Based on a convolutional neural network (CNN) approach, this article proposes an
improved ResNet‐18 model for heartbeat classification of electrocardiogram (ECG) signals …

An automatic system for heart arrhythmia classification can perform a substantial role in
managing and treating cardiovascular diseases. In this paper, a deep learning-based multi …

Background The electrocardiogram (ECG) has been widely used in the diagnosis of heart
disease such as arrhythmia due to its simplicity and non-invasive nature. Arrhythmia can be …

The electrocardiogram (ECG) is one of the most powerful tools used in hospitals to analyze
the cardiovascular status and check health, a standard for detecting and diagnosing …

Arrhythmia classification is the need of the hour as the world is reporting a higher death troll
as a cause of cardiac diseases. Most of the existing methods developed for arrhythmia …

The classification of heartbeats is an important method for cardiac arrhythmia analysis. This
study proposes a novel heartbeat classification method using hybrid time-frequency analysis …

Although convolutional neural networks (CNNs) can be used to classify electrocardiogram
(ECG) beats in the diagnosis of cardiovascular disease, ECG signals are typically processed …

Medical practitioners need to understand the critical features of ECG beats to diagnose and
identify cardiovascular conditions accurately. This would be greatly facilitated by identifying …

Cardiovascular diseases affect approximately 50 million people worldwide; thus, heart
disease prevention is one of the most important tasks of any health care system. Despite the …

Cardiovascular diseases (CVDs) are the leading cause of death today. The current
identification method of the diseases is analyzing the Electrocardiogram (ECG), which is a …