ablation catheter, and the at least one ablation location can be determined based on a complexity assessment and / or a diagnostic result. The at least one ablation location can include one or more heart locations where the complexity exceeds a threshold. The at least one ablation location can include a location with the highest complexity within the plurality of determined complexity regions. The ablation catheter can be configured to deliver one or more ablation energies selected from the group consisting of electromagnetic energy, RF energy, microwave energy, thermal energy, heat energy, cryogenic energy, light energy, laser light energy, chemical energy, acoustic energy, ultrasound energy, mechanical energy, and combinations thereof. The system can further include an energy delivery unit configured to supply ablation energy to the ablation catheter. The energy delivery unit can be configured to deliver one or more ablation energies selected from the group consisting of electromagnetic energy, RF energy, microwave energy, thermal energy, heat energy, cryogenic energy, light energy, laser light energy, chemical energy, acoustic energy, ultrasound energy, and combinations thereof.

[0053] The technology described herein, together with its attributes and attendant benefits, 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. [Brief explanation of the drawings]

[0054] [Figure 1] 1 shows a block diagram of a cardiac information processing system consistent with the concepts of the present invention. [Figure 2A] 1 shows a visual representation of a data structure of a cardiac information processing system consistent with the concepts of the present invention. [Figure 2B] 1 shows a visual representation of a portion of a data structure of a cardiac information processing system consistent with the concepts of the present invention. [Figure 3] 1 shows a schematic diagram of an algorithm for performing a complexity assessment consistent with the concepts of the present invention. [Figure 3A] 1 shows a schematic diagram of an algorithm for performing a complexity assessment consistent with the concepts of the present invention. [Figure 4] FIG. 1 shows a schematic diagram of an algorithm for determining conduction velocity data consistent with the concepts of the present invention. [Figure 5] 1 shows a schematic diagram of an algorithm for determining local rotational activity consistent with the concepts of the present invention. [Figure 5A] 1 shows a graphical representation of anatomical data including a neighborhood of a vertex defined by an outer ring of the vertex, consistent with the concepts of the present invention. [Figure 5B] 1 shows a simplified diagram of a neighborhood including an outer ring of vertices arranged around a central vertex consistent with the concepts of the present invention. [Figure 5C] 1 illustrates representative anatomical structures showing propagating waves rotating around their neighbors, consistent with the concepts of the present invention. [Figure 5D] 5D shows a plot of activation times in the outer ring of apexes of FIG. 5C consistent with the concepts of the present invention. [Figure 5E] 5D shows a graph of the conduction velocity vectors of FIG. 5C consistent with the concepts of the present invention. [Figure 6] 1 shows a schematic diagram of an algorithm for determining local irregular activity consistent with the concepts of the present invention. [Figure 6A] 1 shows an example of a propagating wave exhibiting irregular activity consistent with the concepts of the present invention. [Figure 7] FIG. 1 shows a schematic diagram of an algorithm for determining focal activation consistent with the concepts of the present invention. [Figure 7A] 1 shows representative anatomical structures that exhibit focal activation consistent with the concepts of the present invention. [Figure 7B] 1 shows representative anatomical structures that exhibit focal activation consistent with the concepts of the present invention. [Figure 8] 1 illustrates a display on which cardiac data may be rendered, consistent with the concepts of the present invention. [Figure 9] 1 shows a schematic diagram of a mapping catheter consistent with the concepts of the present invention. [Figure 9A] 1 shows a perspective anatomical view of a heart chamber with a mapping catheter inserted therein, consistent with the concepts of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0055] 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 elements. However, the description is not intended to limit the disclosure to the particular embodiments, but should be construed to include various modifications, equivalents, and / or alternatives to the embodiments described herein.

[0056] The terms "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"), when used herein, specify the presence of stated features, integers, steps, operations, elements, and / or components, but are understood not to exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0057] While terms such as first, second, and third may be used herein to describe various limits, elements, components, regions, layers, and / or sections, it is further understood that these limits, elements, components, regions, layers, and / or sections are not intended to be limited by these terms. These terms are used only to distinguish one limit, element, component, region, layer, or section from another limit, element, component, region, layer, or section. Thus, a first limit, element, component, region, layer, or section discussed below could be referred to as a second limit, element, component, region, layer, or section without departing from the disclosure of this application.

[0058] When an element is said to be "on," "mounted," "connected," or "coupled" to another element, it is further understood that it may be directly on, on, or connected or coupled to the other element, or one or more intervening elements may be present. In contrast, when an element is said to be "directly on," "directly mounted," "directly connected," or "directly coupled" to another element, there are no intervening elements present. Other words used to describe relationships between elements should be interpreted in a similar manner (e.g., "between" vs. "directly between," "adjacent" vs. "directly adjacent," etc.).

[0059] It is further understood that when a first element is said to be "in," "on," and / or "within" a second element, the first element may be disposed within the interior space of the second element, within a portion of the second element (e.g., within a wall of the second element), on the exterior and / or interior surface of the second element, and combinations of one or more of these.

[0060] 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 location (e.g., a target tissue location) is intended to include a component positioned near the anatomical location, as well as a component positioned in, on, and / or within the anatomical location.

[0061] Spatially relative terms such as "beneath," "below," "lower," "above," "upper," and the like may be used to describe the relationship of an element and / or feature to another element(s) and / or feature(s), for example, as shown in the figures. It is further understood that spatially relative terms are intended to encompass different orientations of the device in use and / or operation in addition to the orientation shown in the figures. For example, if a device in the figures were turned over, an element described as "beneath" and / or "below" another element or feature would then be oriented "above" that other element or feature. The device may be otherwise oriented (e.g., rotated 90 degrees or in other orientations) and the spatially relative descriptors used herein interpreted accordingly.

[0062] As used herein, the terms "reduce," "reducing," "reduction," and the like are intended to include a reduction in amount, including a reduction to zero. Reducing the likelihood of occurrence is intended to include prevention of occurrence. Similarly, the terms "prevent," "preventing," and "prevention" are intended to include the actions of "reducing," "reducing," and "reducing," respectively.

[0063] The term "and / or" as used herein should be construed as a specific disclosure of each of the two specified features or components, whether or not the other is present. For example, "A and / or B" should be construed as a specific disclosure of (i) A, (ii) B, and (iii) each of A and B, as if each were set forth individually herein.

[0064] As used herein, unless otherwise specified, "and" can mean "or" and "or" can mean "and." For example, if a feature is described as having A, B, or C, the feature can have any combination of A, B, and C, or 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.

[0065] As used in this disclosure, the phrase "configured" may be used interchangeably with the phrases "suitable," "capable," "designed," "adapted," "made," and "capable," for example, depending on the context. The phrase "configured" does not mean only "specially designed" in hardware. Alternatively, in some contexts, the phrase "configured device" may mean that the device is "capable" of operating with another device or component.

[0066] As used herein, the term "threshold" refers to a maximum level, a minimum level, and / or a range of values ​​associated with a desired or undesirable condition. 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 ​​to cause a desired effect (e.g., effective treatment) and / or prevent or reduce (hereinafter "prevent") an undesirable event (e.g., adverse device and / or clinical event). In some embodiments, a system parameter is maintained above a first threshold (e.g., above a first temperature threshold to produce a desired therapeutic effect on tissue) and below a second threshold (e.g., below a second temperature threshold to prevent undesirable tissue damage). In some embodiments, thresholds are determined to include a safety margin, accounting for, for example, patient variability, system variability, tolerances, etc. As used herein, "above a threshold" refers to a parameter being above a maximum threshold, below a minimum threshold, within a threshold range, and / or outside a threshold range. The threshold may be user defined (eg, the patient's clinician) and / or system defined (eg, during the manufacture of the system).

[0067] The term "diameter," when used herein to describe non-circular geometries, shall be interpreted as the diameter of an imaginary circle that approximates the described geometry. 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.

[0068] 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.

[0069] As used herein, the term "functional element" is intended 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 therapeutic element). Alternatively or additionally, a functional element (e.g., a functional element including a sensor) may be configured to record one or more parameters, such as patient physiological parameters, patient anatomical parameters (e.g., tissue shape parameters), patient environment parameters, and / or system parameters. In some embodiments, a sensor or other functional element is configured to perform a diagnostic function (e.g., to record data used to perform a diagnosis). In some embodiments, a functional element is configured to perform a therapeutic function (e.g., to deliver therapeutic energy and / or a therapeutic agent). In some embodiments, a functional element includes one or more elements constructed and arranged to perform a function selected from the group consisting of: delivering energy, extracting energy (e.g., to cool a component), delivering a drug or other agent, manipulating a system component or patient tissue, recording or sensing a parameter, such as a patient physiological parameter or a system parameter, and one or more combinations thereof. A functional element may include a fluid and / or a fluid delivery system. A functional element may include a reservoir, such as an expandable balloon or other fluid-maintaining reservoir. A "functional assembly" may include an assembly constructed and arranged to perform a function, such as a diagnostic and / or therapeutic function. A functional assembly may include an expandable assembly. A functional assembly may include one or more functional elements.

[0070] As used herein, the term "transducer" is intended to include any component or combination of components that receives energy or any input and generates an output. For example, a transducer may 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, a transducer converts an electrical signal into any output, such as light (e.g., a transducer including a light-emitting diode or a light bulb), sound (e.g., a transducer including a piezoelectric crystal configured to transmit ultrasound energy), pressure, heat energy, cryogenic energy, chemical energy, mechanical energy (e.g., a transducer including a motor or solenoid), magnetic energy, and / or a different electrical signal (e.g., Bluetooth or other wireless communication element). Alternatively or additionally, a transducer may 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, for example, the transducer is configured to deliver one or more of 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 a tissue manipulation element), acoustic energy to tissue (e.g., a transducer including a piezoelectric crystal), chemical energy, electromagnetic energy, magnetic energy, and combinations of one or more thereof.

[0071] As used herein, the term "fluid" can refer to any flowable material, such as a liquid, gas, gel, or material that can be propelled through a lumen and / or opening.

[0072] It will be understood that certain features of the invention, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable subcombination. For example, it will be understood that all features recited in any of the claims (whether independent or dependent) may be combined in any given manner.

[0073] At least some of the drawings and descriptions of the invention have been simplified to focus on elements relevant to a clear understanding of the invention, with the understanding that for clarity, the exclusion of other elements that one skilled in the art would understand may also form part of the invention. However, because such elements are well known in the art and because they do not necessarily facilitate a better understanding of the invention, descriptions of such elements are not provided herein.

[0074] The terms defined in this disclosure are used only to describe specific embodiments of the present disclosure and are not intended to limit the scope of the present disclosure. Terms provided in the singular are intended to include the plural unless clearly indicated otherwise by 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 as the contextual meaning of the relevant art, and should not be interpreted as having an ideal or exaggerated meaning unless clearly defined herein. In some cases, the terms defined in this disclosure should not be interpreted to exclude embodiments of the present disclosure.

[0075] Provided herein is a cardiac information system for generating a diagnostic result related to a patient's cardiac condition. The system can be used to perform a medical procedure on the patient, such as a diagnosis, prognosis, and / or therapeutic treatment for the patient. The system can identify a cardiac conduction pattern in a patient, such as a patient with arrhythmia. The system includes a diagnostic catheter for insertion into the patient's heart. The diagnostic catheter can be configured to record electrical activity data from the patient, such as when the catheter includes one or more electrodes for measuring voltage. The system can further include a processing unit that receives the recorded electrical activity data. The processing unit can include an algorithm configured to perform one or more functions, such as generating calculated electrical activity data, a complexity assessment, and / or a diagnostic result. In some embodiments, the algorithm performs the complexity assessment and generates the diagnostic result. In some embodiments, the complexity assessment is performed by one or more algorithms described herein, which perform the complexity assessment alone or in combination with another algorithm. In some embodiments, the system further includes a therapeutic device, such as a cardiac ablation device and / or a pharmaceutical agent.

[0076] Referring now to FIG. 1 , a block diagram of an embodiment of a cardiac information processing system consistent with the concepts of the present invention is shown. The cardiac information processing system is designated system 100 and may be or include a system configured to perform cardiac mapping, diagnosis, prognosis, and / or therapy, for example, to treat a patient's disease or disorder, such as an arrhythmia or other cardiac condition as described herein. Additionally or alternatively, system 100 may be a system configured to teach and / or validate devices and methods for diagnosing and / or treating a cardiac abnormality or disease in patient P. System 100 may also be used to generate a representation of cardiac activity, for example, a dynamic representation of an activity wavefront propagating across the surface of the heart. In some embodiments, system 100 generates a diagnostic result 1100. Diagnostic result 1100 represents diagnostic data related to the patient's cardiac condition, for example, a diagnostic result based on a complexity assessment as described herein.

[0077] System 100 may include catheter 10, cardiac information console 20, and patient interface module 50, which may be configured to cooperate (e.g., cooperate collectively) to accomplish various functions of system 100. System 100 may include a single power supply (PWR), which may be shared by console 20 and patient interface module 50. Use of a single power supply in this manner significantly reduces the likelihood that leakage currents will propagate to patient interface module 50 and cause errors in localization (e.g., the process of determining the location of one or more electrodes within the body of patient P). Console 20, as shown in FIG. 1, includes bus 21 that electrically and / or otherwise operably connects the various components of console 20 to one another.

[0078] The catheter 10 includes an electrode array 12 that can be delivered percutaneously to a cardiac chamber (HC). In this embodiment, the array of electrodes 12 has a known spatial configuration in three-dimensional (3D) space. For example, in an expanded state, the physical relationship of the electrode array 12 is known or can be reliably assumed. The electrode array 12 can include at least one electrode 12a, or at least three electrodes 12a. The diagnostic catheter 10 also includes a handle 14 and an elongated, flexible shaft 16 extending from the handle 14. Attached to the distal end of the shaft 16 is the electrode array 12, such as a radially expandable and / or compactible assembly. In this embodiment, the electrode array 12 is shown as a basket array, although the electrode array 12 can take other forms in other embodiments. In some embodiments, the expandable electrode array 12 may be constructed and arranged as described with reference to Applicant's International PCT Patent Application No. PCT / US2013 / 057579, filed August 30, 2013, entitled "SYSTEM AND METHOD FOR DIAGNOSING AND TREATING HEART TISSUE," and International PCT Patent Application No. PCT / US2014 / 015261, filed February 7, 2014, entitled "EXPANDABLE CATHETER ASSEMBLY WITH FLEXIBLE PRINTED CIRCUIT BOARD," the contents of each of which are incorporated herein by reference in their entirety for all purposes. In other embodiments, the expandable electrode array 12 may include a balloon, radially deployable arms, a spiral array, and / or other expandable compact structure (e.g., a resiliently biased structure).

[0079] The shaft 16 and expandable electrode array 12 are constructed and arranged to be inserted into a body (e.g., an animal body or a human body, such as the body of a patient P) and advanced through a body vessel, such as the femoral vein and / or other blood vessel. The shaft 16 and electrode array 12 may be constructed and arranged to be inserted through an introducer (not shown, such as a transseptal sheath), e.g., when the electrode array 12 is in a compressed state, it may be slidably advanced through the lumen of the introducer into a body cavity, e.g., a heart chamber (HC), e.g., the right atrium or left atrium.

[0080] The expandable electrode array 12 may include multiple splines (e.g., multiple splines resiliently biased into a basket shape as shown in FIG. 1 ), each spline carrying multiple electrodes 12 a and / or multiple ultrasound (US) transducers 12 b. While three splines are visible in FIG. 1 , the basket array is not limited to three splines, and more or fewer splines may be included in the basket array. Each electrode 12 a may be configured to record (e.g., record, measure, and / or sense) biopotentials (also referred to herein as “electrical activity”), such as voltage levels, at locations on the surface of the heart and / or within the heart chamber HC. The recorded electrical activity is stored by the system 100 as electrical activity data 120 a. The system 100 may perform one or more calculations on the recorded electrical activity data 120 a to generate calculated electrical activity data 120 b. The electrical activity data 120 may include recorded electrical activity data 120 a and / or calculated electrical activity data 120 b. The calculated electrical activity data 120b can include data selected from the group consisting of voltage data, mathematically processed voltage data (e.g., averaged data, integrated data, sorted data, minimum and / or maximum determined data, and / or otherwise mathematically processed data), surface charge data, dipole density data, timing data of electrical events, filtered electrical data, electrical pattern and / or template data, images formed by electrical values ​​at multiple locations, and combinations of one, two, or more of these. As used herein, the terms dipole density, surface charge, and surface charge density are intended to be used interchangeably.

[0081] The calculated electrical activity data 120b may include activation timing data 121, which is data representing the electrical activation (also referred to herein as "activation") of cardiac tissue. In some embodiments, the calculated electrical activity data 120b includes conduction velocity data 122, which is data representing conduction velocity, and / or conduction divergence data 123, which is data representing conduction divergence, each described below. The calculated electrical activity data 120b may be associated with one or more locations of the heart, referred to herein as apexes (single locations) and apexes (multiple locations). In some embodiments, the calculated electrical activity data includes data selected from the group consisting of electrical difference (e.g., delta), average, weighted average, pattern and / or template, goodness of fit (e.g., best fit) to one or more patterns or templates, "flow" between two or more images formed by electrical values ​​at multiple locations (e.g., calculated by one, two, or more optical flow algorithms such as the Horn-Schunck and / or Lucas-Kanade algorithms), data analysis and / or statistical methods such as classification or categorization of electrical activity using a training data set (e.g., independently acquired data such as historical data), computationally optimized fit (e.g., machine learning or predictive analysis, such as by neural networks or deep learning, cluster analysis, etc.), and combinations of one, two, or more of these. The calculated electrical activity data can include a probabilistic model that uses one or more of the foregoing methods as input.

[0082] In some embodiments, activation is determined by an algorithm (e.g., an activation detection algorithm), which may include comparing the electrical data to a threshold value, measuring the slope and / or maximum and / or minimum of the electrical data, comparing (e.g., weighted comparisons) the electrical data at one location with the electrical data at one or more nearby locations, and combinations thereof. In some embodiments, the activation detection algorithm may be constructed and arranged similarly to those described with reference to Applicant's International Application No. PCT / US2017 / 030915, entitled "CARDIAC INFORMATION DYNAMIC DISPLAY SYSTEM AND METHOD," filed May 3, 2017, and International Application No. PCT / US2017 / 030922, entitled "CARDIAC MAPPING SYSTEM WITH EFFICIENCY ALGORITHM," filed May 3, 2017, the contents of each of which are incorporated herein by reference in their entirety for all purposes. To promote spatial continuity of the propagation history map, the activation detection algorithm may include two parallel lines that consider both the raw signal (such as dipole density data and / or voltage data) and the spatial Laplacian signal. In some embodiments, the activation detection algorithm further includes conduction velocity as one consideration in selecting between potential activation timings and developing a voting scheme for multiple features such as gradient, spatial Laplacian, peak amplitude, and / or other such features.

[0083] In an extension to the addition of conduction velocity to activation detection, the problem can be expressed as a cost function with either conduction velocity regularization or conduction velocity inequality constraints. In some embodiments, the activation detection algorithm creates a Gaussian probability distribution function around each detected activation, with the highest probability being the currently detected activation. In the absence of constraints, the propagation history can be output by maximizing the probability of activation for all channels. Alternatively, at least one constraint can be included to restrict the solution to include physiologically plausible conduction (e.g., less than 2 m / s) and can be configured to shift activations slightly from the currently selected activation time. The following is an example of how the cost function is written with conduction velocity constraints:

number

[0084] where P is the probability that activation occurs at a particular vertex i at time τ. The calculation of conduction velocity depends on τ.

[0085] In some embodiments, the activation detection algorithm includes a local minimum in the time derivative of a unipolar electrogram, where the minimum separation between activations is set to a time threshold (e.g., between 50 and 150 ms).

[0086] In some embodiments, the activation detection algorithm includes local minima or maxima of bipolar or Laplacian electrograms with a minimum separation between activations set to a time threshold (e.g., between 50 and 150 milliseconds).

[0087] In some embodiments, the activation detection algorithm includes standard filtering with a band pass of (0.5-1 Hz) to (100-300 Hz) or after an aggressive band pass of (10-30 Hz) to (100-300 Hz).

[0088] In some embodiments, the activation detection algorithm includes local minima and / or local maxima of the time derivative of a bipolar or Laplacian electrogram, with the minimum separation between activations set to a time threshold (e.g., between 50-150 ms). The activation detection algorithm can further include standard filtering with a bandpass of (0.5-1 Hz) to (100-300 Hz) or after an aggressive bandpass of (10-30 Hz) to (100-300 Hz).

[0089] In some embodiments, the activation detection algorithm includes a zero crossing of the Laplacian electrogram after a negative deflection where the minimum separation between activations is set to a time threshold (e.g., between 50 and 150 ms).

[0090] In some embodiments, the minimum separation between activations comprises local maxima of Hilbert transform electrograms (phase mapping) set to a time threshold (e.g., 50-150 ms).

[0091] In some embodiments, the activation detection algorithm may include an algorithm formulated as a supervised learning problem that utilizes machine learning (e.g., neural networks, support vector machines, and / or deep learning). In these embodiments, the algorithm may use a training data set, such as a data set that includes historical data and / or simulated data.

[0092] Each US transducer 12b may be configured to transmit ultrasound signals, receive ultrasound reflections, determine the range to a reflecting target, such as a point on the surface of a cardiac chamber (HC), and provide anatomical data used in creating a digital model of the anatomy. The recorded ultrasound data and / or other anatomical data may be stored by the system 100 as anatomical data 110. The electrical activity data 120 (including, for example, activation timing data 121, conduction velocity data 122, and / or conduction divergence data 123) and / or the anatomical data 110 may be stored in a memory of the system 100, such as a storage device 25 described below.

[0093] As a non-limiting example, in this embodiment, three electrodes 12a and three US transducers 12b are shown on each spline. However, in other embodiments, the basket array may include more or fewer electrodes and / or more or fewer US transducers. Furthermore, the electrodes 12a and transducers 12b may be arranged in pairs, where one electrode 12a is paired with one transducer 12b, with multiple electrode-transducer pairs per spline. However, the concept of the present invention is not limited to this particular electrode-transducer arrangement. In other embodiments, not all electrodes 12a and transducers 12b need be arranged in pairs; some may be arranged in pairs and others may not. Also, in some embodiments, not all splines include the same arrangement of electrodes 12a and transducers 12b. Additionally, in some embodiments, electrodes 12a are arranged on a first set of splines and transducers 12b are arranged on a second set of splines. The array 12 may include at least four electrodes 12a, such as at least 24 electrodes 12a, such as at least 48 electrodes. The array 12 may include at least three splines, such as at least four splines, such as at least six splines.

[0094] In some embodiments, a second catheter, catheter 10', may be used in combination with catheter 10, e.g., to position basket or other electrode arrays of catheter 10' in separate heart chambers to simultaneously map multiple heart chambers. Catheter 10' may be of a similar or different construction to catheter 10 described herein. The electrode array of catheter 10' may be arranged in a different configuration than electrode array 12 of catheter 10. For example, the array of catheter 10' may have only 24 electrodes and no US transducers, while array 12 of catheter 10 has 48 electrodes and 48 US transducers. Catheters 10 and / or 10' may include two or more electrode arrays, such as the illustrated array 12, and a second array positioned proximal to array 12 (e.g., on the shaft 16 of catheter 10 or 10').

[0095] Catheter 10 may include a cable or other conduit, such as cable 18, that is configured to electrically, optically, and / or electro-optically connect catheter 10 to console 20 via connectors 18a and 20a, respectively. In some embodiments, cable 18 includes a mechanism selected from the group consisting of a cable, such as a steering cable, a mechanical linkage, a hydraulic tubing, a pneumatic tubing, and a combination of one or more of these.

[0096] The patient interface module 50 may be configured to electrically isolate one or more components of the console 20 from the patient P (e.g., to prevent the undesired delivery of shocks or other undesired electrical energy to the patient P). The patient interface module 50 may be integral with the console 20, as shown, and / or it may comprise a separate, separate component (e.g., a separate housing). The console 20 includes one or more connectors 20b, each connector comprising a jack, plug, terminal, port, or other custom or standard electrical, optical, and / or mechanical connector. In some embodiments, the connectors 20b are terminated to maintain a desired input impedance across RF frequencies, such as from 10 kilohertz to 20 megahertz. In some embodiments, the termination is achieved by terminating the cable shield with a filter. In some embodiments, the termination filter provides a high input impedance over one frequency range to minimize leakage, for example, at localization frequencies, and a low input impedance over a different frequency range to achieve maximum signal integrity, for example, at ultrasound frequencies. Similarly, the patient interface module 50 includes one or more connectors 50b. At least one cable 52 connects patient interface module 50 to console 20 via connectors 20b and 50b.

[0097] In this embodiment, the patient interface module 50 includes an isolated stereotactic drive system 54, a set of patch electrodes 56, and one or more reference electrodes 58. The isolated stereotactic drive system 54 isolates the stereotactic signals from the rest of the system 100 to prevent current leakage (e.g., signal loss) that would result in performance degradation. In some embodiments, the isolation of the stereotactic signals from the rest of the system includes an impedance range of over 100 kilohms, such as approximately 500 kilohms at stereotactic frequencies. The isolation of the stereotactic drive system 54 may minimize stereotactic position drift and maintain a high degree of isolation between axes (as described below). The stereotactic drive system 54 may operate as a current, voltage, magnetic, acoustic, or other type of energy modality drive. The patch electrodes 56 and / or the set of one or more reference electrodes 58 may consist of conductive electrodes, magnetic coils, acoustic transducers, and / or other types of transducers or sensors based on the energy modality used by the stereotactic drive system 54. Additionally, the isolated stereotactic drive system 54 maintains simultaneous output in all axes (e.g., a stereotactic signal is present at each axis electrode pair while also increasing the effective sampling rate at each electrode location). In some embodiments, the stereotactic sampling rate includes rates between 10 kHz and 20 MHz, e.g., a sampling rate of approximately 625 kHz.

[0098] In some embodiments, the set of patch electrodes 56 includes three pairs of patch electrodes: an "X" pair with two patch electrodes located on either side of the ribs (X1, X2), a "Y" pair with one patch electrode located on the lower back (Y1) and one patch electrode located on the upper chest (Y2), and a "Z" pair with one patch electrode located on the upper back (Z1) and one patch electrode located on the lower abdomen (Z2). The pairs of patch electrodes 56 may be positioned on any set of orthogonal and / or non-orthogonal axes. In the embodiment of FIG. 1, the electrode placement is shown on the patient P, with the electrodes on the back indicated by dashed lines.

[0099] The reference patch electrode 58 may be placed in the lumbar / hip region. Additionally or alternatively, a reference catheter may be placed in a body vessel, such as a vessel in and / or proximal to the lumbar / hip region.

[0100] The placement of the electrodes 56 defines a coordinate system made up of three axes, one axis for each pair of patch electrodes 56. In some embodiments, the axes are non-orthogonal to the body's natural axes, i.e., head-to-toe, chest-to-dorsal, and side-to-side (e.g., rib-to-rib). The electrodes may be positioned such that the axes intersect at an origin, such as an origin located in the heart. For example, the origin of the three intersecting axes may be located at the center of the atrial volume. The system 100 may be configured to provide an "electrical zero" located outside the heart, for example, by placing the reference electrode 58 such that the resulting electrical zero is outside the heart (e.g., avoiding crossing from positive to negative voltages at one or more localized locations).

[0101] As noted above, patch pairs can operate differentially, for example, when neither patch 56 of a pair operates as a reference electrode and both are driven by the system 100 to generate an electric field between them. Alternatively or additionally, one or more of the patch electrodes 56 can function as a reference electrode 58, such that they operate in a single-ground mode. One of any pair of patch electrodes 56 can function as the reference electrode 58 for that patch pair, forming a single-ground patch pair. One or more patch pairs can be independently configured to be single-ground. One or more patch pairs can share a patch as a single-ground reference or can have the reference patch of multiple patch pairs electrically connected.

[0102] Through processing performed by the console 20, the axis can be translated (e.g., rotated) from a first orientation (e.g., a non-physiological orientation based on the placement of the electrodes 56) to a second orientation. The second orientation can include a standard left-posterior-superior (LPS) anatomical orientation, where, for example, the "x" axis is oriented from the patient's right to left, the "y" axis is oriented from the patient's anterior to posterior, and the "z" axis is oriented from the patient's caudal to cranial. The placement of the patch electrodes 56 and the resulting non-standard axis can be selected to provide improved spatial resolution (e.g., due to favorable tissue properties between the electrodes 56 in the non-standard orientation) when compared to patch electrode placement in the resulting axis resulting in a normal physiological orientation. For example, the non-standard electrode 56 placement can result in a reduced negative impact of the low-impedance volume of the lungs on the stereotactic field. Furthermore, the placement of the electrodes 56 can be selected to create axes that pass through the patient's body along paths of equal or at least similar length. Axes of similar length occupy more similar energy densities per unit distance within the body, resulting in more uniform spatial resolution along such axes. Converting non-standard axes to standard orientations can provide a simpler viewing environment for the user. Once the desired rotation is achieved, each axis can be scaled, e.g., lengthened or shortened as needed. Rotation and scaling are performed based on comparing the predetermined (e.g., expected or known) shape and relative dimensions of the electrode array 12 with measurements corresponding to the shape and relative dimensions of the electrode array in a coordinate system in which the patch electrodes are established. For example, rotation and scaling can be performed to convert a relatively inaccurate (e.g., uncalibrated) representation into a more accurate representation. Shaping and scaling the representation of the electrode array 12 can adjust, align, and / or otherwise improve the orientation and relative size of the axes for much more accurate localization.

[0103] The electrical reference electrode(s) 58 can be, or at least include, a patch electrode and / or an electrical reference catheter, which can serve as an “analog ground” reference for the patient. The patch electrode 58 can be placed on the skin and can serve as a current return for defibrillation (e.g., provide a secondary purpose). The electrical reference catheter can include a unipolar reference electrode, which is used to enhance common-mode signal rejection. The unipolar reference electrode, or other electrodes on the reference catheter, can be used to measure, track, correct, and / or calibrate physiological, mechanical, electrical, and / or computational artifacts in the cardiac signal. In some embodiments, these artifacts are due to artifacts induced by breathing, cardiac motion, and / or added signal processing, such as filters. Another form of electrical reference catheter can be an internal analog reference electrode, which can serve as a low-noise “analog ground” for all internal catheter electrodes. Each of these types of reference electrodes can be placed in a relatively similar location, for example, near the waist of an internal blood vessel (as a catheter) and / or above the waist (as a patch). In some embodiments, system 100 includes a reference catheter 58 that includes a fixation mechanism (e.g., a user-actuated fixation mechanism) that may be constructed and arranged to reduce displacement (e.g., accidental or otherwise unintended movement) of one or more electrodes of reference catheter 58. The fixation mechanism may include a mechanism selected from the group consisting of a helical expander, a spherical expander, a circumferential expander, an axially actuated expander, a rotationally actuated expander, and combinations of two or more thereof.

[0104] In some embodiments, console 20 includes a defibrillation (DFIB) protection module 22 connected to connector 20a, which is configured to receive cardiac information from catheter 10. DFIB protection module 22 is configured to have a precise clamping voltage and reduced (e.g., minimal) capacitance. Functionally, DFIB protection module 22 is configured to act as a surge protector and protect the circuitry of console 20 during application of high energy to a patient, for example, during defibrillation of the patient (e.g., using a standard defibrillation device).

[0105] The DFIB protection module 22 may be coupled to three signal paths: a biopotential (BIO) signal path 30, a localization (LOC) signal path 40, and an ultrasound (US) signal path 60. Generally, the BIO signal path 30 filters noise, stores recorded biopotential data, and allows ablation (e.g., delivery of RF energy to tissue) while simultaneously reading (e.g., normally recording) biopotential signals, which is not the case in other systems. Generally, the LOC signal path 40 filters noise from received localization data while allowing high voltage input. Generally, the US signal path 60 uses the ultrasound transducer 12b to acquire distance data from physical structures of the anatomy to generate a 2D or 3D digital model of the heart chamber HC, which may be stored in memory.

[0106] The BIO signal path 30 includes an RF filter 31 coupled to the DFIB protection module 22. In this embodiment, the RF filter 31 operates as a low-pass filter with a high input impedance. A high input impedance is preferred in this embodiment because it minimizes loss of voltage from the source (e.g., the catheter 10), thereby better preserving the received signal (e.g., during RF ablation). The RF filter 31 is configured to allow biopotential signals from the electrode 12a on the catheter 10 to pass through the RF filter 31 (e.g., pass frequencies below 500 Hz), e.g., frequencies in the range of 0.5 Hz to 500 Hz. However, high frequencies, such as high-voltage signals used in RF ablation, are filtered out of the biopotential signal path 30. The RF filter 31 may include a corner frequency between 10 kHz and 50 kHz.

[0107] The BIO amplifier 32 may include a low-noise, single-ended input amplifier that amplifies the RF filtered signal. The BIO filter 33 (e.g., a low-pass filter) filters noise from the amplified signal. The BIO filter 33 may include an approximately 3 kHz filter. In some embodiments, the BIO filter 33 includes an approximately 7.5 kHz filter (e.g., to avoid significant signal loss and / or degradation during cardiac pacing), such as when the system 100 is configured for cardiac pacing.

[0108] The BIO filter 33 may include a differential amplifier stage that is used to remove common-mode power line signals from the biopotential data. The differential amplifier may implement a baseline restoration function that removes DC offset and / or low-frequency artifacts from the biopotential signal. In some embodiments, the baseline restoration function includes a programmable filter that may include one or more filter stages. In some embodiments, the filter includes a state-dependent filter. The characteristics of the state-dependent filter may be based on threshold and / or other levels of parameters (e.g., voltage), and the filter rate varies based on the state of the filter. Components of the baseline restoration function may incorporate noise reduction techniques, such as dithering and / or pulse-width modulation of the baseline restoration voltage. The baseline restoration function may further determine the filter response of one or more stages by measurement, feedback, and / or characterization. The baseline restoration function may further determine and / or distinguish portions of the signal that represent the physiological signal morphology from artifacts in the filter response and computationally restore the original morphology or portions thereof. In some embodiments, restoration of the original form may include direct subtraction of the filter response, and / or additional signal processing of the filter response, such as subtraction, multiplication, filtering, inversion, and combinations thereof, of the filter response after static, time-dependent, and / or spatial-dependent weighting. In some embodiments, the baseline restoration function is implemented in the BIO filter 33, the BIO processor 36, or both.

[0109] The LOC signal path 40 includes a high-voltage buffer 41 coupled to the DFIB protection module 22. In this embodiment, the high-voltage buffer 41 is configured to accommodate relatively high voltages used in therapeutic techniques, such as RF ablation voltages. For example, the high-voltage buffers may have ±100V power rails. In some embodiments, each high-voltage buffer 41 has a high input impedance, e.g., an impedance of 100 kilohms to 10 megahms at local frequencies. In some embodiments, all of the high-voltage buffers 41, when taken together as a total parallel electrical equivalent, also have a high input impedance, e.g., an impedance of 100 kilohms to 10 megahms at local frequencies. In some embodiments, the high-voltage buffer 41 has a bandwidth that maintains good performance over a range of high frequencies, e.g., frequencies between 100 kilohms and 10 megahtz, e.g., frequencies around 2 megahertz. In some embodiments, the high-voltage buffer 41 does not include a passive RF filter input stage, e.g., when the high-voltage buffer 41 has a ±100V power supply. A high frequency bandpass filter 42 may be coupled to the high voltage buffer 41 and may have a passband frequency range of approximately 20 kHz to 80 kHz for use in localization. In some embodiments, the filter 42 has low noise at unity gain (e.g., a gain of 1 or approximately 1).

[0110] The US signal path 60 includes a US isolation multiplexer, MUX 61; a US transformer with a Tx / Rx switch, US transformer 62; a US generation and detection module 63; and a US signal processor 66. The US isolation MUX 61 is connected to the DFIB protection module 22 and is used to turn the US transducers 12b on and off in a predetermined sequence or pattern. The US isolation MUX 61 may be a set of high input impedance switches that, when open, isolates the US system from the remaining US signal path elements and decouples impedance to ground (via the transducers and US signal path 60) from the inputs of the LOC and BIO paths. The US isolation MUX 61 also multiplexes one transmit / receive circuit to one or more of the transducers 12b on the catheter 10. The US transformer 62 operates bidirectionally between the US isolation MUX 61 and the US generation and detection module 63. The US transformer 62 isolates the patient from currents generated by the US transmit and receive circuitry of the module 63 during ultrasound transmission and reception by the US transducers 12b. The US transformer 62 can be configured to selectively engage the transmit and / or receive electronics of the module 63 based on the mode of operation of the transducers 12b, for example, by using a transmit / receive switch. That is, in transmit mode, the module 63 receives a control signal from the US processor 66 (within the data processor 26), which activates US signal generation and connects the output of the Tx amplifier to the US transformer 62. The US transformer 62 couples the signal to the US isolation MUX 61, which selectively activates the US transducers 12b. In receive mode, the US isolation MUX 61 receives reflected signals from one or more transducers 12b, which are passed to the US transformer 62. The US transformer 62 couples the signal to the receive electronics of the US generation and detection module 63, which then forwards the reflected data signals to the US processor 66 for processing and use by the user interface 27 and display 27a. In some embodiments, the processor 66 instructs the MUX 61 and US transformer 62 to enable transmission and reception of ultrasonic waves to activate one or more of the associated transducers 12b in a predetermined sequence or pattern, etc.The US processor 66 may, by way of example, include detecting a single first reflection, detecting and distinguishing between multiple reflections from multiple targets, determining velocity information from Doppler and / or subsequent pulses, determining tissue density information from the amplitude, frequency, and / or phase characteristics of the reflected signal, and any combination of one or more of these.

[0111] An analog-to-digital converter (ADC) 24 is coupled to the BIO filter 33 in the BIO signal path 30 and the high-frequency filter 42 in the LOC signal path 40. The ADC 24 receives a set of individual time-varying analog biopotential voltage signals, one for each electrode 12a. These biopotential signals are differentially referenced to a unipolar electrode for enhanced common-mode rejection, filtered, and gain calibrated for each individual channel via the BIO signal path 30. The ADC also receives a set of individual time-varying analog localization voltage signals for each axis of each patch electrode 56 via the LOC signal path 40, which are output to the ADC 24 as a set of 48 (in this embodiment) localization voltages measured at a single time for the electrode 12a. The ADC 24 has high oversampling to enable noise shaping and filtering, for example, an oversampling rate of approximately 625 kHz. In some embodiments, sampling is performed at or above the Nyquist frequency of the system 100. The ADC 24 is a multi-channel circuit and can combine the BIO and LOC signals or keep them separate. In one embodiment, as a multi-channel circuit, the ADC 24 can be configured to accommodate 48 stereotactic electrodes 12a and 32 auxiliary electrodes (e.g., for ablation or other processes) for a total of 80 channels. In other embodiments, more or fewer channels may be provided. In FIG. 1 , for example, almost all elements of the console 20 can be duplicated for each channel (e.g., except for the UI system 27). For example, the console 20 can include a separate ADC for each channel or an 80-channel ADC. In this embodiment, signal information from the BIO signal path 30 and the LOC signal path 40 is input to and output from various channels of the ADC 24. Outputs from the channels of the ADC 24 are coupled to either the BIO signal processing module 34 or the LOC signal processing module 44, which preprocess the respective signals for subsequent processing, as described below. In either case, pre-processing prepares the received signal for processing by the respective dedicated processors described below.In some embodiments, the BIO signal processing module 34 and the LOC signal processing module 44 may be implemented, in whole or in part, in firmware.

[0112] The biopotential signal processing module 34 may include digital RF filtering with gain and offset adjustment and / or a non-dispersive low-pass filter and an intermediate frequency band. The intermediate frequency band can reject ablation and localization signals. The biopotential signal processing module 34 may also include digital biopotential filtering, which can optimize the output sample rate.

[0113] Additionally, the biopotential signal processing module 34 may also include “pace blanking,” which is, for example, blanking of information received during a time frame when a physician is “pacing” the heart. Temporary cardiac pacing may be implemented, by way of example, through the insertion or application of intracardiac, intraesophageal, and / or percutaneous pacing leads. The goal of temporary cardiac pacing may be to interactively test and / or improve cardiac rhythm and / or hemodynamics. To accomplish the above, active and passive pacing triggers and input algorithmic trigger determination may be performed (e.g., by the system 100). The algorithmic trigger determination may use a subset of channels, edge detection, and / or pulse width detection to determine whether pacing of the patient has occurred. If desired, pace blanking may be applied by the system 100 to all channels or a subset of channels, including channels on which no detection has occurred.

[0114] Additionally, the biopotential signal processing module 34 may also include specialized filters to remove ultrasound signals and / or other unwanted signals (e.g., artifacts from the biopotential data). In some embodiments, edge detection, threshold detection, and / or timing correlation are used to perform this filtering.

[0115] The localization signal processing module 44 can provide individual channel / frequency gain calibration, IQ demodulation with adjusted demodulation phase, synchronous and sequential demodulation (no MUX), narrowband R filtering, and / or time filtering (e.g., interleaving, blanking, etc.), as described below. The localization signal processing module 44 can also include digital localization filtering to optimize the output sample rate and / or frequency response.

[0116] In this embodiment, the algorithmic calculations for the BIO signal path 30, the LOC signal path 40, and the US signal path 60 are performed by the console 20. These algorithmic calculations may include, but are not limited to, processing multiple channels at once, measuring propagation delays between channels, transforming x, y, z data into a spatial distribution of electrode locations, calculating and applying corrections to the set of locations, combining individual ultrasound distances and electrode locations, calculating detected endocardial surface points, and constructing a surface mesh from the surface points. The number of channels processed by the console 20 may be between 1 and 500, e.g., between 24 and 256, e.g., 48, 80, or 96 channels.

[0117] The data processor 26 may include one or more of several types of processing circuitry (e.g., a microprocessor) and memory circuitry, and executes the computer instructions necessary to perform the processing of pre-processed signals from the BIO signal processing module 34, the localization signal processing module 44, and the USTX / RX MUX 61. The data processor 26 may be configured to perform the calculations and data storage and retrieval necessary to perform the functions of the system 100.

[0118] In this embodiment, the data processor 26 may include a biopotential (BIO) processor 36, a localization (LOC) processor 46, and an ultrasound (US) processor 66. The biopotential processor 36 may perform processing of recorded, measured, or sensed biopotentials (e.g., from the electrodes 12a). The LOC processor 46 may perform processing of localization signals. The US processor 66 may perform image processing of reflected US signals (e.g., from the transducer 12b).

[0119] The biopotential processor 36 can be configured to perform various calculations. For example, the BIO processor 36 can include an enhanced common-mode signal rejection filter, which can be bidirectional to minimize distortion and can be seeded with a common-mode signal. The BIO processor 36 can also include an optimized ultrasound rejection filter and can be configured for selectable bandwidth filtering. Data processing steps for the US signal path 60 can be performed by the biosignal processor 34 and / or the bioprocessor 36.

[0120] The localization processor 46 may be configured to perform various calculations. As described in more detail below, the LOC processor 46 may electronically correct (calculate) axes based on the known shape of the electrode array 12, correct for scaling or skew of one or more axes based on the known shape of the electrode array 12, and perform "fitting" to align the measured electrode positions to known possible configurations, which may be optimized with one or more constraints (e.g., physical constraints such as the distance between two electrodes 12 a on a single spline, the distance between two electrodes 12 a on two different splines, the maximum distance between two electrodes 12 a, the minimum distance between two electrodes 12 a, and / or the minimum and / or maximum curvature of the splines).

[0121] The US processor 66 may be configured to perform various calculations related to the generation of US signals via the US transducer 12b and the processing of US signal reflections received by the US transducer 12b. The US processor 66 may be configured to interact with the US signal path 60 to selectively transmit and receive US signals to and from the US transducer 12b. Each of the US transducers 12b may be placed in a transmit mode and / or a receive mode under the control of the US processor 66. The US processor 66 may be configured to construct 2D and / or 3D images of the heart chamber (HC) in which the electrode array 12 is located using reflected US signals received via the US path 60 from the US transducer 12b.

[0122] The console 20 can also include a stereotactic drive circuit, which includes a stereotactic signal generator 28 and a stereotactic drive current monitoring circuit 29. The stereotactic drive circuit provides high-frequency stereotactic drive signals (e.g., 10 kHz to 1 MHz, such as 10 kHz to 100 kHz). Stereotaxis using drive signals at these high frequencies reduces the impact of cellular responses (e.g., from blood cell deformation) on the stereotactic data and / or allows for higher drive currents (e.g., achieving a better signal-to-noise ratio). The signal generator 28 generates a high-resolution digital synthesis of the drive signal (e.g., a sinusoidal wave) with ultra-low phase noise timing. The drive current monitoring circuit provides a high-voltage, wide-bandwidth current source, which is monitored to measure the impedance of the patient P.

[0123] Console 20 may also include at least one data storage device 25 for storing various types of recorded, measured, sensed, and / or calculated information and data, as well as program code that embodies the functionality available from console 20.

[0124] The console 20 may also include a user interface (UI) system 27 configured to output the results of the localization, biopotential, and US processing. The UI system 27 may include at least one display 27a to graphically render such results in 2D, 3D, or a combination thereof. In some embodiments, the display 27a includes two simultaneous views of the 3D results with independently configurable view / camera properties such as view direction, zoom level, pan position, etc., and object properties such as color, transparency, brightness, luminance, etc. The UI system 27 may include one or more user input components, such as a touchscreen, keyboard, joystick, and / or mouse.

[0125] The console 20, or another component of the system 100, may include one or more algorithms, such as the illustrated complexity algorithm 600. The complexity algorithm 600 may include an algorithm such as those described below with reference to FIG. 3. The complexity algorithm 600 may include one or more algorithms, such as one or more of the CV algorithm 200, the LRA algorithm 300, the LIA algorithm 400, the FA algorithm 500, and / or the complexity algorithm 600, described below. The complexity algorithm 600 may identify, quantify, categorize, and / or otherwise evaluate cardiac conduction patterns or characteristics, generating diagnostic information, herein referred to as a diagnostic result 1100. The complexity algorithm 600 may generate an assessment of complexity over time and / or space and / or an assessment of the variability of complexity over time. In some embodiments, the complexity algorithm 600 and / or another algorithm of the system 100 includes a bias. In some embodiments, the algorithm includes a bias toward false positives (e.g., a bias toward not classifying complex regions as complex versus incorrectly identifying non-complex regions as complex). In some embodiments, the algorithm includes a bias toward false negatives. In some embodiments, the algorithm of system 100 includes biases that are set and / or adjusted (herein "set") by a clinician, e.g., to bias system 100 toward the clinician's particular preferences.

[0126] Complexities determined by the algorithms of the present concepts include deviations from expected or normal behavior, which is otherwise a simple, repetitive, and consistent pattern of electrical activity. In cardiac electrical activity, expected or normal behavior of the heart chambers is a consistent, repeated, and coordinated activation of tissues, called sinus rhythm, which initiates at a location (e.g., the sinoatrial node) and propagates smoothly along the heart chambers. Complexities include any deviations that disrupt coherence (e.g., activation time, amplitude, direction, and / or repetition rate) and / or coordination / sequence (e.g., activation time and / or direction). Regions of tissue may self-initiate electrical activation (automatism) or disrupt otherwise coordinated activation. Regions of tissue that may have compromised, scarred, diseased, and / or other heterogeneous characteristics (e.g., fibrosis, various fiber orientations, various endocardial to epicardial pathways) create complexities in cardiac activity, as described above. Regions that create complexities may disrupt expected conduction in a consistent manner. For example, conduction may be redirected in a different direction, with reduced amplitude, but in the same way from activation to activation. Alternatively, regions exhibiting complexity (e.g., as identified by the algorithms of system 100) may disrupt expected conduction in a probabilistic or stochastic manner (e.g., like random fluctuations), which addresses discernible statistical behavior in a manner that disrupts conduction. For example, modified conduction may be identified through regions that exhibit one characteristic manner for X% of the time and a second, different, characteristic manner for Y% of the time. In some embodiments, activation exhibits normal conduction for Z% of the time (where Z<100), but the region is still identified by system 100 as complex due to one or more forms of modified conduction for a portion of the time.

[0127] Algorithms of the present concepts can be configured to identify when multiple regions of complexity interact or otherwise couple to create additional complexity throughout the cardiac chamber, thereby increasing the overall degree of complexity across the chamber, as described below with reference to FIG. 3A. Due to the propagating nature of refractory (inactive) cardiac tissue, complexity affecting the order and timing of activation can have a persistent / permanent effect on subsequent activations over a wide spatial region in time. Thus, as the number of unique or distinct zones of automaticity or heterogeneity increases (tissue-mediated complexity), the resulting electrical activation becomes increasingly complex (e.g., tissue-mediated complexity versus enhanced coupling-related complexity), which is coupled to the propagating nature of cardiac tissue in time and space, established by preceding conduction changes, and impacting subsequent conduction changes. As complexity increases, the ability to distinguish tissue-mediated complexity from coupling-related complexity based on simple electrical measurements becomes more challenging. System 100 can be configured to collect more information over time and space (e.g., simultaneously), and the additional information collected assists one or more algorithms in deciphering local, regional, and global complexity across the heart chambers.

[0128] Complexity algorithm 600 can perform a complexity assessment based on calculated electrical activity data 120b representing multiple vertices, for example, where associated recorded electrical activity data 120a includes data recorded from at least three recording locations within a heart chamber (e.g., on and / or offset from the heart wall). In some embodiments, recorded electrical activity data 120a includes at least one location offset from the heart wall (e.g., at least one non-contact recording). In some embodiments, recorded electrical activity data 120a includes at least one location on the heart wall (e.g., at least one contact recording). In some embodiments, recorded electrical activity data 120a includes at least one location offset from the heart wall and at least one location on the heart wall (e.g., at least one contact and one non-contact recording, “hybrid”). In some embodiments, for each location on the heart wall where a contact-based measurement is made, system 100 is biased to categorize the location as a vertex.

[0129] In some embodiments, algorithm 600 includes a second algorithm configured to calculate surface charge data and / or dipole density data for each of the plurality of vertices based on the recorded electrical activity data 120a (e.g., recorded voltages), for example, if the complexity analysis is based on surface charge data and / or dipole density data. Surface charge data and / or dipole density data may be calculated as described in Applicant's U.S. Patent No. 8,417,313, entitled "METHOD AND DEVICE FOR DETERMINING AND PRESENTING SURFACE CHARGE AND DIPOLE DENSITIES ON CARDIAC WALLS," issued April 9, 2013, and U.S. Patent No. 8,512,255, entitled "DEVICE AND METHOD FOR THE GEOMETRIC DETERMINATION OF ELECTRICAL DIPOLE DENSITIES ON THE CARDIAC WALL," issued August 20, 2013, the contents of each of which are incorporated herein by reference in their entirety for all purposes. In some embodiments, algorithm 600 includes a third algorithm that converts surface charge data and / or dipole density data to surface voltage data in cases where the complexity analysis is based on surface voltage data.

[0130] In some embodiments, the algorithm 600 performs a complexity assessment over a relatively small portion of the patient's heart (e.g., a relatively small portion of the patient's heart chamber), e.g., over a 7 cm portion of the heart wall. 2 A part that represents the following, e.g., 4cm 2 A part that represents the following, e.g. 1cm 2In some embodiments, the algorithm 600 performs a small-segment complexity assessment using voltage and / or dipole density data. In some embodiments, the analysis of a small portion of a patient's heart is performed using the system 100 and related methods described below with reference to FIGS. 9 and 9A.

[0131] In some embodiments, the algorithm 600 performs a complexity assessment over a medium or large portion of the patient's heart, for example, at least 7 cm of the heart wall tissue (e.g., the wall tissue of the atria of the heart). 2 a portion of the patient's heart that represents a minimum surface area of ​​1 cm 2 , for example 4cm 2 , for example 7cm 2In these embodiments, electrical activity may be recorded (e.g., by electrodes 12a) from at least 24 locations within the heart (e.g., within a single heart chamber) and calculated electrical activity data 120b may be determined for at least 64 vertices. In some embodiments, electrical activity may be recorded from at least 24 heart wall locations (e.g., via contact-based recording) with or without additional recordings made offset from the heart wall (e.g., in the flowing blood via non-contact-based recording). In these embodiments, electrical activity may be recorded from at least 48 heart wall locations or at least 64 heart locations. In some embodiments, electrical activity is recorded from both locations on the heart wall and locations offset from the heart wall, for example, when data is recorded from at least 24, at least 48, or at least 54 contact and non-contact locations within a heart chamber. In these embodiments, calculated electrical activity data 120b may be determined for at least 100 vertices, eg, at least 500, at least 3000, and / or at least 5000 vertices.

[0132] In some embodiments, the complexity algorithm 600 incorporates data through various depths (e.g., layers) of tissue. In thicker tissue, electrical conduction may vary throughout the thickness. Tissue stretch and / or strain may also affect the conductive properties of the tissue. Measuring, recording, and / or calculating electrical or biomechanical data throughout the tissue depth may be used to improve the accuracy and / or specificity of the complexity algorithm 600. In some embodiments, surface charge density and / or dipole density are calculated throughout the thickness of the tissue of the cardiac chamber, and the calculated data are used as input to the complexity algorithm 600. In some embodiments, the surface charge density and / or dipole density are determined as described in applicant's co-pending U.S. patent application Ser. No. 15 / 926,187, entitled "DEVICE AND METHOD FOR THE GEOMETRIC DETERMINATION OF ELECTRICAL DIPOLE DENSITIES ON THE CARDIAC WALL," filed March 20, 2018, the contents of which are incorporated herein by reference in their entirety for all purposes.

[0133] Complexity algorithm 600 may evaluate variations in one or more characteristics, e.g., electrical, mechanical, functional, and / or physiological characteristics of the heart, that vary over time, space, magnitude, and / or state. Study of cardiac motion, function, and other characteristics over the past several decades has provided a substantial understanding of what is considered "normal." Cardiac conditions, such as cardiac arrhythmias, exhibit variations from the normal in many ways, and these variations may be quantified, qualified, and / or evaluated by complexity algorithm 600.

[0134] In some embodiments, variations in time or temporal repetition and / or stability (e.g., measures of temporal regularity and / or irregularity) indicate the presence of cardiac arrhythmia. Electrical characteristics (e.g., cycle length, dominant frequency, harmonic organization, partitioning or measures of waveform "energy," Shannon entropy, waveform bias within a time window, temporal waveform recurrence, regularity, and / or higher-order statistics of the electrical data, such as kurtosis) can be measured or otherwise determined by system 100, and these characteristics can be included in the assessment performed by complexity algorithm 600. System 100 can determine these variables using tools such as interval analysis, Fourier transform, Hilbert transform or other transforms, wavelet analysis, and combinations thereof.

[0135] The mechanical and / or functional (herein "mechanical") properties evaluated by algorithm 600 may include the timing of cardiac wall deflection over time. In some embodiments, system 100 determines, and algorithm 600 evaluates, a combination of electrical and / or mechanical data, such as electromechanical delay (which may also vary, e.g., as a function of time).

[0136] In some embodiments, algorithm 600 evaluates variations in the magnitude and / or state of the characteristics determined by system 100. For example, the evaluated electrical characteristics can include evaluation of electrical activity at the cardiac surface, such as rms amplitude, peak-to-peak amplitude, peak-negative amplitude, and combinations thereof. The evaluated mechanical characteristics can include global or average deflection of the cardiac wall through one or more phases of the cardiac cycle. In some embodiments, the combination of electrical and mechanical data includes the ratio of electrical magnitude to mechanical magnitude and / or functional efficiency.

[0137] In some embodiments, the algorithm 600 evaluates variations across space or in direction of one or more properties. For example, the electrical properties evaluated may include directional dipoles formed in different directions (e.g., determined from data recorded by unipolar electrodes), conduction velocity direction, spatial waveform analysis, and combinations thereof. In some embodiments, a Laplacian operator may be applied to electrical activity data 120a recorded from multipolar and / or omnipolar catheters to provide calculated data for the algorithm 600 to evaluate.

[0138] In some embodiments, the algorithm 600 evaluates variations in one or more characteristics with respect to two or more of time, space, magnitude, and / or state. In some embodiments, the algorithm 600 evaluates two or more of these that vary simultaneously, such as spatiotemporal variations. In these embodiments, the algorithm 600 may evaluate electrical characteristics to determine whether a pattern of interest (e.g., focal, rotational, irregular, directional, and / or timing pattern) occurs. The algorithm 600 may evaluate spatiotemporal features or patterns, such as activation sequences or conduction patterns, that exhibit one or more of the following characteristics: propagation that "fires" through restricted "gaps" or openings, regionally restricted gyration reentry, and other irregular conduction patterns (e.g., patterns that vary in time and space), rotation around a central core or lesion, and / or focal activation spreading from a single location. The algorithm 600 may include evaluation of changes in conduction velocity (e.g., magnitude and / or direction). The algorithm 600 may perform a qualitative and / or quantitative analysis of one or more of these characteristics to provide a complexity assessment.

[0139] The complexity assessment provided by the algorithm 600 may include a binary measurement of whether the complexity occurred one or more times at each location (e.g., each vertex) evaluated. The complexity assessment provided by the algorithm 600 may include a static level of complexity (e.g., sum, mean, median, variance, standard deviation, and / or percentile level) over a period of time. The determined static level is thresholded to calculate and / or display a subset range of static data. The complexity assessment provided by the algorithm 600 may include an assessment of the variability of complexity over time (e.g., over one or more periods of time), such as an assessment of changes in rate, frequency, degree, percentile, and / or probability. The complexity algorithm 600 may perform multiple complexity assessments sequentially, for example, using a "rolling window" as described below with reference to FIG. 8. The multiple complexity assessments may include an assessment of static quantities of complexity over time.

[0140] The complexity algorithm 600 can assess complexity (e.g., complexity changes) and generate results (e.g., diagnostic results 1100) that are used for multiple purposes. For example, the algorithm 600 can provide an assessment of complexity stability and / or consistency and / or other arrhythmogenic conditions based on analyzed recording durations of a few minutes or less (e.g., durations less than 10 minutes). The assessment can distinguish between regions of consistent complexity and regions of transient or intermittent complexity. Consistent regions can be associated with characteristics of specific tissue substrates. In the cardiac system, regions of anisotropic, heterogeneous, abnormal, or diseased tissue substrates may consistently produce variations and / or complexities in electrical activity at that tissue location. However, regions of normal tissue may also see variations or other complexities (e.g., wave collisions, interference, fusion, functional blocks, etc.) due to downstream interactions of complex propagating wavefronts caused by anisotropic regions of the tissue substrate. This complexity is a “functional” effect, where the electrophysiological interactions of propagating waves can cause these waves to interfere or interact in complex, often intermittent, ways. Because cardiac tissue remains refractory (unable to reactivate) for a period of time after each activation, functional effects occur not only at the moment the activation wave passes, but also long after it has passed. The net result is that cardiac tissue activation complexity, as identified by the complexity algorithm 600, may occur in areas where the tissue itself is not abnormal or diseased, but is due to previous complex interactions that occurred at other tissue locations. Fixed, substrate-mediated complexity (or mechanical) recurs stochastically at the same location. Functional complexity may vary in location and frequency of occurrence at a given location. The complexity algorithm 600 may be configured to evaluate consistency, stability, reproducibility, and / or patterns of complexity to distinguish fixed, substrate-mediated complexity from functional complexity, as described below with reference to FIG. 3A.

[0141] The complexity algorithm 600 is used to determine electrical changes resulting from a delivered therapy (e.g., RF or other cardiac ablation, such as therapy provided by the therapy subsystem 800, as described below). A comparison of complexity and / or complexity consistency (herein "complexity") before, during, or between therapeutic activities can be used to indicate the electrophysiological impact of the delivered therapy. The algorithm 600 can provide the comparison in the form of a mean difference plot. Therapeutic events can be brief, such as a few seconds (at a single or few locations), or up to several minutes (for more extensive manipulations such as ablation lines, loops, cores, boxes, etc.). The longer the therapeutic activity or interval, the greater the change in the comparison may be. In some embodiments, the system 100 provides a real-time (e.g., during treatment) feedback loop of cause (treatment) and effect (complexity assessment, such as variation in complexity before and after treatment). System 100 can be configured to provide complexity assessment (e.g., complexity calculation via recorded electrical activity data 120a and algorithm 600) in a relatively short time (e.g., less than 10 minutes, or less than 5 minutes), thereby encouraging clinicians to reduce therapeutic interval times and assess complexity after each interval. In these embodiments, unnecessary ablations can be avoided and / or overall procedure times can be reduced.

[0142] Complexity algorithm 600 may be configured to generate complexity data (e.g., complexity assessment output) in real time, such that complexity data (e.g., diagnostic result 1100) may also be presented dynamically, in real time. For example, system 100 may record and process electrical activity data 120a, and algorithm 600 may analyze the recorded activity over time windows having durations ranging from 5 seconds to 60 seconds, for example, using a rolling window (e.g., as described below with reference to FIG. 8). Algorithm 600 provides multiple complexity assessments by continuously analyzing recorded electrical activity data 120a over the total assessed duration, adding new data and discarding the oldest data as electrical activity data 120a continues to be recorded. The complexity assessment (e.g., multiple complexity assessments provided in video format) may be provided in real time (e.g., with a short processing delay), e.g., during a treatment (e.g., ablation), to dynamically determine when the treatment has achieved a desired result (e.g., when sufficient energy has been delivered to cause a desired effect, such as an electrical block), and / or how to modify the treatment to achieve treatment goals or otherwise improve efficiency. Alternatively, or additionally, the provided complexity assessment may be visualized (e.g., in playback mode) one or more times after the associated recording of electrical activity data 120a has stopped to administer additional treatments and / or modify treatments.

[0143] The complexity algorithm 600 may provide a complexity assessment based on electrical activity data 120 (and / or additional patient data 150, described below) recorded during two separate clinical procedures (e.g., a first clinical procedure and a subsequent second clinical procedure). The algorithm 600 may provide one or more complexity assessments for each clinical procedure, allowing, for example, comparisons to be made between assessments from two different procedures (e.g., assessments made by the algorithm 600). The second clinical procedure may be separated from the first clinical procedure by days, weeks, months, or years. The comparative assessments made by the algorithm 600 may assess the therapeutic effectiveness of the first procedure and cardiac tissue recovery (e.g., healing) or adaptation between treatments. The cardiac tissue may adapt in response to altered electrical properties (e.g., altered patterns, rhythms, etc., from electrical remodeling, etc.) and / or altered mechanical properties (e.g., function) of the tissue, each caused by the aforementioned therapeutic procedures. The approach used in the second clinical procedure can be based on the assessment provided by the algorithm 600 (e.g., in the form of the diagnostic result 1100), the tissue response to the treatment provided in the first procedure (e.g., the electrical and mechanical responses described above), etc.

[0144] While algorithm 600 is described above as analyzing electrical activity data 120, in some embodiments, algorithm 600 further includes in its evaluation an analysis of “additional patient data” recorded by system 100 (e.g., the complexity assessment is based on additional patient data 150 recorded by system 100 as well as electrical activity data 120 and anatomical data 110, as described above). For example, system 100 may include one or more functional elements configured as sensors, such as functional element 99 of catheter 10, functional element 899 of treatment catheter 800 described below, and / or functional element 199 of system 100. Functional element 99 of catheter 10 may include one or more sensors disposed on an expandable spline (as shown) of electrode array 12 and / or shaft 16. Functional element 199 of system 100 may include sensors disposed proximate to the patient (e.g., on the patient's skin or relatively close to the patient) and / or within the patient (e.g., temporarily or chronically disposed beneath the patient's skin). In some embodiments, one or more electrodes 12 a and / or ultrasound transducer 12 b are configured to record additional patient data 150 .

[0145] In some embodiments, the sensor-based functional elements 99, 199, and / or 899 include sensors selected from the group consisting of electrodes or other sensors for recording electrical activity, force sensors, pressure sensors, magnetic sensors, motion sensors, velocity sensors, accelerometers, strain gauges, physiological sensors, glucose sensors, pH sensors, blood sensors, blood gas sensors, blood pressure sensors, flow sensors, optical sensors, spectrometers, interferometers, measurement sensors, such as for measuring size, distance, and / or thickness, tissue assessment sensors, and combinations of one, two, or more of these.

[0146] The additional patient data recorded by system 100 (e.g., via catheter 10, functional element 199, functional element 899, and / or other sensors of system 100) may include patient mechanical information, patient physiological information, and / or patient functional information. The additional data recorded by system 100 may include data regarding patient parameters selected from the group consisting of cardiac wall motion, cardiac wall velocity, cardiac tissue strain, cardiac blood flow magnitude and / or direction, blood vorticity, cardiac valve mechanics, blood pressure, tissue properties such as density, tissue characteristics, and / or tissue properties that are biomarkers of tissue properties such as metabolic activity or drug uptake, tissue composition (e.g., collagen, cardiac muscle, fat, connective tissue), and combinations of one, two, or more thereof.

[0147] As explained above, the one or more complexity assessments performed by algorithm 600 can be based on additional patient data, such as when both electrical activity data 120 and additional patient data 150 are included in the analysis performed. In some embodiments, the complexity assessments performed by algorithm 600 include assessment of one or more of tissue electromechanical delay, the ratio of the magnitude of the electrical property to the mechanical property, and combinations thereof.

[0148] The additional patient data 150 may also include previous data from the same patient (e.g., data collected during a previous procedure) or previous data from a set of historical patterns other than the patient being diagnosed or treated. The data may be used to form a computational model in which the existing patient data is fitted, categorized, ranked, prioritized, optimized, and / or evaluated as described above.

[0149] The diagnostic results 1100 may include measured data and / or data resulting from an analysis of the measured data (e.g., an analysis of recorded electrical activity data 120a and / or anatomical data 110). The diagnostic results 1100 may be provided (e.g., provided to the patient's clinician) in one or more forms, such as displayed on display 27a, provided audibly (e.g., by a speaker of system 100), and / or provided in a printed report (e.g., by a printer of system 100). The diagnostic results 1100 can be used by a clinician to customize a patient's treatment, for example, to determine the location of tissue to ablate in a cardiac ablation procedure, as described, for example, in Applicant's co-pending U.S. patent application Ser. No. 14 / 422,941, filed February 20, 2015, entitled "CATHETER, SYSTEM AND METHODS OF MEDICAL USES OF SAME, INCLUDING DIAGNOSTIC AND TREATMENT USES FOR THE HEART," the contents of which are incorporated herein by reference in their entirety for all purposes.

[0150] In some embodiments, the diagnosis 1100 is based on a complexity assessment performed by the complexity algorithm 600 for a single heart wall location or multiple heart wall locations. The single and / or multiple location diagnosis 1100 may be presented to a user (e.g., the patient's clinician) with reference to an image of the patient's anatomy (e.g., via the display 27a). The diagnosis 1100 may include a complexity assessment over time, e.g., over a predetermined period of time.

[0151] As described above, system 100 may be configured to perform a medical procedure (e.g., a diagnostic, prognostic, and / or therapeutic procedure) related to a patient's arrhythmia or other cardiac condition. System 100 may be configured to perform a medical procedure on a patient having a cardiac condition selected from the group consisting of atrial fibrillation, atrial flutter, atrial tachycardia, atrial bradycardia, ventricular tachycardia, ventricular bradycardia, ectopic, congestive heart failure, angina pectoris, arterial stenosis, and combinations of one, two, or more thereof. In some embodiments, system 100 performs a medical procedure on a patient exhibiting heterogeneous activation, conduction, depolarization, and / or repolarization that vary in time, space, magnitude, and / or state (e.g., a combination of rates, etc.). The electrical activity of the patient's heart may include patterns that can be detected or mapped by the system 100, for example, patterns selected from the group consisting of focal, reentrant, rotational, circling, irregular (e.g., with respect to direction and / or velocity), functional block, permanent block, and combinations thereof.

[0152] System 100 may include a therapy subsystem 800, a device or agent (e.g., pharmaceutical agent) for treating a patient (e.g., treating one or more cardiac conditions of the patient). In the embodiment shown in FIG. 1 , therapy subsystem 800 includes a therapy catheter 850 including a shaft 860, which may be configured to be advanced through the patient's vasculature into one or more chambers of the patient's heart using standard interventional techniques. In some embodiments, a distal portion of shaft 860 is advanced into the patient's left atrium through a transseptal sheath, not shown, such as a standard device used in left atrial ablation procedures. The therapy catheter 850 includes a therapy element 870 at its distal end (shown) or at least at the distal portion of shaft 860. The therapy element 870 may include one or more therapy elements, such as one or more energy delivery elements configured to deliver energy to ablate cardiac tissue (e.g., ablation energy delivered to a heart wall). The therapy element 870 may include an array (e.g., a linear or other array) of therapy elements. Therapeutic element 870 can include one or more electrodes configured to deliver radio frequency (RF) or other electromagnetic energy to tissue. In some embodiments, therapeutic element 870 includes one or more energy delivery elements configured to deliver energy in a form selected from the group consisting of electromagnetic energy, such as RF energy and / or microwave energy, thermal energy, such as heat energy and / or cryogenic energy, light energy, such as laser light energy, acoustic energy, such as ultrasound energy, chemical energy, mechanical energy, and combinations thereof. In some embodiments, therapeutic element 870 includes one or more agent delivery elements (e.g., one or more needles, iontophoretic elements, and / or fluid jets) configured to deliver an agent (e.g., a pharmaceutical agent) to cardiac tissue or other tissue of the patient.

[0153] The treatment subsystem 800 may further include an energy delivery unit, EDU 810, to provide energy to one or more treatment elements 870. The EDU 810 may provide one or more forms of energy selected from the group consisting of electromagnetic energy, such as RF energy and / or microwave energy, thermal energy, such as heat energy and / or cryogenic energy, light energy, such as laser light energy, acoustic energy, such as ultrasound energy, chemical energy, mechanical energy, and combinations thereof. Alternatively or additionally, the EDU 810 may provide a drug to one or more treatment elements 870, such as when the treatment element 870 includes a drug delivery element as described above.

[0154] In some embodiments, the treatment subsystem 800, treatment catheter 850, and / or EDU 810 are constructed and arranged similarly to like components described in applicant's co-pending U.S. patent application Ser. No. 14 / 422,941, filed Feb. 20, 2015, entitled "CATHETER, SYSTEM AND METHODS OF MEDICAL USES OF SAME, INCLUDING DIAGNOSTIC AND TREATMENT USES FOR THE HEART," the entire contents of which are incorporated herein by reference.

[0155] In some embodiments, the therapy subsystem 800 is used to treat the patient based on the diagnostic results 1100 (e.g., results based on the complexity assessment provided by the algorithm 600). For example, ablation energy is delivered to the heart wall at one or more locations (e.g., one or more of the apexes described above), where the complexity assessment determines whether the complexity level for the locations exceeds (e.g., exceeds) a threshold, and therapy is delivered to all locations that exceed the threshold. In some embodiments, in a region of multiple apexes, one apex is selected for ablation, where the system 100 (e.g., via the algorithm 600) determines the maximum complexity level present (e.g., a "local maximum" is ablated), where the maximum complexity level may be an absolute maximum or a relative maximum.

[0156] In some embodiments, the therapy provided by system 100 (e.g., ablation energy delivered to one or more apexes) is delivered in a closed-loop manner, e.g., in manual (clinician-driven), automatic (e.g., system 100-driven), and / or semi-automatic (e.g., combined clinician and system 100-driven) modes. Closed-loop operation may include maneuvering the therapy element 870 to the location to be treated (e.g., via clinician manipulation and / or robotically manipulated therapy device 850 by system 100) and / or setting the energy level delivered.

[0157] 2A and 2B, which respectively illustrate a data structure and a visual representation of a portion of a data structure consistent with the concepts of the present invention. As described above, system 100 may measure and record the size and shape of heart chamber HC to provide an approximation of the shape of heart chamber HC, for example, during diastole. In some embodiments, system 100 measures heart chamber HC via ultrasound transducer 12b of catheter 10, and the measurement information may then be processed by processor 26 and recorded as a set of information defined by a data structure described below. Alternatively or additionally, system 100 may include other imaging elements and / or devices to provide anatomical information of the heart to processor 26. The processed information provided by processor 26 (e.g., anatomical data 110) may be stored as a set of nodes, each including a vertex V of a geometric representation of an anatomical shape, e.g., a triangular mesh representing heart chamber HC illustrated by mesh 80. Each vertex V of mesh 80 is connected to adjacent vertices V by edges E, which are edges of the polygons (e.g., triangles) defining mesh 80.

[0158] Any vertex V may be defined as a central vertex CV. For a central vertex CV, a "neighborhood" surrounding the vertex V may be defined (referred to herein as a "neighborhood" or "neighborhood of a vertex"). For example, a first-neighbor neighborhood may include the central vertex CV and all vertices V connected to the central vertex CV by a single edge E. Furthermore, a second-neighbor neighborhood may include all vertices V connected to any of the central vertex CV's first neighbors by a single edge E. A two-edge-connected neighborhood is shown in FIG. 2B. A multi-edge-connected neighborhood may be defined by the number of edges from the central vertex CV (e.g., in a five-edge-connected neighborhood, each included vertex V is within five edges of the central vertex CV). As used herein, a "border vertex" may be defined as a vertex V included in the neighborhood and located a specific number of edges from the central vertex (i.e., the number of edges defining the size of the neighborhood). A "boundary vertex" may be defined as a vertex V that is one edge connected to a boundary vertex but is not included in the neighborhood (a vertex that is within one edge connection of a boundary vertex but not in the neighborhood).

[0159] For each vertex V, information corresponding to its anatomical location may be recorded and stored by system 100. For example, for a given point in time, biopotential data measured by system 100 may be processed and recorded as a set of values, each corresponding to a vertex V at that time (a "frame" of data). System 100 may be configured to record biopotential or other data over an extended period of time (e.g., 100 ms to 500 ms) represented by multiple consecutive frames, each containing time-related information associated with a vertex V of mesh 80.

[0160] In some embodiments, each frame includes not only biopotential data corresponding to each vertex V, but also other calculated and / or measured information corresponding to each vertex V. For example, system 100 may include one or more algorithms to classify each vertex V for each frame, as described below (e.g., classification information is stored for each frame). Additionally or alternatively, system 100 may “pre-process” the recorded biopotential data and store the results of the processing for each frame. For example, for each vertex V in each frame, BIO processor 36 may determine whether the vertex is “active” at that moment (e.g., along the leading edge of a depolarizing conduction wave propagating through cardiac tissue). In some embodiments, a binary active or inactive “flag” (i.e., a binary yes / no data point) reduces algorithm processing time. Additionally or alternatively, for each vertex V in each frame, a current activation status and activation history may be stored (e.g., the history represents whether the vertex is active or has been active within a predetermined period, such as within the previous 100 milliseconds). In these embodiments, the length of the history recorded for each vertex and / or the resolution of that recording may be selected (e.g., pre-selected by the manufacturer of system 100 and / or selected by the operator) to balance the speed of one or more algorithms of system 100 and the overall resolution of the resulting calculations. As used herein, activations “within” a neighborhood may include all activations recorded for each vertex V in the neighborhood for all frames (e.g., the length of the recording), or may include only activations within a time window (e.g., a rolling time window, described below with reference to FIG. 8 ) of the activation of the central vertex CV of the neighborhood, e.g., within + / −100 ms of the activation of the central vertex CV. In some embodiments, activations are only included in the set of neighborhood activations if they are considered to be within a “minimum and maximum speed estimate,” as described below with reference to FIG. 4 .For example, if activation of a border vertex occurs within 100 ms of activation of a central vertex CV, but the physical distance between the points on the tissue represented by the two vertices is "too long or too short" such that the calculated velocity is not within the maximum or minimum velocity (e.g., the estimated range of physiological conduction in the tissue), the activation will be excluded.

[0161] In some embodiments, system 100 is constructed and arranged to perform one or more algorithms described herein on a portion of mesh 80. For example, a portion of mesh 80 representing tissue proximate a pulmonary vein may be analyzed (e.g., by FA algorithm 500, described below) to identify focal activity, because focal activity near pulmonary veins is associated with patients with arrhythmias such as AF. Additionally or alternatively, one or more algorithms of system 100 may include a bias, and / or one or more thresholds of the algorithm may be adjusted (e.g., biased) based on the anatomy being analyzed. For example, FA algorithm 500 may be biased to identify focal activity near pulmonary veins.

[0162] Referring now to FIG. 3 , a schematic diagram of an algorithm for performing a complexity assessment consistent with the concepts of the present invention is shown. The illustrated algorithm 600 can be included in one or more portions of the system 100 described above, such as when the console 20 includes the algorithm 600. The algorithm 600 is configured to perform the complexity assessment based on recorded biopotential data, such as biopotential data recorded by the electrodes 12 a of the catheter 10. The algorithm 600 may perform the complexity assessment based on electrical activity data 120 (e.g., activation timing data 121) and / or anatomical data 110, as shown in FIG. 3 .

[0163] In step 610, for each frame (as described above), active vertices (as defined above) of anatomical data 110 are determined and activation propagation data is calculated. Step 610 may use an optical flow algorithm (e.g., Horn-Schunck) or other 2D or 3D image-based analysis algorithm to calculate the activation propagation data at each location.

[0164] In step 620, an analysis of the frame-to-frame activation propagation data is performed. This analysis can identify patterns such as rotational patterns, local irregularity patterns, focal activation patterns, and / or other normal or abnormal electrical activity patterns. Patterns can be identified using one or more pattern detection algorithms, such as algorithms 300, 400, and / or 500 described below.

[0165] In step 630, a complexity assessment is performed, e.g., generating a diagnosis 1100. The diagnosis 1100 is provided to a clinician to, e.g., determine a therapy to be administered to the patient (such as one or more cardiac tissue locations for performing a cardiac ablation procedure, e.g., using the therapy subsystem 800 described above with reference to FIG. 1). In some embodiments, the algorithm 600 further includes a complexity algorithm 650 configured to process and / or evaluate the diagnosis 1100, as described below with reference to FIG. 3A.

[0166] The diagnostic results 1100 may include scalar values, e.g., a scalar value assigned to each evaluated vertex and representing a “level” of complexity calculated over a period of time (e.g., period TP described below). Additionally or alternatively, the diagnostic results 1100 may include time-varying values, e.g., a binary value assigned to each evaluated vertex and representing “complex” or “not” calculated for some point in time (e.g., period TP1 described below). In some embodiments, the binary time-varying values ​​are summed or otherwise combined to determine a scalar value of the level of complexity over a longer period of time TP (e.g., periods TP2, TP3, or TP4 described below). In some embodiments, the binary and / or scalar values ​​are “permanently” assigned to vertices in subsequent frames of data; e.g., a binary “yes” may be permanently assigned to a vertex for two, three, or more subsequent frames, potentially overriding a binary “no” from the calculated results. Additionally, repeated positive indicators may be assigned longer persistence, e.g., three binary "yes" frames (for a single vertex) may be assigned five additional "yes" values ​​(for a total of eight, assuming all associated subsequent values ​​are "no"), while a single binary "yes" frame may be assigned only two additional "yes" values ​​(for a total of three).

[0167] In some embodiments, electrical activity data 120a is recorded (e.g., by electrodes 12a) from at least 10, or at least 48, or at least 64 heart wall locations (e.g., in a contact mapping procedure). In these embodiments, the vertices determined by system 100 may include the recording locations and / or other heart wall locations. In these embodiments, the electrical activity data may be recorded simultaneously or sequentially.

[0168] In some embodiments, electrical activity data 120a is recorded (e.g., by electrodes 12a) from at least 10 locations within the heart chamber, or at least 48 locations, or at least 64 locations (e.g., contacting and / or non-contacting the heart wall). In these embodiments, the apexes determined by system 100 may include heart wall-based recording locations and / or other heart wall locations. In these embodiments, electrical activity data 120 may be recorded simultaneously or sequentially.

[0169] 3A, as described above with reference to FIG. 3, a complexity algorithm 650 may be configured to process and / or evaluate the diagnostic result 1100 generated in step 630. In step 6510, the algorithm 650 may evaluate the type and consistency of each complex activation pattern identified in the diagnostic result 1100. In steps 6520 and 6530, the algorithm 650 may evaluate the proximity (e.g., spatial) and / or relationship (e.g., temporal) between each complex activation pattern and then determine whether the identified complex activation pattern is part of a “macro-level” complex activation pattern. In step 6540, the algorithm 650 may apply computational methods to evaluate and / or predict the probabilistic outcome of delivering therapy to the locations of the macro-level complex activation patterns. In some embodiments, the computational methods include data analysis / statistical techniques, such as classification or categorization, of electrical activity using a training data set (e.g., separately acquired data such as historical data) and / or a computationally optimized fit (e.g., machine learning or predictive analysis, such as by neural networks or deep learning, cluster analysis, etc.).

[0170] Step 6540 may be configured to provide an updated diagnosis 1100′ as shown, which may include identifying macro-level complexity, prioritizing therapeutic targets, probabilistic and / or predictive therapeutic strategies, one or more modifications to the diagnosis 1100, and combinations thereof. In some embodiments, the probabilistic outcome of delivering therapy is determined or provided through the use of machine learning and is described in Applicant's co-pending U.S. Provisional Patent Application No. 62 / 668,659, filed May 8, 2018, entitled “CARDIAC INFORMATION PROCESSING SYSTEM,” the contents of which are incorporated herein by reference in their entirety for all purposes. In some embodiments, the predictive therapeutic strategy may be to transition the current rhythm to a less complex rhythm (e.g., transition from atrial fibrillation to atrial tachycardia), a strategy determined, for example, using state analysis. The state of the current rhythm may be defined by one or more complexity measures (e.g., cycle length, number of cardiac waves, Shannon entropy, and / or dominant frequency). The change in state can be estimated for various therapeutic strategies (e.g., various ablation locations and / or durations). The therapeutic strategy estimated to change the rhythm to the least complex state can then be implemented. The complexity algorithm 650 can take other patient data (e.g., MRI / CT data, patient health history data, and / or previous ablation history data) as input.

[0171] The complexity algorithm 600 can include an analysis of the recorded electrical activity data 120a, which is recorded over time periods TP, which can include similar or different lengths of time. Each time period TP can represent all or a portion of a continuous recording for that time period TP, or all or a portion of multiple recordings that cumulatively represent the time period TP. In some embodiments, the time period TP represents two or more periods of recording electrical activity and the time between recordings. In some embodiments, the data recorded over a time period is segmented into multiple time periods TP (e.g., multiple periods of the same duration), and a complexity assessment is calculated for each time period TP. The complexity assessment can then be displayed to the user in a video-like format (e.g., displayed on display 27a, as described below with reference to FIG. 8). In some embodiments, each time period TP (e.g., time period TP2 described below) includes a sufficiently long time period TP so that the user can reasonably perceive the displayed information in a “real-rate” manner (e.g., the information is displayed at the same rate as it occurs). In these embodiments, the displayed information may be presented in a “real-time” manner (e.g., information is displayed as it occurs, with minimal delay due to processing by system 100). Alternatively or additionally, period TP may include a sufficiently short period (e.g., period TP1 described below) such that a user would not be able to reasonably perceive the displayed information if displayed at real-time. In these embodiments, a rolling “average” of the data may be displayed at real-time, and / or the data may be replayed frame-by-frame or in other slow-motion manner to allow a user to reasonably perceive the data. Additionally or alternatively, various methods of displaying cumulative, total, average, or persistent data may be implemented to provide a user with a perceptible time-dependent representation of the calculated data. Furthermore, each period TP (e.g., TP3 and / or TP4 described below) may include an extended period and / or a period spanning two or more separate recordings, allowing a time-compressed (e.g., time-lapse) data set to be displayed to the user. Playback and other data display modes are described in more detail below with reference to FIG. 8.

[0172] In some embodiments, the time period TP1 comprises a relatively short period of time, such as a period during which 1 to 10 activations occur in the cardiac tissue being evaluated (e.g., as represented by a set of vertices as described herein). Similarly, TP1 may comprise a duration between 0.3 milliseconds and 2000 milliseconds, e.g., a period of approximately 150 milliseconds. In some embodiments, the catheter 10 comprises a contact mapping catheter (e.g., a “roving” contact mapping catheter configured to record electrical activity data 120a via electrodes 12a from only a single, distinct portion of a heart chamber at a time). In these embodiments, the time period TP1 may approximate a “visit,” which is the total recording time at a single, distinct portion of a heart chamber. Subsequent time periods TP1 may approximate subsequent visits to the same or different portions of the heart chamber. In these embodiments, two, three, or more recordings, each comprising a period approximately equal to TP1, may be combined to create a more complete dataset of recorded electrical activity. Two, three, or more recordings can be combined spatially based on the portion of the heart chamber recorded and temporally based on cardiac cycle information, as is known in the art of contact cardiac mapping. In some embodiments, catheter 10 includes a mapping catheter (e.g., a basket catheter) configured to record electrical activity data 120a via electrodes 12a from a distributed set of locations around the entire circumference of the heart chamber, the electrode locations intended to be in contact with or adjacent to the heart wall. In some embodiments, catheter 10 includes a mapping catheter (e.g., a basket catheter) configured to record electrical activity data 120a via electrodes 12a from a distributed set of locations offset from the heart wall.

[0173] In some embodiments, complexity algorithm 600 includes analysis of electrical activity data 120a recorded for a period TP2, including a moderate number of electrical activations, e.g., between 3 and 3000 activations, e.g., between 10 and 600 activations, or between 25 and 300 activations. Correspondingly, TP2 may include a duration between 0.3 and 500 seconds, e.g., between 1 and 90 seconds, or between 4 and 30 seconds. In some embodiments, period TP2 represents the length of a single data recording, e.g., a contact and / or non-contact recording of intra-chamber electrical activity data 120a.

[0174] In some embodiments, complexity algorithm 600 is configured to analyze electrical activity data 120a recorded for a time period TP3, which may include a large number of electrical activations, e.g., 2,000-300,000 activations, e.g., 6,000-40,000 activations. Correspondingly, TP3 may include a duration of 5 minutes to 8 hours, e.g., 15 minutes to 60 minutes. In some embodiments, time period TP3 represents the length of several recordings of acute electrical activity, e.g., several recordings obtained before, after, and / or between looped iterations of diagnosis and therapy (e.g., therapy provided by therapy subsystem 800 described above with reference to FIG. 1).

[0175] In some embodiments, the complexity algorithm 600 is configured to analyze activation and / or electrical data from measurements made at a regional focus. The regional focus may include a region of tissue comprising approximately 5%-50% of the cardiac chamber surface (e.g., 5%-50% of the endocardial surface of an atrium or ventricle). Measurements may be made for a time sufficient to capture the complex conduction characteristics representative of the rhythm, for example, capturing approximately 3-3,000 activations. In some embodiments, the electrode array 12 is sequentially manipulated to different locations to form a collective map including data from each location.

[0176] In some embodiments, the complexity algorithm 600 includes an analysis of the electrical activity data 120a recorded for a period TP4, including a period of days, weeks, months, and / or years (e.g., across multiple clinical diagnostic procedures performed on a patient). In some embodiments, the period TP4 represents the length of several electrical activity recordings across multiple clinical procedures, e.g., over days, weeks, months, or years.

[0177] In some embodiments, complexity algorithm 600 receives additional patient data 150 and includes both electrical activity data 120 and patient data 150 in its complexity analysis, for example, as described above with reference to FIG. 1. In some embodiments, complexity algorithm 600 includes one or more of algorithms 200, 300, 400, and / or 500 described below, each of which may include a complexity assessment based on electrical activity data 120, anatomical data 110, and / or additional patient data 150.

[0178] Referring now to FIG. 4, a schematic diagram of an algorithm for determining conduction velocity data consistent with the concepts of the present invention is shown. System 100 may include a conduction velocity algorithm, CV algorithm 200, that analyzes anatomical data, represented by data 110, and activation timing data, represented by data 121. Algorithm 600 of the above complexity may include CV algorithm 200. CV algorithm 200 may include one or more instructions executed by a processor of system 100, such as processor 26 of console 20. CV algorithm 200 may process anatomical data 110 and electrical activity data 120 (e.g., activation timing data 121) for each activation of the associated vertex to determine conduction velocity at each vertex of anatomical data 110, as described herein.

[0179] In some embodiments, the CV algorithm 200 calculates one or more components of the velocity (direction and / or magnitude) at each vertex of the anatomical data 110 as the depolarizing conduction wave passes through the vertex. Conduction velocity (e.g., the velocity at each vertex as the depolarizing conduction wave passes through the vertex) can be determined by determining the spatial gradient of the activation time (τ) using the following equation:

number

[0180] Each processed vertex can be considered a "central vertex," and a small "neighborhood" of vertices and activation times adjacent to each central vertex can be used to estimate the spatial gradient and determine the conduction velocity at the central vertex. In some embodiments, a method for estimating the spatial gradient of activation times for a vertex given a small neighborhood and the location of the vertex in the small neighborhood includes fitting the activation times of the neighborhood to a function (e.g., a polynomial function) of the vertex's location. In some embodiments, polynomial surface fitting is used.

[0181] The CV algorithm 200 may process each frame of anatomical data 110 and electrical activity data 120a recorded by the system 100. Steps 210-250, described below, process a single frame of data. Multiple frames may be processed by repeating steps 210-250 with subsequent frames.

[0182] In step 210, a set of active vertices is determined using anatomical data 110 and electrical activity data 120 (eg, activation timing data 121).

[0183] In step 220, for each active vertex of the anatomical structure (for the current frame), a neighborhood of the vertex may be defined around that vertex (e.g., the middle vertex of that neighborhood). In some embodiments, a neighborhood connected by multiple edges (e.g., 5) may be used to define a neighborhood of approximately 200 mm of the anatomical surface. 2~315mm 2 We define a neighborhood covering τ, where the neighborhood (e.g., the neighborhood described above with reference to Figure 2B) contains 60–120 vertices. Within a neighborhood defined by multiple edge-connected neighborhoods, all activation times τ are required to be within a certain minimum velocity estimate (e.g., a minimum velocity estimate of approximately 0.3 m / s), and velocity is estimated as follows:

number

[0184] The principal components of this neighborhood are then determined by creating a matrix of all vertex locations in the mean-removed neighborhood. Using singular value decomposition (SVD) of the matrix of vertex locations, we can determine the three singular vectors of the local neighborhood that correspond to the principal components of the neighborhood. The vertex locations in the neighborhood are then calculated by adding the singular vectors to the neighborhood P original is converted to a bias defined by the principal components of the neighborhood by multiplying the position of each vertex of P original × singular vector = P prinipal This becomes:

[0185] After the transformation, the neighborhood is i ,v i ,k i ), where (u i ,v i ,k i ) are the amounts of the first, second, and third principal components, respectively, and are expressed as follows: th ) is used to describe the position of the vertices.

number

[0186] In some embodiments, optional step 230 is performed. In step 230, P prinipal The singular vector with the smallest singular value in is removed, resulting in a transformation from the 3D domain to the 2D planar domain, which is performed using the function

number

[0187] The resulting plane is a best-fit plane of the 3D positions of the vertices transformed into a 2D plane. A 3D to 2D transformation can be performed to ensure that the calculated conduction velocities are tangent to the surface anatomy and / or to reduce the dimensionality of the polynomial surface fitting performed in subsequent steps, described below.

[0188] In step 240, a function (e.g., a best fit cubic polynomial surface function, T) is used to calculate the function of position (u i ,v i ) as the local activation time τ i For example, write T(u i ,v i )≒τ i becomes.

number

[0189] Given the set [u,v]=τ, the following matrix can be constructed to solve for the coefficients A:

number

[0190] The above can be solved by least squares analysis. The singular value decomposition is the matrix A: A = USV T to compute the pseudo-inverse of A, which can be used to compute the coefficients.

number

[0191] In step 250, the conduction velocity can be solved analytically by taking the derivative of the surface (eg, polynomial surface T), as shown below:

number

[0192] The conduction velocity can then be normalized to create a unit vector, for example using the following equation:

number

[0193] Through the previous steps, the algorithm 200 generates a set of conduction velocity data, denoted as data 122 , which is based on the anatomical data 110 and the activation timing data 121 .

[0194] In some embodiments, the conduction velocity data 122 can be represented on an anatomical surface (e.g., via display 27a of system 100) by converting the resulting conduction velocity unit vector back to the original coordinate system (e.g., the coordinate system of anatomical data 110), for example, using the following equation:

number

[0195] For each activation (eg, each activation at each central vertex in each frame), conduction velocity can be expressed in two and / or three dimensions, for example, using the following equation:

number

[0196] Referring now to FIG. 5, a schematic diagram of an algorithm for determining localized rotational activity consistent with the concepts of the present invention is shown. System 100 may include LRA algorithm 300, an algorithm for determining localized rotational activity. The complexity algorithm 600 described above may include LRA algorithm 300. LRA algorithm 300 may be configured to determine angular variation in conduction velocity relative to a central apex. In patients with atrial fibrillation (AF) and other arrhythmias, cardiac electrical activity may manifest as rotors (e.g., rotational electrical activity around a central obstruction). Such rotational activity has long been thought to play a major role in the persistence of cardiac arrhythmias such as AF (e.g., rotational activity is associated with causing and / or perpetuating these undesirable conditions).

[0197] In some embodiments, the LRA algorithm 300 is used to process each frame of anatomical data 110 and electrical activity data 120 (e.g., activation timing data 121) collected by the system 100. Steps 310-360, described below, perform processing of a single frame of data. Multiple frames may be processed by repeating steps 310-360 with subsequent frames. In some embodiments, the LRA algorithm 300 further includes conduction velocity data 122 in its analysis. Alternatively or additionally, the LRA algorithm 300 may be configured to determine the conduction velocity data 122, for example, if the LRA algorithm 300 is configured similarly to the CV algorithm 200.

[0198] In step 310, a set of active vertices is determined using anatomical data 110 and electrical activation data 120 (eg, activation timing data 121).

[0199] In step 320, for each active vertex of the anatomical structure (in the current frame), a neighborhood of the vertex may be defined around that vertex (e.g., the central vertex of the neighborhood). For each neighborhood, a ring of vertices around the central vertex may be defined by the boundary vertices of the neighborhood, as shown in Figures 5A and 5B.

[0200] In step 330, for each neighborhood, the activation times and conduction velocities of the vertices within the neighborhood may be grouped (e.g., binned). For each neighborhood, all activation times within a certain maximum velocity estimate (e.g., a maximum velocity estimate of approximately 0.05 m / s) may define (e.g., restrict) the set of activations to be grouped. In some embodiments, only activation times reachable from the central vertex activation of the group at a given maximum velocity (e.g., 0.05 m / s) are included in the group. The activations in each neighborhood may be grouped as shown in FIG. 5B. In some embodiments, the average activation timing data 121 and / or average conduction velocity data 122 for all activations within the group are also assigned to the boundary vertices, as shown in FIG. 5B.

[0201] In step 340, vertices with linear trends (e.g., increasing or decreasing trends) in activation time around the outer ring of vertices are identified. For example, a linear fit with R2≧0.7 may be identified as a trend. Figure 5D shows the activation time trend line.

[0202] In step 350, the total angular change between the average conduction velocities assigned to the first and last vertices of the linear trend identified in step 340 is determined. Figure 5E shows the conduction velocities of the identified linear trend transformed to the origin 0,0. Figure 5E graphically illustrates the total angular change between average conduction velocities as described above.

[0203] In step 360, the LRA algorithm 300 classifies the central vertex as "rotational" if the linear trend identified in step 340 exceeds a threshold (e.g., an operator-defined threshold) and / or if the total angular change identified in step 350 exceeds a threshold.

[0204] The LRA algorithm 300 generates a set of data (e.g., creates new data and / or modifies existing data), which is classified activation data 140 (e.g., data that has been filtered, categorized, identified, and / or classified to identify activations as rotational in nature).

[0205] Referring now to FIG. 5A, a graphical representation of anatomical data 110 is shown, including a neighborhood of a vertex defined by an outer ring of the vertex.

[0206] Referring now to FIG. 5B, a simplified representation of a vertex neighborhood is shown, including an outer ring of vertices arranged around a central vertex. In some embodiments, activation within the neighborhood is segmented or binned and then averaged. The average value may be assigned to a single vertex, such as the boundary vertex within the segment. For example, all activation within the neighborhood area represented by the shaded region S1 may be averaged and "assigned" to vertex V1. In some embodiments, binning is performed to limit the effect of noise on subsequent calculations performed on the data. In some embodiments, the size of the segment S1 is selected to increase the resolution of the system 100 (e.g., smaller segments) or decrease subsequent calculation time (e.g., larger segments).

[0207] Referring now to Figure 5C, a representative anatomical structure is shown illustrating an exemplary propagating wave rotating around a neighborhood, where the neighborhood is defined by an outer ring of vertices arranged around a central vertex. The mean conduction vector is also shown from each boundary vertex of the ring.

[0208] Referring now to FIG. 5D, a plot of activation time for the outer ring of vertices of FIG. 5C is shown, where activation time is plotted against degrees around the central vertex. As noted above, the points on the plot represent a set of vertices within the ring with a linear trend. In the data shown in FIG. 5D, the trend extends from about 200° to about 375°, indicating that the cardiac wave propagated 175° around the central vertex.

[0209] Referring now to FIG. 5E, a graph of the conduction velocity vectors associated with FIG. 5C is shown, with the vectors transformed to the 0,0 point. The change in conduction velocity around the central apex can be determined by summing the angles between successive conduction velocity vectors. In this example, the conduction velocity vectors of the illustrated data, represented by angle α, sum to 155°.

[0210] Referring now to FIG. 6, a schematic diagram of an algorithm for determining local irregular activity consistent with the concepts of the present invention is shown. The system 100 may include an LIA algorithm 400, which is an algorithm for determining local irregular activity. The complexity algorithm 600 described above may include the LIA algorithm 400. The LIA algorithm 400 may be configured to determine the angle between the direction of conduction approaching the central apex and the direction of conduction leaving the central apex. Irregular activity, such as significant fragmentation, irregular reentrant activity, and / or disorganized conduction, has long been believed to play a major role in the persistence of cardiac arrhythmias, including AF.

[0211] In some embodiments, the LIA algorithm 400 is used to process each frame of anatomical data 110 and electrical activity data 120 (e.g., activation timing data 121) collected by the system 100. Steps 410-460, described below, perform processing of a single frame of data. Multiple frames may be processed by repeating steps 410-460 with subsequent frames. In some embodiments, the LIA algorithm 400 also includes conduction velocity data 122 in its analysis. Alternatively or additionally, the LIA algorithm 400 may be configured to determine conduction velocity data 122, for example, if the LIA algorithm 400 is configured similarly to the CV algorithm 200.

[0212] In step 410, a set of active vertices is determined using the anatomical data 110 and the activation timing data 121.

[0213] In step 420, for each active vertex of the anatomical structure (in the current frame), a neighborhood of the vertex may be defined around that vertex (e.g., the central vertex of the neighborhood). As shown in Figure 5A, for each neighborhood, a ring of vertices around the central vertex may be defined by the boundary vertices of the neighborhood.

[0214] In step 430, for each neighborhood, the LIA algorithm 400 may be configured to determine the average conduction velocity direction for all activations within the neighborhood that have an activation time (within the maximum conduction velocity, e.g., 0.3 m / s to 3 m / s) earlier than the activation time of the central vertex and have a conduction velocity direction toward the central vertex. In some embodiments, only a subset of these activations are included in the calculation of the average conduction velocity direction.

[0215] In step 440, for each neighborhood, the LIA algorithm 400 may be configured to determine the average conduction velocity direction for all activations within the neighborhood that have activation times slower than the activation time of the central vertex (within the maximum conduction velocity, e.g., 0.3 m / s to 3 m / s) and have conduction velocity directions away from the central vertex. In some embodiments, only a subset of these activations are included in the calculation of the average conduction velocity direction.

[0216] In step 450, the LIA algorithm 400 determines the angle between the mean conduction velocity direction entering the neighborhood and the mean conduction velocity direction leaving the neighborhood.

[0217] In step 460, the LIA algorithm 400 classifies the central vertex as "irregular" if the angle determined in step 450 exceeds a threshold (e.g., an operator-defined threshold). The LIA algorithm 400 generates a set of data (e.g., creates new data and / or modifies existing data) that is classified activation data 140 (e.g., data that has been filtered, categorized, identified, and / or classified to identify activations as irregular in nature). In some embodiments, a vertex may be previously classified as rotational (e.g., if the LRA algorithm 300 was previously run), and the LIA algorithm 400 does not reclassify or additionally classify the vertex as irregular. Alternatively or additionally, the classified activation data 140 may allow multiple classifications for each vertex. In these embodiments, the system 100 may be configured to apply weighting factors or prioritize certain classifications; for example, a rotational classification may be considered more important than an irregular classification.

[0218] Referring now to FIG. 6A, an example of a propagating wave exhibiting irregular activation consistent with the concepts of the present invention is shown. FIG. 6A shows a propagating wave PW1 entering a small region, dot CV. Conduction velocities from PW1 can be averaged to determine the average conduction velocity direction entering region CV. FIG. 6A also shows a propagating wave PW2 exiting region CV. Conduction velocities from PW2 can be averaged to determine the average conduction velocity direction leaving region CV. The LIA algorithm 400 can be configured to determine the angle β between the direction of conduction approaching the CV and the direction of conduction leaving the CV (as described above). The LIA algorithm 400 can classify the central apex of that activation period as irregular if the angle exceeds a threshold (e.g., a user-defined threshold, also as described above).

[0219] Referring now to FIG. 7, a schematic diagram of an algorithm for determining focal activation consistent with the concepts of the present invention is shown. System 100 may include FA algorithm 500, an algorithm for determining focal activation (also called focal activity). Algorithm 600 of the above complexity may include FA algorithm 500. FA algorithm 500 may be configured to determine whether activation at the apex originates from a previous cardiac wavefront or whether activation spontaneously initiates from the apex (known as focal activation). Focal activation is detected at the apex when the activation is earlier than activation at neighboring apexes, and conduction spreads outward from the apex. Focal activation from pulmonary veins has been shown to play a central role in the sustainment of paroxysmal AF. More generally, focal activity is also believed to play a major role in the sustainment of cardiac arrhythmias, including AF.

[0220] In some embodiments, the FA algorithm 500 is used to process each frame of anatomical data 110 and electrical activity data 120 (e.g., activation timing data 121) collected by the system 100. Steps 510-560, described below, perform processing of a single frame of data. Multiple frames may be processed by repeating steps 510-560 with subsequent frames. In some embodiments, the FA algorithm 500 also includes conduction velocity data 122 in its analysis. Additionally or alternatively, the FA algorithm 500 may be configured to determine conduction velocity data 122, for example, if the FA algorithm 500 is configured similarly to the CV algorithm 200. In some embodiments, the FA algorithm 500 includes conduction divergence data 123, as defined below, in its analysis. The conduction divergence data 123 may be generated by the FA algorithm 500 and / or another algorithm of the system 100 (e.g., generated prior to application of the FA algorithm 500).

[0221] In some embodiments, the conduction divergence data 123 includes the divergence of conduction velocity from each vertex of the anatomical data 110. The divergence of the conduction velocity field may be defined as:

number

number

[0222] A vertex is classified as "well-defined" in conduction divergence data 123 if the conduction velocity divergence is determined to have a positive value above a threshold for all activations of all vertices. In some embodiments, divergence is classified as well-defined if half of the vertices in a neighborhood connected by multiple edges (e.g., five) have conduction velocities within a minimum conduction velocity range. A positive divergence threshold of 0.05 may be used.

[0223] In step 510, a set of active vertices is determined using the anatomical data 110 and the activation timing data 121.

[0224] In step 520 , a set of diverging active vertices is identified from the set of active vertices determined in step 510 .

[0225] In step 530, for each divergent active vertex, a neighborhood of the vertex is defined around that vertex (e.g., the central vertex of the neighborhood). For each neighborhood, a ring of vertices around the central vertex may be defined by the boundary vertices of the neighborhood, as shown in FIG. 5A.

[0226] In step 540, a set of "boundary vertices" is defined, the set including the neighbors connected by one edge to each boundary vertex of the neighborhood.

[0227] In step 550, the activation time of each boundary vertex defined in step 540 is determined.

[0228] In step 560, the FA algorithm 500 classifies a central vertex as "focal" if the activation time of each of its boundary vertices is slower than the activation time of the central vertex. The FA algorithm 500 generates a set of data (e.g., creates new data and / or modifies existing data) that is classified activation data 140 (e.g., data that has been filtered, categorized, identified, and / or classified to identify activations as focal in nature). In some embodiments, a vertex may have been previously classified as rotational and / or irregular (e.g., if the LRA algorithm 300 and / or the LIA algorithm 400 were previously run), and the FA algorithm 500 does not reclassify or additionally classify the vertex as focal. Alternatively or additionally, the classified activation data 140 may allow for multiple classifications for each vertex. In these embodiments, system 100 may be configured to apply weighting factors or prioritize particular classifications (as described above), for example, rotational classifications may be considered more important than irregular and / or focal classifications.

[0229] Referring now to Figures 7A and 7B, representative anatomical structures showing focal activation and focal and passive activation, respectively, are shown, consistent with the concepts of the present invention. As shown in Figure 7A, a dot CV indicates the vertex currently being evaluated. A boundary vertex BV is shown surrounding a propagating wavefront PW3 extending from the dot CV. As shown in Figure 7B, a dot CV1 indicates the first vertex, and a dot CV2 indicates the second vertex. Zoom window (i) in Figure 7B shows the vertex's neighborhood around CV1, and zoom window (ii) in Figure 7B shows the vertex's neighborhood around CV2. In the zoom window in Figure 7B, the neighborhoods are shown projected onto a plane and interpolated to a regular grid. As mentioned above, the complexity algorithm 600 can include a supervised learning algorithm, e.g., a learning algorithm trained with an appropriately labeled training set. The neighborhood of the central region (e.g., the region around the vertex CV) can be interpolated to a regular nxm grid, with each value at a grid point containing an activation time, as shown in zoom windows (i) and (ii) of FIG. 7B. Temporal information can be added by concatenating multiple images. Once the activation times are on a regular grid, a learning algorithm (e.g., a feedforward neural network, a convolutional neural network, a support vector machine, etc.) can be trained on a large patient set to identify conduction patterns of interest given images of the conduction patterns. After the activation time data is evaluated for the conduction pattern of interest and simultaneously transformed into image space, the labeled output can be converted back and displayed (e.g., in 3D anatomical space). In some embodiments, the complexity algorithm 600 can be configured to identify electrical patterns selected from the group consisting of LIA, LRA, focal, slow conduction velocity, isthmus-like conduction, figure-of-eight conduction, loop conduction such as double, triple, or multi-loop conduction, gyratory reentry, and combinations thereof. For example, as shown in zoom (i) of FIG. 7B, focal conduction is shown, for example, focal conduction identified by algorithm 600 as a region of interest.As shown in zoom (ii) of FIG. 7B, passive conduction is shown, for example, passive conduction identified by algorithm 600 as a region of "non-concern."

[0230] Referring now to FIG. 8 , an embodiment of a display capable of rendering cardiac data (e.g., activation and / or other biopotential and / or anatomical data) consistent with the concepts of the present invention is shown. The cardiac data can consist of a series of frames of data that can be dynamically displayed as a function of time. Display 1400 of FIG. 8 can be generated using the same processor, modules, and databases described above to render other displays, such as display 27a of FIG. 1 . In some embodiments, system 100 and / or display 1400 can be constructed and arranged similarly to the displays described in applicant's co-pending International PCT Patent Application No. PCT / US2017 / 030915, filed May 3, 2017, entitled “CARDIAC INFORMATION DYNAMIC DISPLAY SYSTEM AND METHOD,” the contents of which are incorporated herein by reference in their entirety for all purposes.

[0231] Within a main cardiac information display window or region, in window 1405 (e.g., a portion of display 1400), a digital model of cardiac anatomy 1402 is shown with cardiac activation data superimposed or overlaid thereon. In this embodiment, the cardiac activation data is rendered and activation states are indicated by a series of colors superimposed on the digital cardiac model 1402.

[0232] Display 1400 may simultaneously display two or more unique graphical representations representing different physiological parameters of one or more portions of the heart as represented by the displayed digital heart model 1402. The various graphical representations used to represent these physiological parameters may be selected from the following group: colors, color ranges, patterns, symbols, shapes, opacity levels, stipples, hues, geometric shapes of 2D or 3D objects, and combinations thereof. The graphical representations used to represent physiological characteristics may be static and / or dynamic.

[0233] Simultaneous displays of multiple physiological characteristics (e.g., distinguished via various graphic displays) may be superimposed in one or more combinations on one or more digital models of cardiac anatomy. Various physiological parameters, such as minimum reactivation time, conduction velocity, the number of occurrences in which the vorticity threshold is exceeded during a period, and / or other physiological parameters, may each be represented by a unique graphic display. Cross-hatch patterns with discrete levels of hatch density and / or line thickness may be superimposed on the digital model to identify regions classified into different conduction velocity categories. Surface spheroids may be superimposed around nodes where the vorticity is greater than a threshold, with the diameter of the spheroid displayed proportional to the number of occurrences in which the vorticity threshold is exceeded during the duration of cardiac activity. Hatch patterns and spheroids are provided herein as non-limiting examples of graphic displays.

[0234] In some embodiments, a display of the electrocardiogram EGM 1410 is presented in an auxiliary cardiac information display window 1415 below the main cardiac information display window 1405 displaying the reconstructed heart 1402 .

[0235] A set of user-interactive controls, controls 1420, may include a window width control 1422 configured to allow a user to set the duration of the display (e.g., the duration the calculated data displayed represents) in the main cardiac information display window 1405, shown here set to 30 milliseconds. The window width (duration) is indicated by a semi-transparent sliding window, window 1412, superimposed on the EGM 1410. A user-selectable and / or configurable display scale, scale 1424, is also provided, which may be used to adjust the time scale, t SCALE can be set, where t SCALE is set to 3 milliseconds. Thus, the horizontal axis of EGM 1410 includes 3 millisecond increments. Controls 1426, which are play, rewind, and fast forward controls, are also included as shown.

[0236] In some embodiments, the diagnostic results 1100 are displayed in the main cardiac information display window 1405; for example, a graphical representation of the complexity assessment may be displayed superimposed on the reconstructed heart 1402 (e.g., a complexity assessment including a calculation of the complexity of each vertex of the reconstructed heart 1402). In these embodiments, the window width of the window 1412 may indicate the portion of the recorded data analyzed in the indicated complexity assessment (e.g., the time period the displayed complexity assessment represents). For example, the displayed complexity assessment may include an average of several complexity assessments (calculated over two or more time periods shorter than within the window 1412). Calculations of various complexity assessments are described above. The width of the window 1412 may be user-selectable and / or adjustable to generate a complexity assessment that includes data from longer or shorter time periods. Two or more complexity assessments may be displayed in a frame-by-frame manner (e.g., a movie), with the window 1412 “rolling” across the EGM 1410 (e.g., a “rolling window”), showing the segment of data analyzed for each frame. Alternatively or additionally, a user can manually position or adjust the window 1412 to generate a complexity assessment of a desired segment of the recorded data.

[0237] The semi-transparent sliding window 1412 is synchronized with the cardiac activation data shown overlaid on the reconstructed heart 1402. Thus, the semi-transparent sliding window 1412 and the cardiac activation data overlaid on the reconstructed heart 1402 can change dynamically with respect to a common time scale. The displays are temporally linked and change together because their output is based on the same time-dependent data.

[0238] Controls 1428, a set of display mode or layer controls, may be provided to allow a user to control at least a portion of the display of main window 1405, and in particular, at least a portion of the display of cardiac activation data of reconstructed heart 1402. In this embodiment, separate "buttons" (e.g., electromechanical switches, touchscreen icons, and / or other user-interactive controls) are provided as controls 1428 for selecting "Color Map," "Texture Map," "Shade Map," and "Pattern Map" graphical options. In some embodiments, one or more such controls are provided. Not all such controls need be provided in all embodiments. In some embodiments, none of controls 1428 need be provided.

[0239] 8, a reconstructed heart chamber 1402 is shown with cardiac activation data represented as changing colors (e.g., changing grayscales in response to a color map button). For illustrative purposes, a portion of the reconstructed heart chamber 1402 is shown with a texture map 1404 in response to a texture map button, a shade map 1406 in response to a shade map button, and a pattern map 1408 in response to a pattern map button. That is, in some embodiments, such buttons (or similar controls) are used to selectively turn on each map.

[0240] For example, size graphics (e.g., graphics showing roughness, texture, etc.), which may be uniform, and / or directional graphics (e.g., grain, lines, spikes, etc.), may be directional and may be overlaid on surface anatomical structures to visualize conduction or substrate properties. The z-height "roughness" of the size graphics may be increased or decreased proportionally to the degree of the property being displayed (e.g., the magnitude of the property). Also, block orientation may be indicated with directional graphics (e.g., spikes shown in texture map 1404 of FIG. 8).

[0241] Continuing with the above example, shading and / or a distinct fixed color palette or gradient (different from other color palettes used), e.g., grayscale, can be used to distinguish various degrees of block, such as fixed block, directional block, and / or functional block conditions.

[0242] Multidirectional regions of activation can be displayed with different unidirectional textures or lines overlaid, creating a "hatch" pattern, as shown in pattern map 1408. Calculations of indices of fibrosis and / or other physiological condition indices characterizing the surface / substrate can be displayed with uniform textures, e.g., fine patterns such as a pattern resembling cement in appearance, or coarse patterns such as a pattern resembling pebbles in appearance. Indicators of fibrosis or other physiological conditions presenting obstacles or obstructions in the conduction pattern can be determined by a combination of velocity, directional uniformity, and / or other conduction pattern characteristics.

[0243] The incorporation of textures, patterns, shading, etc. into the surface of the heart chamber 1402 provides a way to provide more information (e.g., visually) in conjunction with other types of cardiac activity information. This configuration is an enhanced implementation of visual "layers" of the map display, which can be used individually or in any combination to provide information related to multiple variables simultaneously, such as through the use of user interactive controls 1420.

[0244] In some embodiments, one or more of the vertex classifications described herein are shown on the reconstructed heart chamber 1402. In these embodiments, the classification may be indicated as described above, such as with a color overlay and / or other graphic display. In some embodiments, colored or otherwise distinguishable “dots” are used to indicate vertices that have been classified as having a particular property (herein “classified”). Overlapping dots and / or other indicators may be used to indicate multiple classifications (e.g., multiple similar and / or different classifications). Overlapping indicators may be displayed in the same location using different radii, heights from the surface of the anatomy, and / or offsets along the surface of the anatomy in different directions. In some embodiments, the graphic indicator is displayed “persistently,” e.g., if a vertex is classified in a first frame, the classification indicator may persist on the display for one or more subsequent frames. Additionally or alternatively, the classification indicator may be displayed for multiple vertices, e.g., for vertices connected by two edges of the classified vertex.

[0245] 9 and 9A, a schematic diagram of a mapping catheter and a perspective anatomical view of a heart chamber into which the mapping catheter has been inserted are respectively shown consistent with the concepts of the present invention. The catheter 10′ includes an electrode array 12′ including one, two, three, or more electrodes 12a. In some embodiments, the electrode array 12′ includes fewer than 24 electrodes, e.g., fewer than 12 electrodes, such as 10, 8, 6, 4, or 3 electrodes. The electrode array 12′ may include an array of expandable splines to which the electrodes 12a are attached. The catheter 10′ may be percutaneously inserted into a patient to deliver the electrode array 12′ percutaneously to a heart chamber (HC), and may be constructed and arranged similarly to the catheter 10 described above with reference to FIG. 1. FIG. 9A shows the electrode array 12′ percutaneously inserted into a heart chamber (HC). The electrode 12a is positioned in contact with a portion of the heart wall, resulting in electrical activity data 120a that can be recorded, for example, by the system 100 described herein. A region of analysis is shown surrounding the tissue proximate the contact location of the electrode 12a. In some embodiments, the recorded electrical activity data 120a is processed by the system 100, for example, by performing a complexity analysis using the algorithm 600 described above with reference to FIG. 3, and the generated diagnosis 1100 can be “assigned” to the region of analysis (e.g., the diagnosis is recorded in relation to a vertex of an anatomical model represented within the region of analysis). In some embodiments, the diagnosis 1100 for the region of analysis indicates potential therapeutic benefit from an intervention (e.g., tissue ablation) in the region of analysis (e.g., with or without collection and / or analysis of data from other regions of the heart chamber). In some embodiments, some regions of analysis are interrogated by the catheter 10′, for example, when the electrode array 12′ is repositioned relative to a different portion of the heart chamber (HC) and additional data is recorded and analyzed.

[0246] The above-described embodiments should be understood to serve as illustrative examples only, and further embodiments are contemplated. Any feature described herein with respect to any one embodiment may be used alone or in combination with other features described, and may also be used in combination with one or more features of any other embodiment, or in any combination of any other embodiment. Furthermore, equivalents and modifications not described above may also be used without departing from the scope of the present invention, as defined in the appended claims.

Claims

1. 1. A cardiac diagnostic system comprising:

1. A diagnostic catheter configured for insertion into the heart of a patient, comprising: a diagnostic catheter configured to record electrical activity data of the patient at a plurality of recording locations; at least one processing unit including an algorithm that correlates the electrical activity data with a location of the heart, the algorithm being operable to perform a complexity assessment using the electrical activity data to identify substrate-mediated complexity, and to generate a diagnostic result related to a cardiac condition based on the complexity assessment; Equipped with the recorded electrical activity data includes recorded voltage data; the at least one processing unit further comprising a second algorithm executable to calculate surface charge data and / or dipole density data on the cardiac wall of the heart based on the recorded voltage data; and The system wherein the complexity assessment is based on the surface charge data and / or the dipole density data.

2. The system of claim 1 , wherein the diagnostic results include an assessment of variation in complexity over time and space.

3. The system of claim 1 , wherein the complexity assessment comprises a macro-level complexity assessment.

4. 2. The system of claim 1, wherein the complexity assessment represents an assessment of a portion of a heart chamber, wherein the plurality of recording locations includes at least three recording locations within the heart chamber, and wherein the at least one processing unit is configured to calculate electrical activity data for at least three apexes on the heart wall, and wherein the calculation is based on electrical activity data recorded at the at least three recording locations.

5. The portion of the heart chamber is within 7 cm of the surface of the heart wall. ２ Below, 4cm ２ Less than or equal to 1 cm ２ 5. The system of claim 4, comprising:

6. 2. The system of claim 1, wherein the complexity assessment represents an assessment of a portion of a heart chamber, wherein the plurality of recording locations includes at least 24 recording locations within the heart chamber, and wherein the at least one processing unit is configured to calculate calculated electrical activity data for at least 64 apexes on the heart wall, and wherein the calculation is based on electrical activity data recorded at the at least 24 recording locations.

7. The system of claim 6 , wherein the at least 64 vertices comprises at least 500 vertices.

8. The portion of the heart chamber is at least 1 cm of the surface of the heart wall. ２ , at least 4 cm ２ , or at least 7 cm ２ The system of claim 6 , comprising:

9. 2. The system of claim 1, wherein the at least one processing unit is configured to calculate calculated electrical activity data for a plurality of vertices on the cardiac wall, the calculation being based on electrical activity data recorded at the at least three recording locations.

10. 10. The system of claim 9, wherein the second algorithm is executable to calculate the surface charge data and / or the dipole density data for each of the plurality of vertices based on the recorded voltage data.

11. 11. The system of claim 10, wherein the at least one processing unit further comprises a third algorithm, wherein the third algorithm is operable to convert the surface charge data and / or dipole density data into surface voltage data, and wherein the complexity assessment is based on the surface voltage data.

12. The system of claim 1 , wherein the complexity assessment is based on electrical activity data containing between 3 and 3000 activations.

13. 10. The system of claim 1, wherein the complexity assessment is based on electrical activity data recorded over a period of 0.3 seconds to 500 seconds.

14. The system of claim 1 , wherein the complexity assessment is based on electrical activity data recorded over a period of 5 minutes to 8 hours.

15. The system of claim 1 , wherein the diagnostic result comprises an assessment of complexity at a single heart wall location.

16. 16. The system of claim 15, further comprising a display, the system configured to generate the diagnosis associated with an image of the patient's anatomy on the display.

17. The system of claim 1 , wherein the diagnostic result comprises an assessment of complexity at multiple cardiac wall locations.

18. 20. The system of claim 17, further comprising a display, the system configured to generate the diagnosis associated with an image of the patient's anatomy on the display.

19. The system of claim 1 , wherein the diagnostic catheter includes at least one electrode.

20. The system of claim 1 , wherein the diagnostic catheter includes at least one ultrasound transducer.

21. The system of claim 1 , wherein the diagnostic catheter includes a plurality of splines, wherein each spline includes at least one electrode and at least one ultrasound transducer.

22. 10. The system of claim 1, wherein the cardiac condition comprises a condition selected from the group consisting of atrial fibrillation, atrial flutter, atrial tachycardia, atrial bradycardia, ventricular tachycardia, ventricular bradycardia, ectopic, congestive heart failure, angina pectoris, arterial stenosis, and combinations thereof.

23. 2. The system of claim 1, wherein the cardiac condition comprises a condition selected from the group consisting of heterogeneous activation, conduction, depolarization, and / or repolarization, irregularity patterns that vary in time, space, magnitude, and / or state, irregularity patterns that are focal, reentrant, rotational, circling, directional irregularity, velocity irregularity, functional block, permanent block, and combinations thereof.

24. The system comprises: and further comprising an ablation catheter for insertion into the heart of the patient, the ablation catheter configured to deliver ablation energy to at least one location in the heart wall. The system of claim 1 .

25. 25. The system of claim 24, wherein the algorithm is configured to determine at least one ablation location, wherein the at least one ablation location includes one or more cardiac wall locations for receiving the ablation energy from the ablation catheter, and wherein the at least one ablation location is determined based on the complexity assessment and / or the diagnosis.

Images