Contemporary methods used to interpret the electrocardiogram (ECG) signal for diagnosis
or monitoring are based on expert knowledge and rule-centered algorithms. In recent years …

In the dynamic field of medical artificial intelligence (AI), cardiology stands out as a key area
for its technological advancements and clinical application. This review explores the …

Deep learning has emerged as the preferred modeling approach for automatic ECG
analysis. In this study, we investigate three elements aimed at improving the quantitative …

Cardiovascular diseases (CVDs) are a major global health concern, causing significant
morbidity and mortality. AI's integration with healthcare offers promising solutions, with data …

Providing reliable detection of QRS complexes is key in automated analyses of
electrocardiograms (ECG). Accurate and timely R-peak detections provide a basis for ECG …

The field of deep-learning-based ECG analysis has been largely dominated by
convolutional architectures. This work explores the prospects of applying the recently …

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) plays a critical role in the early detection of heart diseases.
However, ECG signals are often contaminated with various types of noises, including …

Background Heart failure (HF), a global health challenge, requires innovative diagnostic and
management approaches. The rapid evolution of deep learning (DL) in healthcare …

The automated analysis of medical diagnostic videos, such as ultrasound and endoscopy,
provides significant benefits in clinical practice by improving the efficiency and accuracy of …