Training deep neural network classifiers for electrocardiograms (ECGs) requires sufficient
data. However, imbalanced datasets pose a major problem for the training process and …

Large high-quality datasets are essential for building powerful artificial intelligence (AI)
algorithms capable of supporting advancement in cardiac clinical research. However …

The scientific community has recently shown increasing interest in generating synthetic ECG
data. In particular, synthetic ECG signals can be beneficial for understanding cardiac …

Nowadays, Electrocardiogram (ECG) signals can be measured using wearable devices,
such as smart watches. Most wearable devices provide only a few details; however, they …

Electrocardiogram (ECG) data collection during emergency situations is challenging,
making ECG data generation an efficient solution for dealing with highly imbalanced ECG …

Deep learning models need a sufficient amount of data in order to be able to find the hidden
patterns in it. It is the purpose of generative modeling to learn the data distribution, thus …

AI techniques have recently been put under the spotlight for analyzing electrocardiograms
(ECGs). However, the performance of AI-based models relies on the accumulation of large …

This paper presents the results of a novel scoping review on the practical models for
generating three different types of synthetic health records (SHRs): medical text, time series …

Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide. Recent
advancements in machine learning have significantly enhanced early detection and …

Generative adversarial networks (GANs) have become a full-fledged branch of the most
important neural network models for unsupervised machine learning. A multitude of loss …