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Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death

Circulation research, 2021-01, Vol.128 (2), p.172-184

2021

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Machine Learning-Enabled Multimodal Fusion of Intra-Atrial and Body Surface Signals in Prediction of Atrial Fibrillation Ablation Outcomes

Circulation. Arrhythmia and electrophysiology, 2022-08, Vol.15 (8), p.e010850-e010850

2022

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Three dimensional reconstruction to visualize atrial fibrillation activation patterns on curved atrial geometry

PloS one, 2021-04, Vol.16 (4), p.e0249873-e0249873

2021

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Atrial fibrillation signatures on intracardiac electrograms identified by deep learning

Computers in biology and medicine, 2022-06, Vol.145, p.105451-105451, Article 105451

2022

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Online webinar training to analyse complex atrial fibrillation maps: A randomized trial

PloS one, 2019-07, Vol.14 (7), p.e0217988-e0217988

2019

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Machine Learning to Classify Intracardiac Electrical Patterns During Atrial Fibrillation: Machine Learning of Atrial Fibrillation

Circulation. Arrhythmia and electrophysiology, 2020-08, Vol.13 (8), p.e008160-e008160

2020

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Characterizing Electrogram Signal Fidelity and the Effects of Signal Contamination on Mapping Human Persistent Atrial Fibrillation

Frontiers in physiology, 2018-09, Vol.9, p.1232-1232

2018

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Atrial fibrillation ablation outcome prediction with a machine learning fusion framework incorporating cardiac computed tomography

Journal of cardiovascular electrophysiology, 2023-05, Vol.34 (5), p.1164-1174

2023

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Wavefront Field Mapping Reveals a Physiologic Network Between Drivers Where Ablation Terminates Atrial Fibrillation

Circulation. Arrhythmia and electrophysiology, 2019-08, Vol.12 (8), p.e006835-e006835

2019

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Identification and Characterization of Sites Where Persistent Atrial Fibrillation Is Terminated by Localized Ablation

Circulation. Arrhythmia and electrophysiology, 2018-01, Vol.11 (1), p.e005258-e005258

2018

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Termination of persistent atrial fibrillation by ablating sites that control large atrial areas

Europace (London, England), 2020-06, Vol.22 (6), p.897-905

2020

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Interaction of Localized Drivers and Disorganized Activation in Persistent Atrial Fibrillation: Reconciling Putative Mechanisms Using Multiple Mapping Techniques

Circulation. Arrhythmia and electrophysiology, 2018-06, Vol.11 (6), p.e005846-e005846

2018

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Non-invasive Spatial Mapping of Frequencies in Atrial Fibrillation: Correlation With Contact Mapping

Frontiers in physiology, 2021-01, Vol.11, p.611266-611266

2021

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Spatial relationship of organized rotational and focal sources in human atrial fibrillation to autonomic ganglionated plexi

International journal of cardiology, 2017-08, Vol.240, p.234-239

2017

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Machine Learning to Classify Intracardiac Electrical Patterns During Atrial Fibrillation

Circulation. Arrhythmia and electrophysiology, 2020-08, Vol.13 (8), p.e008160-e008160

2020

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VALIDATING NON-INVASIVE INDICES OF AF COMPLEXITY AGAINST INTRACARDIAC MEASUREMENTS

Journal of the American College of Cardiology, 2021-05, Vol.77 (18), p.1354-1354

2021

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Abstract 14443: Deep Learning to Identify Atrial Fibrillation Phenotypes in Individual Patientsbased on Rate, Variability, Electrogram Shape

Circulation (New York, N.Y.), 2021-11, Vol.144 (Suppl_1)

2021

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CLASSIFICATION OF INDIVIDUAL ATRIAL INTRACARDIAC ELECTROGRAMS BY DEEP LEARNING

Journal of the American College of Cardiology, 2021-05, Vol.77 (18), p.3217-3217

2021

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MACHINE LEARNING CLASSIFIES INTRACARDIAC ELECTROGRAMS OF ATRIAL FIBRILLATION FROM OTHER ARRHYTHMIAS

Journal of the American College of Cardiology, 2021-05, Vol.77 (18), p.279-279

2021

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PROBING MACHINE LEARNING TO SEPARATE ATRIAL FIBRILLATION FROM OTHER ARRHYTHMIAS

Journal of the American College of Cardiology, 2021-05, Vol.77 (18), p.3410-3410

2021

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PO-685-04 DIFFERING DISTRIBUTION OF PREMATURE ATRIAL COMPLEXES THAT DO AND DO NOT PRECIPITATE SPONTANEOUS ATRIAL FIBRILLATION BY NON-INVASIVE IMAGING

Heart rhythm, 2022-05, Vol.19 (5), p.S377-S377

2022

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Abstract 14742: Deep Learning of Intracardiac Electrograms in Atrial Arrhythmia

Circulation (New York, N.Y.), 2020-11, Vol.142 (Suppl_3 Suppl 3), p.A14742-A14742

2020

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Abstract 14738: Classification of Atrial Fibrillation by Deep Learning of Electrogram Shapes versus Rate and Regularity

Circulation (New York, N.Y.), 2020-11, Vol.142 (Suppl_3 Suppl 3), p.A14738-A14738

2020

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B-PO02-044 ELECTROGRAM FINGERPRINTS OF ATRIAL FIBRILLATION

Heart rhythm, 2021-08, Vol.18 (8), p.S113-S114

2021

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B-PO04-062 COMPARING THE ACCURACY OF FEATURES TO IDENTIFY ATRIAL FIBRILLATION ON INTRACARDIAC ELECTROGRAMS

Heart rhythm, 2021-08, Vol.18 (8), p.S303-S304

2021

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Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death

Machine Learning-Enabled Multimodal Fusion of Intra-Atrial and Body Surface Signals in Prediction of Atrial Fibrillation Ablation Outcomes

Three dimensional reconstruction to visualize atrial fibrillation activation patterns on curved atrial geometry

Atrial fibrillation signatures on intracardiac electrograms identified by deep learning

Online webinar training to analyse complex atrial fibrillation maps: A randomized trial

Machine Learning to Classify Intracardiac Electrical Patterns During Atrial Fibrillation: Machine Learning of Atrial Fibrillation

Characterizing Electrogram Signal Fidelity and the Effects of Signal Contamination on Mapping Human Persistent Atrial Fibrillation

Atrial fibrillation ablation outcome prediction with a machine learning fusion framework incorporating cardiac computed tomography

Wavefront Field Mapping Reveals a Physiologic Network Between Drivers Where Ablation Terminates Atrial Fibrillation

Identification and Characterization of Sites Where Persistent Atrial Fibrillation Is Terminated by Localized Ablation

Termination of persistent atrial fibrillation by ablating sites that control large atrial areas

Interaction of Localized Drivers and Disorganized Activation in Persistent Atrial Fibrillation: Reconciling Putative Mechanisms Using Multiple Mapping Techniques

Non-invasive Spatial Mapping of Frequencies in Atrial Fibrillation: Correlation With Contact Mapping

Spatial relationship of organized rotational and focal sources in human atrial fibrillation to autonomic ganglionated plexi

Machine Learning to Classify Intracardiac Electrical Patterns During Atrial Fibrillation

VALIDATING NON-INVASIVE INDICES OF AF COMPLEXITY AGAINST INTRACARDIAC MEASUREMENTS

Abstract 14443: Deep Learning to Identify Atrial Fibrillation Phenotypes in Individual Patientsbased on Rate, Variability, Electrogram Shape

CLASSIFICATION OF INDIVIDUAL ATRIAL INTRACARDIAC ELECTROGRAMS BY DEEP LEARNING

MACHINE LEARNING CLASSIFIES INTRACARDIAC ELECTROGRAMS OF ATRIAL FIBRILLATION FROM OTHER ARRHYTHMIAS

PROBING MACHINE LEARNING TO SEPARATE ATRIAL FIBRILLATION FROM OTHER ARRHYTHMIAS

PO-685-04 DIFFERING DISTRIBUTION OF PREMATURE ATRIAL COMPLEXES THAT DO AND DO NOT PRECIPITATE SPONTANEOUS ATRIAL FIBRILLATION BY NON-INVASIVE IMAGING

Abstract 14742: Deep Learning of Intracardiac Electrograms in Atrial Arrhythmia

Abstract 14738: Classification of Atrial Fibrillation by Deep Learning of Electrogram Shapes versus Rate and Regularity

B-PO02-044 ELECTROGRAM FINGERPRINTS OF ATRIAL FIBRILLATION

B-PO04-062 COMPARING THE ACCURACY OF FEATURES TO IDENTIFY ATRIAL FIBRILLATION ON INTRACARDIAC ELECTROGRAMS

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