Objective. The standard twelve-lead electrocardiogram (ECG) is a widely used tool for
monitoring cardiac function and diagnosing cardiac disorders. The development of smaller …

Deep learning-based methods have demonstrated high classification performance in the
detection of cardiovascular diseases from electrocardiograms (ECGs). However, their …

The main challenge in utilizing deep learning (DL) for clinical diagnostic support is its lack of
explainability and interpretability. Recent approaches aim to explain DL decisions from …

Atrial fibrillation (AF) is our society's most common cardiac arrhythmic disease, leading to
increased morbidity and mortality. Predicting AF episodes during sinus rhythm based on …

Atrial fibrillation (AF) is the most commonly treated arrhythmia and is associated with the risk
of stroke and heart failure. As its diagnosis is often based on the analysis of a Holter …

With the advancement in digital communication and artificial intelligence-based
applications, the emphasis on information security has intensified. Traditional authentication …

Objective. The availability of online electrocardiogram (ECG) repositories can aid
researchers in develo** automated cardiac abnormality diagnostic systems. Using such …

Sympathovagal imbalance is known to precede the on-set of atrial fibrillation (AF) and has
been analyzed extensively based on heart rate variability (HRV). However, the relationship …

Deep learning (DL) has demonstrated high accuracy in ECG analysis but lacks in
explainability. Although explanations can be estimated using explainable artificial …

Long-term electrocardiograms (ECGS) recorded with mobile patches can help to detect
paroxysmal diseases like atrial fibrillation (AF) when combined with automated ECG …