IP Library Granted Patent US 12,390,113
Granted Patent B2
US 12,390,113 · App. 16/042,973 · Granted Aug 19, 2025

User interface for presenting simulated anatomies of an electromagnetic source

Inventor: Christopher Villongco (Oakland, CA)
Assignee: VEKTOR MEDICAL, INC.
A61B5/02028A61B5/318A61B5/319A61B5/339A61B5/341A61B5/7275A61B5/743A61B5/7435A61B34/10G06V10/00G06V10/764G09B23/30A61B5/1075A61B5/25A61B5/316A61B5/349A61B5/361A61B5/363A61B5/364A61B5/6805A61B5/7235A61B5/725A61B5/7267A61B5/7425A61B5/7445A61B6/032A61B6/503A61B2034/105G06F18/22G06F30/20G06N3/045G06N3/08G06N5/04G06N5/046G06N20/00G06T3/4007G06T17/205G06T19/20G06T2210/41G06V2201/03G09B23/285G16H50/20G16H50/50
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,390,113
App. No.
16/042,973
Granted
Aug 19, 2025
Kind
B2
Abstract

Systems are provided for generating data representing electromagnetic states of a heart for medical, scientific, research, and/or engineering purposes. The systems generate the data based on source configurations such as dimensions of, and scar or fibrosis or pro-arrhythmic substrate location within, a heart and a computational model of the electromagnetic output of the heart. The systems may dynamically generate the source configurations to provide representative source configurations that may be found in a population. For each source configuration of the electromagnetic source, the systems run a simulation of the functioning of the heart to generate modeled electromagnetic output (e.g., an electromagnetic mesh for each simulation step with a voltage at each point of the electromagnetic mesh) for that source configuration. The systems may generate a cardiogram for each source configuration from the modeled electromagnetic output of that source configuration for use in predicting the source location of an arrhythmia.

Claims (44)

1. A method for treating an arrhythmia of a patient, the method comprising:

under control of one or more computing systems,

accessing seed anatomies of a body part that is a heart, each seed anatomy having a seed value for each of a plurality of anatomical parameters of the body part;

for each of a plurality of seed anatomies of the body part, displaying a seed representation of the body part based on seed values of anatomical parameters of that seed anatomy;

accessing a set of weights that includes a weight for each seed anatomy;

generating a three-dimensional (3D) mesh representing the body part based on a simulated value for each anatomical parameter by, for each anatomical parameter, combining the seed values of the seed anatomies of that anatomical parameter, factoring in the weights of the seed anatomies;

displaying a simulated representation of the body part, the simulated representation derived from the 3D mesh; and

for each of a plurality of electrophysiology parameter specifications that include an arrhythmia source location,

running a simulation based on the 3D mesh to generate modeled electromagnetic output of the body part based on that electrophysiology parameter specification; and

generating a cardiogram based on the modeled electromagnetic output; and

storing the cardiogram in association with the arrhythmia source location of that electrophysiology parameter specification;

receiving a patient cardiogram; and

identifying an arrhythmia source location associated with a generated cardiogram based on similarity between the patient cardiogram and the generated cardiogram; and

performing an ablation on the heart of the patient based on the identified arrhythmia source location.

2. The method of claim 1 wherein the seed representations are displayed in a circular arrangement with the simulated representation displayed within the circular arrangement.

3. The method of claim 2 further comprising, under control of the one or more computing systems, displaying, in association with each displayed seed representation for a seed anatomy, an indication of the weight associated with that seed anatomy.

4. The method of claim 3 further comprising, under control of the one or more computing systems, displaying a line between each displayed seed representation and the displayed simulated representation, wherein the displayed indication of the weight for a seed anatomy is displayed in association with the displayed line between the displayed seed representation for that seed anatomy and the displayed simulated representation.

5. The method of claim 1 further comprising, under control of the one or more computing systems, providing a user interface element for specifying the weight for each seed anatomy.

6. The method of claim 1 further comprising, under control of the one or more computing systems, providing a user interface element for specifying a plurality of sets of weights, with each set including a weight for each seed anatomy.

7. The method of claim 6 wherein the plurality of sets of weights are specified by providing a range of weights and an increment.

8. A method for treating an arrhythmia of a patient, the method comprising:

under control of one or more computing systems,

for each of a plurality of seed anatomies of a heart, displaying a seed representation of the heart based on seed values of anatomical parameters of that seed anatomy; and

displaying a simulated representation of the heart based on a simulated anatomy derived, for each anatomical parameter, from a simulated value for that anatomical parameter generated from a weighted combination of the seed values of the seed anatomies for that anatomical parameter; and

for each of a plurality of electrophysiology parameter specifications that include a source location of a heart disorder,

running a simulation of electrical activity of the heart over time to generate simulated electromagnetic output of the heart having the simulated anatomy and having that electrophysiology parameter specification;

generating derived electromagnetic data from the simulated electromagnetic output; and

storing a mapping of the derived electromagnetic data to that source location;

receiving patient electromagnetic data; and

identifying a source location that is mapped to derived electromagnetic data that is similar to the patient electromagnetic data; and

performing an ablation on the heart of the patient based on the identified source location.

9. The method of claim 8 wherein the seed representations are displayed in a circular arrangement with the simulated representation displayed within the circular arrangement.

10. The method of claim 8 further comprising, under control of the one or more computing systems, displaying, in association with each displayed seed representation for a seed anatomy, an indication of a weight associated with that seed anatomy.

11. A method for treating an arrhythmia of a patient, the method comprising:

under control of one or more computing systems;

generating a three-dimensional (3D) mesh representing a heart based on a value for each of a plurality of anatomical parameters of the heart;

for each of a plurality of electrophysiology parameter specifications that include an arrhythmia source location,

running a simulation based on the 3D mesh to generate modeled electromagnetic output of a heart based on that electrophysiology parameter specification; and

generating a simulated cardiogram based on the modeled electromagnetic output; and

training a neural network using each simulated cardiogram labeled with an arrhythmia source location as training data;

receiving a patient cardiogram collected from the patient;

applying the trained neural network to the patient cardiogram to identify an arrhythmia source location; and

outputting an indication of the identified arrhythmia source location; and

performing an ablation on the heart of the patient based on the identified arrhythmia source location.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 27, 2025
From: VEKTOR MEDICAL, INC.
To: THE VEKTOR GROUP, INC.
Reel/Frame 073265/0864 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 15, 2019
From: VILLONGCO, CHRISTOPHER
To: VEKTOR MEDICAL, INC.
Reel/Frame 048885/0974 →
Continuity (2)
Provisional Application 62663049 · Apr 26, 2018
Related Publication 20190333640A1 · Oct 31, 2019
References Cited (268)
US 4352163A · Schultz et al. · 1982 [cited by applicant]
US 5458116A · Egler · 1995 [cited by applicant]
US 5596634A · Fernandez et al. · 1997 [cited by applicant]
US 5601084A · Sheehan · 1997 [cited by applicant]
US 5803084A · Olson · 1998 [cited by applicant]
US 5891132A · Hohla · 1999 [cited by applicant]
US 6269336B1 · Ladd et al. · 2001 [cited by applicant]
US 6292783B1 · Rohler et al. · 2001 [cited by applicant]
US 6324513B1 · Nagai et al. · 2001 [cited by applicant]
US 6370412B1 · Armoundas et al. · 2002 [cited by applicant]
US 6567805B1 · Johnson et al. · 2003 [cited by applicant]
US 6895084B1 · Saylor et al. · 2005 [cited by applicant]
US 6931273B2 · Groenewegen et al. · 2005 [cited by applicant]
US 7010347B2 · Schecter · 2006 [cited by applicant]
US 7286866B2 · Okerlund et al. · 2007 [cited by applicant]
US 7424137B2 · Badilini et al. · 2008 [cited by applicant]
US 8224640B2 · Sharma et al. · 2012 [cited by applicant]
US 8521266B2 · Narayan · 2013 [cited by applicant]
US 8838203B2 · Van Dam et al. · 2014 [cited by applicant]
US 8849389B2 · Anderson et al. · 2014 [cited by applicant]
US 9014795B1 · Yang · 2015 [cited by applicant]
US 9129053B2 · Mansi et al. · 2015 [cited by applicant]
US 9277970B2 · Mansi et al. · 2016 [cited by applicant]
US 9320126B2 · Valcore, Jr. · 2016 [cited by applicant]
US 9706935B2 · Spector · 2017 [cited by applicant]
US 9842725B2 · Valcore, Jr. · 2017 [cited by applicant]
US 9948642B2 · Wang · 2018 [cited by applicant]
US 10039454B2 · Sapp, Jr. et al. · 2018 [cited by applicant]
US 10311978B2 · Mansi et al. · 2019 [cited by applicant]
US 10319144B2 · Krummen et al. · 2019 [cited by applicant]
US 10342620B2 · Kiraly et al. · 2019 [cited by applicant]
US 10363100B2 · Trayanova et al. · 2019 [cited by applicant]
US 10402966B2 · Blake, III · 2019 [cited by applicant]
US 10483005B2 · Seegerer · 2019 [cited by examiner]
US 10556113B2 · Villongco · 2020 [cited by applicant]
US 10617314B1 · Villongco · 2020 [cited by applicant]
US 10679348B2 · Blake, III · 2020 [cited by applicant]
US 10713790B2 · Adler · 2020 [cited by applicant]
US 10860754B2 · Villongco · 2020 [cited by applicant]
US 10861246B2 · Voth · 2020 [cited by applicant]
US 10912476B2 · Spector · 2021 [cited by applicant]
US 10925511B2 · Blake et al. · 2021 [cited by applicant]
US 10952794B2 · Villongco · 2021 [cited by applicant]
US 10998101B1 · Tran · 2021 [cited by applicant]
US 11013471B2 · Villongco · 2021 [cited by applicant]
US 11065060B2 · Villongco · 2021 [cited by applicant]
US 11259756B2 · Villongco · 2022 [cited by applicant]
US 11259871B2 · Villongco · 2022 [cited by applicant]
US 11338131B1 · Krummen · 2022 [cited by applicant]
US 11547369B2 · Villongco · 2023 [cited by applicant]
US 20010049688A1 · Fratkina et al. · 2001 [cited by applicant]
US 20020010679A1 · Felsher · 2002 [cited by applicant]
US 20020154153A1 · Messinger · 2002 [cited by applicant]
US 20020188599A1 · Mcgreevy · 2002 [cited by applicant]
US 20030182124A1 · Khan · 2003 [cited by applicant]
US 20030195400A1 · Glukhovsky · 2003 [cited by applicant]
US 20040059237A1 · Narayan · 2004 [cited by applicant]
US 20040083092A1 · Valles · 2004 [cited by applicant]
US 20040153128A1 · Suresh et al. · 2004 [cited by applicant]
US 20040176697A1 · Kappenberger · 2004 [cited by applicant]
US 20070031019A1 · Lesage · 2007 [cited by applicant]
US 20070032733A1 · Burton · 2007 [cited by applicant]
US 20070060829A1 · Pappone · 2007 [cited by applicant]
US 20070219452A1 · Cohen et al. · 2007 [cited by applicant]
US 20080077032A1 · Holmes et al. · 2008 [cited by applicant]
US 20080140143A1 · Ettori · 2008 [cited by applicant]
US 20080177192A1 · Chen · 2008 [cited by applicant]
US 20080205722A1 · Schaefer · 2008 [cited by applicant]
US 20080234576A1 · Gavit-Houdant et al. · 2008 [cited by applicant]
US 20080288493A1 · Yang et al. · 2008 [cited by applicant]
US 20090088816A1 · Harel et al. · 2009 [cited by applicant]
US 20090099468A1 · Thiagalingam · 2009 [cited by applicant]
US 20090275850A1 · Mehendale · 2009 [cited by applicant]
US 20100266170A1 · Khamene · 2010 [cited by applicant]
US 20110028848A1 · Shaquer · 2011 [cited by applicant]
US 20110118590A1 · Zhang · 2011 [cited by applicant]
US 20110251505A1 · Narayan · 2011 [cited by applicant]
US 20110307231A1 · Kirchner · 2011 [cited by applicant]
US 20120173576A1 · Gilliam et al. · 2012 [cited by applicant]
US 20130006131A1 · Narayan · 2013 [cited by applicant]
US 20130035576A1 · O'Grady et al. · 2013 [cited by applicant]
US 20130096394A1 · Gupta · 2013 [cited by applicant]
US 20130131529A1 · Jia · 2013 [cited by applicant]
US 20130131629A1 · Grattoni et al. · 2013 [cited by applicant]
US 20130150742A1 · Briggs · 2013 [cited by applicant]
US 20130197881A1 · Mansi et al. · 2013 [cited by applicant]
US 20130268284A1 · Heck · 2013 [cited by applicant]
US 20130304445A1 · Iwamura et al. · 2013 [cited by applicant]
US 20140005562A1 · Bunch · 2014 [cited by applicant]
US 20140088943A1 · Trayanova · 2014 [cited by applicant]
US 20140107511A1 · Banet · 2014 [cited by applicant]
US 20140122048A1 · Vadakkumadan et al. · 2014 [cited by applicant]
US 20140200575A1 · Spector · 2014 [cited by applicant]
US 20140276152A1 · Narayan · 2014 [cited by applicant]
US 20150005652A1 · Banet et al. · 2015 [cited by applicant]
US 20150057522A1 · Nguyen · 2015 [cited by applicant]
US 20150216432A1 · Yang · 2015 [cited by applicant]
US 20150216434A1 · Ghosh · 2015 [cited by applicant]
US 20150216438A1 · Bokan et al. · 2015 [cited by applicant]
US 20150294082A1 · Passerini et al. · 2015 [cited by applicant]
US 20160008635A1 · Burdette · 2016 [cited by applicant]
US 20160038743A1 · Foster et al. · 2016 [cited by applicant]
US 20160113725A1 · Trayanova et al. · 2016 [cited by applicant]
US 20160117816A1 · Taylor · 2016 [cited by applicant]
US 20160135702A1 · Perez · 2016 [cited by applicant]
US 20160135706A1 · Sullivan · 2016 [cited by applicant]
US 20160143553A1 · Chien · 2016 [cited by applicant]
US 20160192868A1 · Levant et al. · 2016 [cited by applicant]
US 20160331337A1 · Ben-Haim · 2016 [cited by applicant]
US 20170027465A1 · Blauer · 2017 [cited by applicant]
US 20170027649A1 · Kiraly · 2017 [cited by applicant]
US 20170061617A1 · Cochet · 2017 [cited by applicant]
US 20170065195A1 · Nguyen · 2017 [cited by applicant]
US 20170068796A1 · Passerini et al. · 2017 [cited by applicant]
US 20170079542A1 · Spector · 2017 [cited by applicant]
US 20170112401A1 · Rapin · 2017 [cited by applicant]
US 20170150928A1 · del Alamo de Pedro · 2017 [cited by applicant]
US 20170156612A1 · Relan · 2017 [cited by applicant]
US 20170161439A1 · Raduchel et al. · 2017 [cited by applicant]
US 20170161896A1 · Blake, III · 2017 [cited by applicant]
US 20170178403A1 · Krummen · 2017 [cited by applicant]
US 20170185740A1 · Seegerer · 2017 [cited by applicant]
US 20170202421A1 · Hwang et al. · 2017 [cited by applicant]
US 20170202521A1 · Urman et al. · 2017 [cited by applicant]
US 20170209698A1 · Mllongco · 2017 [cited by applicant]
US 20170231505A1 · Mahajan · 2017 [cited by applicant]
US 20170273588A1 · He · 2017 [cited by applicant]
US 20170319089A1 · Lou · 2017 [cited by applicant]
US 20170319278A1 · Trayanova · 2017 [cited by applicant]
US 20170330075A1 · Tuysuzoglu · 2017 [cited by applicant]
US 20170367603A1 · Spector · 2017 [cited by applicant]
US 20180012363A1 · Seiler · 2018 [cited by applicant]
US 20180020916A1 · Ruppersberg · 2018 [cited by applicant]
US 20180032689A1 · Kiranyaz · 2018 [cited by applicant]
US 20180260706A1 · Galloway et al. · 2018 [cited by applicant]
US 20180279896A1 · Ruppersberg · 2018 [cited by applicant]
US 20180317800A1 · Coleman et al. · 2018 [cited by applicant]
US 20180318606A1 · Robinson · 2018 [cited by applicant]
US 20180326203A1 · Hastings et al. · 2018 [cited by applicant]
US 20180333104A1 · Sitek · 2018 [cited by applicant]
US 20190038363A1 · Adler · 2019 [cited by applicant]
US 20190051419A1 · Seegerer et al. · 2019 [cited by applicant]
US 20190060006A1 · Van Dam · 2019 [cited by applicant]
US 20190069795A1 · Kiranya · 2019 [cited by applicant]
US 20190104951A1 · Valys · 2019 [cited by applicant]
US 20190104958A1 · Rappel · 2019 [cited by applicant]
US 20190125186A1 · Ruppersberg · 2019 [cited by applicant]
US 20190216350A1 · Sullivan et al. · 2019 [cited by applicant]
US 20190223946A1 · Coates · 2019 [cited by applicant]
US 20190304183A1 · Krummen · 2019 [cited by applicant]
US 20190328254A1 · Villongco · 2019 [cited by applicant]
US 20190328257A1 · Villongco · 2019 [cited by applicant]
US 20190328457A1 · Villongco et al. · 2019 [cited by applicant]
US 20190328458A1 · Shmayahu · 2019 [cited by applicant]
US 20190332729A1 · Villongco · 2019 [cited by applicant]
US 20190333639A1 · Villongco · 2019 [cited by applicant]
US 20190333641A1 · Villongco · 2019 [cited by applicant]
US 20190333643A1 · Villongco · 2019 [cited by applicant]
US 20200107781A1 · Navalgund et al. · 2020 [cited by applicant]
US 20200245935A1 · Krummen et al. · 2020 [cited by applicant]
US 20200289025A1 · Dichterman · 2020 [cited by examiner]
US 20200324118A1 · Garner et al. · 2020 [cited by applicant]
US 20200405148A1 · Tran · 2020 [cited by applicant]
US 20210012868A1 · Wolf et al. · 2021 [cited by applicant]
US 20210015390A1 · Zhou et al. · 2021 [cited by applicant]
US 20210076961A1 · Villongco · 2021 [cited by applicant]
US 20210137384A1 · Robinson et al. · 2021 [cited by applicant]
US 20210205025A1 · Erkamp et al. · 2021 [cited by applicant]
US 20210219923A1 · Eun Young Yang · 2021 [cited by applicant]
US 20210259560A1 · Venkatraman · 2021 [cited by applicant]
US 20220047237A1 · Liu · 2022 [cited by applicant]
CN 103829941A · 2014 [cited by applicant]
CN 104680548A · 2015 [cited by applicant]
CN 105194799A · 2015 [cited by applicant]
CN 105263405A · 2016 [cited by applicant]
CN 106725428 · 2017 [cited by applicant]
CN 107025618A · 2017 [cited by applicant]
CN 107951485A · 2018 [cited by applicant]
CN 113851225A · 2021 [cited by applicant]
CN 114206218A · 2022 [cited by applicant]
JP H4174643A · 1992 [cited by applicant]
JP H08289877A · 1996 [cited by applicant]
JP 2007517633A · 2007 [cited by applicant]
JP 2012508079A · 2012 [cited by applicant]
JP 2014530074A · 2014 [cited by applicant]
JP 2015163199A · 2015 [cited by applicant]
JP 2017140381A · 2017 [cited by applicant]
JP 2018036996A · 2018 [cited by applicant]
WO 2006066324A1 · 2006 [cited by applicant]
WO 2014097063A1 · 2014 [cited by applicant]
WO 2015031576A1 · 2015 [cited by applicant]
WO 2015104672A2 · 2015 [cited by applicant]
WO 2015153832A1 · 2015 [cited by applicant]
WO 2015196140A1 · 2015 [cited by applicant]
WO 2018190715A1 · 2018 [cited by applicant]
WO 2019118640A1 · 2019 [cited by applicant]
WO 2020101864 · 2020 [cited by applicant]
WO 2020142539 · 2020 [cited by applicant]
Burch GE. The history of vectorcardiography. Med Hist Suppl. 1985;(5):103-31. (Year: 1985). [cited by examiner]
Günther, Frauke, and Stefan Fritsch. “Neuralnet: training of neural networks.” R J. 2.1 (2010): 30. (Year: 2010). [cited by examiner]
USPTO, Office Action mailed Feb. 9, 2023, U.S. Appl. No. 17/590,119. [cited by applicant]
Hill et al., Investigating a Novel Activation-Repolarisation Time Metric to Predict Localised Vulnerability to Reentry Using Computational Modelling. Journal: PLoS ONE. Mar. 2, 2016. [Retrieved]: Nov. 30, 2022. Retrieve… [cited by applicant]
Cobb, Leonard A., et al. “Changing incidence of out-of-hospital ventricular fibrillation, 1980-2000.” Jama 288.23 (2002): 3008-3013. [cited by applicant]
Gonzales, Matthew J., et al. “Structural contributions to fibrillatory rotors in a patient-derived computational model of the atria.” EP Europace 16.suppl 4 (2014): iv3-iv10. [cited by applicant]
Krishnamurthy, Adarsh, et al. “CRT Response is Greater in Patients With Larger Fraction of the Myocardium Performing Negative Regional Work.” Circulation 128.Suppl 22 (2013): A11135-A11135, Abstract only. [cited by applicant]
Krishnamurthy, Adarsh, et al. “Patient-specific models of cardiac biomechanics.” Journal of computational physics 244 (2013): 4-21. [cited by applicant]
Krummen, David E., et al. “Rotor stability separates sustained ventricular fibrillation from selfterminating episodes in humans.” Journal of the American College of Cardiology 63.24 (2014): 2712-2721. [cited by applicant]
Nash, Martyn P., et al. “Evidence for multiple mechanisms in human ventricular fibrillation.” Circulation 114.6 (2006): 536-542. [cited by applicant]
Ten Tusscher, K. H. W. J., et al. “A model for human ventricular tissue.” American Journal of PhysioloQy-Heart and Circulatory PhysioloQy 286.4 (2004): H1573-H1589. [cited by applicant]
Villongco, Christopher T., et al. “Patient-specific modeling of ventricular activation pattern using surface ECG-derived vectorcardiogram in bundle branch block.” Progress in biophysics and molecular bioloQy 115.2 (2014… [cited by applicant]
Vadakkumpadan, Fijoy, et al. “Image-based estimation of ventricular fiber orientations for personalized modeling of cardiac electrophysiology.”  IEEE-TMI 31.5 (2012): 1051-1060. [cited by applicant]
Taggart, Peter, et al. “Developing a novel comprehensive framework for the investigation of cellular and whole heart electrophysiology in the in situ human heart: Historical perspectives, current progress and future pro… [cited by applicant]
Tobon, Catalina, et al. “Dominant frequency and organization index maps in a realistic three-dimensional computational model of atrial fibrillation.”  Europace 14.suppl_5 (2012): v25-v32. [cited by applicant]
Kors, J.A., et al., “Recontruction of the Frank vectorcardiogram from standard electrocardiographic leads: diagnostic comparison of different methods,” European Heart Journal, vol. 11, Issue 12, Dec. 1, 1990, pp. 1083-1… [cited by applicant]
Tšić, Ivan et al., “Electrocardiographic Systems With Reduced Numbers of Leads—Synthesis of the 12-Lead ECG,” IEEE Reviews in Biomedical Engineering, vol. 7, 2014, pp. 126-142. [cited by applicant]
Frank, Ernest, “An Accurate, Clinically Practical System for Spatial Vectorcardiography,” American Heart Association, Inc., downloaded from http://circ.ahajournals.org/ at Cons California Dig Lib on Mar. 12, 2014. [cited by applicant]
Vozda, M et al.., “Methods for derivation of orthogonal leads from 12-lead electrocardiogram: a review,” Elsevier, Biomedical Signal Processing and Control 19 (2015), 23-34. [cited by applicant]
Si, Hang, “TetGen, a Delaunay-Based Quality Tetrahedral Mesh Generator,” ACM' Transactions on Mathematical Software, vol. 41, No. 2, Article 11, Jan. 2015, 36 pages. [cited by applicant]
Tajbakhsh, Nima et al., “Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?” IEEE Transactions on Medical Imaging (2016) vol. 35, e-pp. 1-17). [cited by applicant]
International Search Report and Written Opinion issued for PCT/US2019/029181 mailed Sep. 16, 2019. [cited by applicant]
International Search Report and Written Opinion issued for PCT/US2019/029184 mailed Sep. 24, 2019. [cited by applicant]
Carrault, Guy, et al. “A model-based approach for learning to identify cardiac arrhythmias,” Joint European Conference on Artificial Intelligence in Medicine and Medicine Decision Making. Springer, Berlin Heidelberg, 19… [cited by applicant]
Carrualt, Guy, et al. “Temporal abstraction and inductive logic programming for arrhythmia recognition from electrocardiograms.” Artificial intelligence in medicine 28.3 (2003): 231-263. [cited by applicant]
Hren, Rok, et al. “Value of simulated body surface potential maps as templates in localizing sites of ectopic activation for radiofrequency ablation” Physiol. Meas. 18 (1997) 373-400. Mar. 7, 1997. [cited by applicant]
International Search Report and Written Opinion issued for PCT/US16/68449 mailed on Mar. 29, 2017. [cited by applicant]
International Search Report and Written Opinion issued for PCT/US2019/069136 mailed May 11, 2020, 13 pages. [cited by applicant]
Thakor and Tong (Annual Reviews in Biomedicine and Engineering (2004) vol. 6, 453-495). [cited by applicant]
International Search Report and Written Opinion issued in PCT/US2020/036754 mailed Oct. 15, 2020, 13 pages. [cited by applicant]
Siregar, P. “An Interactive Qualitative Model in Cardiology” Computers and Biomedical Research 28, pp. 443-478, May 16, 1994. [cited by applicant]
Cuculich, Phillip S. et al., “Noninvasive Cardiac Radiation for Ablation of Ventricular Tachycardia” New England Journal of Medicine, 377; 24, pp. 2325-2336, Dec. 14, 2017. [cited by applicant]
Graham, Adam J. et al., “Evaluation of ECG Imaging to Map Haemodynamically Stable and Unstable Ventricular Arrhythmias” downloaded from http://ahajournals.org on Jan. 22, 2020. [cited by applicant]
Sapp, John L. et al., “Real-Time Localization of Ventricular Tachycardia Origin From the 12-Lead Electrocardiogram” JACC: Clinical Electrophysiology by the Amercian College of Cardiology Foundation, vol. 3, 2017, pp. 68… [cited by applicant]
Potse, Mark et al., “Continuos Localization of Cardian Activation Sites Using a Database of Multichannel ECG Recordings” IEEE Transactions of Biomedical Engineering, vol. 47, No. 5, May 2000, pp. 682-689. [cited by applicant]
Zhou, Shijie et al. “Rapid 12-lead automoated localization method: Comparison to electrocardiogramaging (ECGI) in patient-specific geometry”, Journal of Electrocardiology, vol. 51, 2018, pp. S92-S97. [cited by applicant]
Zhou, Shijie et al. “Localization of ventricular activation origin using patient-specific geometry: Preliminary results” J. Carciovasc Electrophysiol, 2018; 29: pp. 979-986. [cited by applicant]
International Search Report and Written Opinion issued for PCT/US2019/058217 mailed Feb. 7, 2020, 9 pages. [cited by applicant]
Andreu et al., “Integration of 3D Electroanatomic Maps and Magnetic Resonance Scar Characterization Into the Navigation System to Guide Ventricular Tachycardia Ablation”, Circ Arrhythm Electrophysiol, Oct. 2011, 4(5), p… [cited by applicant]
Acharya et al., A deep convolutional neural network model to classify heartbeats, Computers in Biology and Medicine (Oct. 1, 2017) vol. 89, pp. 389-396. [cited by applicant]
Acharya et al., Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals, Computers in Biology and Medicine (Sep. 1, 2018, Epub Sep. 27, 2017) 100:270-278. [cited by applicant]
Acharya UR, Fujita H, Lih OS, Hagiwara Y, Tan JH, Adam M. Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Information sciences. Sep. 1, 2017 ;4… [cited by applicant]
Bond et al., XML-BSPM: An XML Format for Storing Body Surface Potential Map Recordings, BMC Medical Informatics and Decision Making 2010, 10:28, http://www.biomedcentra.com/1472-6947/10/28, 27 pages. [cited by applicant]
Dandu Ravi Varma, “Managing DICOM images: Tips and tricks for the radiologist”, Indian J Radiol Imaging., Jan.-Mar. 2012; 22(1), pp. 4-13. [cited by applicant]
Extended European Search Report issued in European Patent Application No. 19215701.4 and dated Apr. 17, 2020, 9 pages. [cited by applicant]
Extended European Search Report issued in European Patent Application No. 19792821.1 and dated Mar. 15, 2021. 10 pages. [cited by applicant]
Garg, et al., ECG Paper Records Digitization Through Image Processing Techniques, vol. 48, No. 13, Jun. 2012, 4 pages. [cited by applicant]
Goodfellow, Ian et al., Generative Adversarial Nets, Advances in Neural Information Processing Systems, pp. 2672-2680, 2014. [cited by applicant]
Halevy, A., Norvig, P., & Pereira, F. (2009). The unreasonable effectiveness of data. IEEE intelligent systems, 24(2), 8-12. [cited by applicant]
He, Zhenliang et al., Facial Attribute Editing by Only Changing What You Want, IEEE Transactions on Image Processing, 2018. [cited by applicant]
Hershey S, Chaudhuri S, Ellis DP, Gemmeke JF, Jansen A, Moore RC, Plakal M, Platt D, Saurous RA, Seybold B, Slaney M. CNN architectures for large-scale audio classification. In2017 ieee international conference on acous… [cited by applicant]
International Searching Authority, International Search Report and Written Opinion, PCT Patent Application PCT/US2021/057616, mailed Jan. 28, 2022, 9 pages. [cited by applicant]
Jacquemet, V., “Lessons from Computer Simulation of Ablation of Atrial Fibrillation”, J Physiol. 594(9): 2417-2430, May 1, 2016. [cited by applicant]
Kiranyaz et al., Real-time patient-specific EDG classification by 1-D convolutional neural networks, IEEE Transactions on Biomedical Engineering (Mar. 2016) 63:664-675. [cited by applicant]
Kiranyaz S, Ince T, Hamila R, Gabbouj M. Convolutional neural networks for patient- specific ECG classification. In2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)… [cited by applicant]
Lee H, Kwon H. Going deeper with contextual CNN for hyperspectral image classification. IEEE Transactions on Image Processing, Jul. 1, 20171;26(10):4843-55. [cited by applicant]
Li D, Zhang J, Zhang Q, Wei X. Classification of ECG signals based on 1 D convolution neural network. In2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom) Oct. 12, 2017… [cited by applicant]
Light, D., E., et al. “Two Dimensional Arrays for Real Time Volumetric and Intracardiac Imaging with Simultaneous Electrocardiagram”, 1196-2000 IEEE Ultrasonics Symposium, retrieved on Sep. 24, 2021. [cited by applicant]
Lyon, et al. J.R. Soc Interface vol. 15:1-18. (2017). [cited by applicant]
Rahhal et al., Convolutional neural networks for electrocardiogram classification, Journal of Medical and Biological Engineering (Mar. 30, 2018) 38: 1014-1025. [cited by applicant]
Ravichandran et al., Novel Tool for Complete Digitization of Paper Electrocardiography Data, vol. 1, 2013, 7 pages. [cited by applicant]
Sapp et al., “Real-Time Localization of Ventricular Tachycardia Origin From the 12-Lead Electrocardiogram”, JACC Clinical Electrophysiology, vol. 3, No. 7, Jul. 2017. [cited by applicant]
Sharma, et al., Digitalization of ECG Records, International Journal of Engineering Research and Applications, www.ijera.com, ISSN: 2248-9622, vol. 10, Issue 6, (Series-VIII) Jun. 2020, pp. 01-05. [cited by applicant]
Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE transa… [cited by applicant]
Therrien R, Doyle S. Role of training data variability on classifier performance and generalizability. In Medical Imaging 2018: Digital Pathology Mar. 6, 2018 (vol. 10581, p. 1058109). International Society for Optics a… [cited by applicant]
Xiong et al. Computing and Cardiology vol. 44: pp. 1-4. (2017). [cited by applicant]
Zentara, et al., ECG Signal Coding Methods in Digital Systems, Communication Papers of the Federated Conference on Computer Science and Information Systems, DOI: 10.15439/2018F108, ISSN 2300-5963 ACSIS, vol. 17, pp. 95-… [cited by applicant]
Zhang, C., et al. “Patient-Specific ECG Classification Based on Recurrent Neural Networks and Clustering Technique,” Proceedings of the IASTED International Conference in Biomedical Engineering (Bio Med 2017); Feb. 20-2… [cited by applicant]
Zhu X, Vondrick C, Fowlkes CC, Ramanan D. Do we need more training data?. International Journal of Computer Vision.Aug. 2016;119(1):76-92. [cited by applicant]
Zubair M, Kim J, Yoon C. An automated ECG beat classification system using convolutional neural networks. In2016 6th international conference on IT convergence and security (ICITCS) Sep. 26, 2016 (pp. 1-5). IEEE. [cited by applicant]