Deep learning models developed through multi-lead electrocardiogram (ECG) signals are
considered the leading methods for the automated detection of arrhythmia on computer …

Electrocardiogram (ECG) is a widely used reliable, non-invasive approach for
cardiovascular disease diagnosis. With the rapid growth of ECG examinations and the …

With the emergence of the Internet of Things (IoT), investigation of different diseases in
healthcare improved, and cloud computing helped to centralize the data and to access …

In this paper, a novel approach based on deep belief networks (DBN) for electrocardiograph
(ECG) arrhythmias classification is proposed. The construction process of ECG classification …

According to the World Health Organization (WHO) report, heart disease is spreading
throughout the world very rapidly and the situation is becoming alarming in people aged 40 …

Cardiovascular disease (CVD) has become one of the most serious diseases that threaten
human health. Over the past decades, over 150 million humans have died of CVDs. Hence …

Electrocardiogram (ECG) signal analysis can be time-consuming, tedious, and error-prone.
Therefore, automated analysis is need of time that will assist clinicians in detecting cardiac …

Arrhythmia detection and classification is a crucial step for diagnosing cardiovascular
diseases. However, deep learning models that are commonly used and trained in end-to …

Cardiac arrhythmias pose a significant danger to human life; therefore, it is of utmost
importance to be able to efficiently diagnose these arrhythmias promptly. There exist many …

The electrocardiogram (ECG) is a highly effective non-invasive tool for monitoring heart
activity and diagnosing cardiovascular diseases (CVDs). Automatic detection of arrhythmia …