Atrial fibrillation easily leads to stroke, cerebral infarction and other complications, which will
seriously harm the life and health of patients. Traditional deep learning methods have weak …

Arrhythmia accounts for more than 80% of sudden cardiac death, and its incidence rate has
increased rapidly recently. Nowadays, many studies have applied artificial intelligence (AI) …

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

The noninvasive fetal ECG (FECG) is used to monitor fetal well-being at prenatal and
intrapartum. However, it is challenging to detect the FECG signal from the abdominal …

Automated diagnosis of cardiovascular diseases (CVDs) has become an imperative need
for remote or in-hospital heart monitoring. This is a challenging task because of the tenuous …

The degraded quality of the electrocardiogram (ECG) signals is the main source of false
alarms in critical care units. Therefore, a preliminary analysis of the ECG signal is required to …

Atrial fibrillation (AF) is one of the frequent and potentially dangerous arrhythmias that can
participate in cardioembolic stroke and heart failure. Early AF identification is possible by the …

Objective. Most arrhythmias due to cardiovascular diseases alter the heart's electrical
activity, resulting in morphological alterations in electrocardiogram (ECG) recordings. ECG …

Atrial fibrillation is the most common abnormal heart condition and contributes primarily to
cardiac morbidity and mortality. In the last decades, portable Electrocardiogram (ECG) …

Background Electrocardiogram (ECG) is a powerful tool for studying cardiac activity and
diagnosing various cardiovascular diseases, including arrhythmia. While machine learning …