Heart disease is one of the significant challenges in today's world and one of the leading
causes of many deaths worldwide. Recent advancement of machine learning (ML) …

The global need for effective disease diagnosis remains substantial, given the complexities
of various disease mechanisms and diverse patient symptoms. To tackle these challenges …

Diagnosing the fetal cardiac abnormalities by fetal electrocardiogram (FECG) is a difficult
task, but it is essential to identify the fetus health condition. FECG monitoring is essential to …

Cardiovascular disease is the leading cause of global mortality and responsible for millions
of deaths annually. The mortality rate and overall consequences of cardiac disease can be …

The classification of Cardiotocography (CTG) signal abnormalities is critical in the
identification of fetal anomalies. The non-stationary nature of CTG, as well as the dataset …

Imbalanced class distribution of time series data often results in bias in the classification of
surfaces, the classifier cannot usually achieve the best classification performance. When the …

Data-driven learning is the most representative deep learning method. Generative
adversarial networks (GANs) are designed to generate sufficient data to support such …

This study uses convolutional neural networks (CNNs) and cardiotocography data for the
real-time classification of fetal status in the mobile application of a pregnant woman and the …

The rapid advancement of deep neural networks (DNNs) has significantly transformed
various sectors, demonstrating unparalleled proficiency in managing intricate tasks in …

Recently, significant efforts have been made to create Health Digital Twins (HDTs), Digital
Twins for clinical applications. Heart modeling is one of the fastest-growing fields, which …