M. Alhusseini

发表

Comparing phase and electrographic flow mapping for persistent atrial fibrillation

Paul J. Wang, J. Brachmann, S. Narayan, 2019, Pacing and clinical electrophysiology : PACE.

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

W. Rappel, S. Narayan, M. Alhusseini, 2018, Front. Physiol..

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

I. Hernández-Romero, S. Narayan, A. Rogers, 2021, Frontiers in Physiology.

Non-Invasive Assessment of Complexity of Atrial Fibrillation: Correlation with Contact Mapping and Impact of Ablation.

Paul J. Wang, I. Hernández-Romero, F. Fernández‐Avilés, 2020, Circulation. Arrhythmia and electrophysiology.

Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death

Paul J. Wang, Peter Bailis, M. Zaharia, 2020, Circulation research.

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

Paul J. Wang, J. Brachmann, W. Rappel, 2018, Circulation. Arrhythmia and electrophysiology.

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

Paul J. Wang, S. Narayan, O. Razeghi, 2022, Circulation. Arrhythmia and electrophysiology.

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

S. Narayan, Y. Dong, A. Rogers, 2019, PloS one.

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

Paul J. Wang, J. Brachmann, S. Narayan, 2018, Circulation. Arrhythmia and electrophysiology.

Noninvasive Assessment of Complexity of Atrial Fibrillation

Paul J. Wang, I. Hernández-Romero, F. Fernández‐Avilés, 2020 .

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

Paul J. Wang, M. Turakhia, J. Brachmann, 2019, Circulation. Arrhythmia and electrophysiology.

Clinical Implications of Ablation of Drivers for Atrial Fibrillation: A Systematic Review and Meta-Analysis

Paul J. Wang, S. Narayan, P. Heidenreich, 2018, Circulation. Arrhythmia and electrophysiology.

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

W. Rappel, S. Narayan, A. Rogers, 2021, PloS one.

Two Independent Mapping Techniques Identify Rotational Activity Patterns at Sites of Local Termination During Persistent Atrial Fibrillation

Paul J. Wang, W. Rappel, S. Narayan, 2017, Journal of cardiovascular electrophysiology.

Spatial relationship of sites for atrial fibrillation drivers and atrial tachycardia in patients with both arrhythmias.

Paul J. Wang, S. Narayan, A. Rogers, 2017, International journal of cardiology.

Machine Learning to Classify Intracardiac Electrical Patterns During Atrial Fibrillation

Peter Bailis, Matei Zaharia, Sanjiv M. Narayan, 2020, Circulation. Arrhythmia and electrophysiology.

Atrial fibrillation signatures on intracardiac electrograms identified by deep learning

S. Narayan, A. Rogers, C. Krittanawong, 2022, Comput. Biol. Medicine.

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

Paul J. Wang, D. Rubin, S. Narayan, 2023, Journal of cardiovascular electrophysiology.

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

Paul J. Wang, M. Turakhia, J. Brachmann, 2020, Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.

Deep Neural Network Trained on Surface ECG Improves Diagnostic Accuracy of Prior Myocardial Infarction Over Q Wave Analysis

Paul J. Wang, G. Clifford, L. Tereshchenko, 2020, 2021 Computing in Cardiology (CinC).

B-AB16-04 DEEP LEARNING IMPROVES ON CLASSICAL FEATURES FOR IDENTIFYING ATRIAL TACHYARRHYTHMIAS FROM INTRACARDIAC ELECTROGRAMS

S. Narayan, A. Rogers, C. Krittanawong, 2021, Heart Rhythm.

MACHINE LEARNING CLASSIFIES INTRACARDIAC ELECTROGRAMS OF ATRIAL FIBRILLATION FROM OTHER ARRHYTHMIAS

S. Narayan, A. Rogers, C. Krittanawong, 2021 .

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

S. Narayan, I. Pantos, D. Krummen, 2017, International journal of cardiology.

Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death

Paul J. Wang, M. Zaharia, Firas Abuzaid, 2020 .

Abstract 11100: Persistent Atrial Fibrillation as a Dynamic Network of Competing Zones of Control

Paul J. Wang, W. Rappel, S. Narayan, 2019 .

Deep Neural Network Trained on Surface ECG Improves Diagnostic Accuracy of Prior Myocardial Infarction Over Q Wave Analysis

Paul J. Wang, G. Clifford, L. Tereshchenko, 2021, 2021 Computing in Cardiology (CinC).

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

S. Narayan, I. Pantos, D. Krummen, 2017, International journal of cardiology.