| Year
|
Name
|
Title of Abstract
|
Affiliation
|
| 2025 |
Bauke Arends
|
Electrocardiogram Based Prediction Of Structural Heart Disease Risk Using Explainable Deep Learning |
Utrecht University, Nederlands |
| 2024 |
Nathaniel Riek |
Saliency Maps to Enhance Explainability of Occlusion Myocardial Infarction Classification Among Pre-Hospital Chest Pain Patients |
University of Pittsburg, USA |
| 2023 |
Iris van der Schaaf,
|
CineECG for Visualization of Changes in Ventricular Activation and Repolarization During Ischemia
|
University Medical Center Utrecht, Netherlands |
| 2022 |
Trisha Dwivedi |
Machine Learning Models of 6L ECGs for the Interpretation of Left Ventricular Hypertrophy (LVH)
|
AliveCor, USA |
| 2021 |
Brian Zenger |
Electrocardiographic Differences in Acute Ischemic Responsesto Exercise and Pharmacological Stress |
University of Utah, Utah |
| 2019 |
Jacob Melgaard |
Method to Visualize and Quantify Ventricular Dyssynchrony |
Aalborg University, Denmark |
| 2018 |
Ran Xiao |
Identification of Ischemic ST Changes through Deep Learning |
University of California San Francisco, California |
| 2017 |
Travis Moss |
Continuous ECG Monitoring of Cardiorespiratory Dynamics Detects Clinical Deterioration in Acute Care Patients with Cardiovascular Disease |
University of Virginia, Virginia |
| 2016 |
Jose Vicente |
Detecting late sodium current block on the ECG: biomarkers beyond QTc |
US Food and Drug Administration, Maryland |
| 2015 |
Naoki Misumida |
Abnormal Q wave in Leads I and aVL Predicts Long-term Adverse Cardiovascular Events in Patients with First Anterior S Elevation Myocardial Infarction |
Mount Sinai Beth Israel, New York |
| 2014 |
Kedar Aras |
Sensitivity of epicardial electrical markers to acute myocardial ischemia detection |
University of Utah, Utah |
| 2013 |
Peter van Dam |
Going beyond QT: Integrated electrocardiographic and vectorcardiographic analysis of20 QTc-prolonging drugs |
University of Nijmegen, Netherlands |
|
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