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Found 4 papers, 2 papers with code
Text-to-ECG: 12-Lead Electrocardiogram Synthesis conditioned on Clinical Text Reports
We compare the performance of our model with other representative models in text-to-speech and text-to-image.
Automatic Detection of Noisy Electrocardiogram Signals without Explicit Noise Labels
Automatic deep learning-based examination of ECG signals can lead to inaccurate diagnosis, and manual analysis involves rejection of noisy ECG samples by clinicians, which might cost extra time.
Lead-agnostic Self-supervised Learning for Local and Global Representations of Electrocardiogram
In this work, we propose an ECG pre-training method that learns both local and global contextual representations for better generalizability and performance on downstream tasks.
Towards Synthesizing Twelve-Lead Electrocardiograms from Two Asynchronous Leads
The electrocardiogram (ECG) records electrical signals in a non-invasive way to observe the condition of the heart, typically looking at the heart from 12 different directions.
