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Found 5 papers, 2 papers with code
Artificial Intelligence System for Detection and Screening of Cardiac Abnormalities using Electrocardiogram Images
In this study, we present an AI system developed to detect and screen cardiac abnormalities (CAs) from real-world ECG images.
Defending Against Adversarial Attack in ECG Classification with Adversarial Distillation Training
The results show that our method performed more effectively against adversarial attacks targeting on ECG classification than the other baseline methods, namely, adversarial training, defensive distillation, Jacob regularization, and noise-to-signal ratio regularization.
A Deep Bayesian Neural Network for Cardiac Arrhythmia Classification with Rejection from ECG Recordings
Finally, with the help of a clinician, we conduct case studies to explain the results of large uncertainties and incorrect predictions with small uncertainties.
MetaVA: Curriculum Meta-learning and Pre-fine-tuning of Deep Neural Networks for Detecting Ventricular Arrhythmias based on ECGs
MAML is expected to better transfer the knowledge from a large dataset and use only a few recordings to quickly adapt the model to a new person.
CardioLearn: A Cloud Deep Learning Service for Cardiac Disease Detection from Electrocardiogram
Electrocardiogram (ECG) is one of the most convenient and non-invasive tools for monitoring peoples' heart condition, which can use for diagnosing a wide range of heart diseases, including Cardiac Arrhythmia, Acute Coronary Syndrome, et al.
