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Found 6 papers, 3 papers with code
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.
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.
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.
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.
ECG-QA: A Comprehensive Question Answering Dataset Combined With Electrocardiogram
As a result, our dataset includes diverse ECG interpretation questions, including those that require a comparative analysis of two different ECGs.
Optimizing Neural Network Scale for ECG Classification
Although ECG signals are time-series data, CNN-based models have been shown to outperform other neural networks with different architectures in ECG analysis.
