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Found 6 papers, 5 papers with code
Transfer Learning in ECG Diagnosis: Is It Effective?
The adoption of deep learning in ECG diagnosis is often hindered by the scarcity of large, well-labeled datasets in real-world scenarios, leading to the use of transfer learning to leverage features learned from larger datasets.
MPCNN: A Novel Matrix Profile Approach for CNN-based Sleep Apnea Classification
Sleep apnea (SA) is a significant respiratory condition that poses a major global health challenge.
MELEP: A Novel Predictive Measure of Transferability in Multi-Label ECG Analysis
We introduce MELEP, which stands for Muti-label Expected Log of Empirical Predictions, a novel measure to estimate how effective it is to transfer knowledge from a pre-trained model to a downstream task in a multi-label settings.
Enhancing Deep Learning-based 3-lead ECG Classification with Heartbeat Counting and Demographic Data Integration
Nowadays, an increasing number of people are being diagnosed with cardiovascular diseases (CVDs), the leading cause of death globally.
LightX3ECG: A Lightweight and eXplainable Deep Learning System for 3-lead Electrocardiogram Classification
In clinical practices and most of the current research, standard 12-lead ECG is mainly used.
VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations
Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs.
