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Found 5 papers, 1 papers with code
Interpretation of Intracardiac Electrograms Through Textual Representations
Understanding the irregular electrical activity of atrial fibrillation (AFib) has been a key challenge in electrocardiography.
Automated Cardiovascular Record Retrieval by Multimodal Learning between Electrocardiogram and Clinical Report
Automated interpretation of electrocardiograms (ECG) has garnered significant attention with the advancements in machine learning methodologies.
Transfer Knowledge from Natural Language to Electrocardiography: Can We Detect Cardiovascular Disease Through Language Models?
The learned embeddings are evaluated on two downstream tasks: (1) automatic ECG diagnosis report generation, and (2) zero-shot cardiovascular disease detection.
GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction
In this paper, we propose a physiologically-inspired data augmentation method to improve performance and increase the robustness of heart disease detection based on ECG signals.
Cardiac Disease Diagnosis on Imbalanced Electrocardiography Data Through Optimal Transport Augmentation
In this paper, we focus on a new method of data augmentation to solve the data imbalance problem within imbalanced ECG datasets to improve the robustness and accuracy of heart disease detection.
