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Comparison of Detailed and Simplified Models of Human Atrial Myocytes to Recapitulate Patient Specific Properties.

, , , and . PLoS Comput. Biol., (2016)

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Comparison of Detailed and Simplified Models of Human Atrial Myocytes to Recapitulate Patient Specific Properties., , , and . PLoS Comput. Biol., (2016)Deep Neural Network Trained on Surface ECG Improves Diagnostic Accuracy of Prior Myocardial Infarction Over Q Wave Analysis., , , , , , , , , and 4 other author(s). CinC, page 1-4. IEEE, (2021)Deep Learning for Ventricular Arrhythmia Prediction Using Fibrosis Segmentations on Cardiac MRI Data., , , , , , , , , and 5 other author(s). CinC, page 1-4. IEEE, (2022)Atrial fibrillation signatures on intracardiac electrograms identified by deep learning., , , , , , , and . Comput. Biol. Medicine, (2022)Defining the Predictive Ceiling of Electrogram Features Alone for Predicting Outcomes From Atrial Fibrillation Ablation., , , , , , , , , and 1 other author(s). CinC, page 1-4. IEEE, (2023)Adaptive frequency tracking of the baseline ECG identifies the site of atrial fibrillation termination by catheter ablation., , , , , , , and . Biomed. Signal Process. Control., 8 (6): 969-980 (2013)A deep learning-based electrocardiogram risk score for long term cardiovascular death and disease., , , , , , , , , and 7 other author(s). npj Digit. Medicine, (2023)Spectral analysis of periodic fluctuations in electrocardiographic repolarization., and . IEEE Trans. Biomed. Eng., 46 (2): 203-212 (1999)Intra-cardiac Signatures of Atrial Arrhythmias Identified by Machine Learning and Traditional Features., , , , , and . FIMH, volume 12738 of Lecture Notes in Computer Science, page 671-678. Springer, (2021)Temporal Stability of Dominant Frequency as Predictor of Atrial Fibrillation Recurrence., , , , , , , , and . CinC, page 1-4. IEEE, (2019)