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Computing in cardiology

Showing results (11-20 of 140) with videos related to

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Computing in Cardiology|June 23, 2018
Detecting Ischemic Stress to the Myocardium Using Laplacian Eigenmaps and Changes to Conduction VelocityWilson W Good, Burak Erem, Jaume Coll-Font, et al.
Computing in Cardiology|February 5, 2026
Comparison of LGE MRI Scar Identification Methods for Atrial Computational ModelingJake A Bergquist, Benjamin Orkild, Eugene Kwan, et al.
Computing in Cardiology|February 5, 2026
Predicting Ventricular Arrhythmia in Myocardial Ischemia Using Machine LearningAnna Busatto, Jake A Bergquist, Tolga Tasdizen, et al.
Computing in Cardiology|March 23, 2019
Effects of ECG Signal Processing on the Inverse Problem of ElectrocardiographyLaura R Bear, Y Serinagaoglu Dogrusoz, J Svehlikova, et al.
Computing in Cardiology|March 23, 2019
A Common-Ground Review of the Potential for Machine Learning Approaches in Electrocardiographic Imaging Based on Probabilistic Graphical ModelsJaume Coll-Font, Linwei Wang, Dana H Brooks
Computing in Cardiology|April 29, 2017
The Role of Reduced Left Ventricular, Systolic Blood Volumes in ST Segment Potentials Overlying Diseased Tissue of the Ischemic HeartBrett M Burton, Jess D Tate, Wilson Good, et al.
Computing in Cardiology|December 14, 2011
Hypotension as a Risk Factor for Acute Kidney Injury in ICU PatientsLi-Wei Lehman, Mohammed Saeed, George Moody, et al.
Computing in Cardiology|July 19, 2011
The PhysioNet/Computing in Cardiology Challenge 2010: Mind the GapGeorge B Moody
Computing in Cardiology|July 19, 2021
Deep Learning Based Prediction of Atrial Fibrillation Disease Progression with Endocardial Electrograms in a Canine ModelBram Hunt, Eugene Kwan, Mark McMillan, et al.
Computing in Cardiology|March 13, 2020
Regions of High Dominant Frequency in Chronic Atrial Fibrillation Anchored to Areas of Atrial FibrosisNathan Angel, Eugene G Kholmovski, Elyar Ghafoori, et al.
Pageof 14

Showing results (11-20 of 140) with videos related to

Sort By:
Pageof 14
Computing in Cardiology|June 23, 2018
Detecting Ischemic Stress to the Myocardium Using Laplacian Eigenmaps and Changes to Conduction VelocityWilson W Good, Burak Erem, Jaume Coll-Font, et al.
Computing in Cardiology|February 5, 2026
Comparison of LGE MRI Scar Identification Methods for Atrial Computational ModelingJake A Bergquist, Benjamin Orkild, Eugene Kwan, et al.
Computing in Cardiology|February 5, 2026
Predicting Ventricular Arrhythmia in Myocardial Ischemia Using Machine LearningAnna Busatto, Jake A Bergquist, Tolga Tasdizen, et al.
Computing in Cardiology|March 23, 2019
Effects of ECG Signal Processing on the Inverse Problem of ElectrocardiographyLaura R Bear, Y Serinagaoglu Dogrusoz, J Svehlikova, et al.
Computing in Cardiology|March 23, 2019
A Common-Ground Review of the Potential for Machine Learning Approaches in Electrocardiographic Imaging Based on Probabilistic Graphical ModelsJaume Coll-Font, Linwei Wang, Dana H Brooks
Computing in Cardiology|April 29, 2017
The Role of Reduced Left Ventricular, Systolic Blood Volumes in ST Segment Potentials Overlying Diseased Tissue of the Ischemic HeartBrett M Burton, Jess D Tate, Wilson Good, et al.
Computing in Cardiology|December 14, 2011
Hypotension as a Risk Factor for Acute Kidney Injury in ICU PatientsLi-Wei Lehman, Mohammed Saeed, George Moody, et al.
Computing in Cardiology|July 19, 2011
The PhysioNet/Computing in Cardiology Challenge 2010: Mind the GapGeorge B Moody
Computing in Cardiology|July 19, 2021
Deep Learning Based Prediction of Atrial Fibrillation Disease Progression with Endocardial Electrograms in a Canine ModelBram Hunt, Eugene Kwan, Mark McMillan, et al.
Computing in Cardiology|March 13, 2020
Regions of High Dominant Frequency in Chronic Atrial Fibrillation Anchored to Areas of Atrial FibrosisNathan Angel, Eugene G Kholmovski, Elyar Ghafoori, et al.
Pageof 14