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1. ConvexECG: Lightweight and Explainable Neural Networks for Personalized, Continuous Cardiac Monitoring

2. Successful cardiac resynchronization therapy reduces negative septal work in patient-specific models of dyssynchronous heart failure

8. List of contributors

15. Predicting acute termination and non-termination during ablation of human atrial fibrillation using quantitative indices

22. Electrical Substrate Ablation for Refractory Ventricular Fibrillation: Results of the AVATAR Study.

24. Populations of in silico myocytes and tissues reveal synergy of multiatrial‐predominant K+‐current block in atrial fibrillation

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

30. Abstract 17934: Performance of an Electrocardiographic Deep Learning Model for Detecting Wall Motion Abnormalities: An Analysis of Impact From Cardiovascular Comorbidities and Race in an External Validation Study

33. Abstract 17420: Enhanced Identification of Cardiac Wall Motion Abnormalities: An Externally Validated Deep Neural Network Approach Outperforms Expert and Quantitative Analysis of Electrocardiograms

36. Opportunities and challenges in heart rhythm research: Rationale and development of an electrophysiology collaboratory

37. Intra-cardiac Signatures of Atrial Arrhythmias Identified by Machine Learning and Traditional Features

38. Successful Cardiac Resynchronization Therapy Reduces Negative Septal Work in Patient-Specific Models of Dyssynchronous Heart Failure

39. Visions for digital integrated cardiovascular care: HRS Digital Health Committee perspectives

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