Objective: Vast 12-lead ECGs repositories provide opportunities to develop new machine
learning approaches for creating accurate and automatic diagnostic systems for cardiac …

Artificial Intelligence (AI) use in automated Electrocardiogram (ECG) classification has
continuously attracted the research community's interest, motivated by their promising …

Sleep is one of the most important human physiological activities, and plays an essential
role in human health. Polysomnography (PSG) is the gold standard for measuring sleep …

Background and objectives Arrhythmia classification based on electrocardiograms (ECG)
can enhance clinical diagnostic efficiency. However, due to the significant differences in the …

Electrocardiogram (ECG) is a widely used technique for diagnosing cardiovascular disease.
The widespread emergence of smart ECG devices has sparked the demand for intelligent …

Recent advances in wearable healthcare devices such as smartwatches allow us to monitor
and manage our health condition more actively, for example, by measuring our …

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

Objective. The primary purpose of this work is to analyze the ability of N-BEATS architecture
for the problem of prediction and classification of electrocardiogram (ECG) signals. To …

This paper presents a new deep learning methodology to detect among up to 17 classes of
cardiac arrhythmia based on beat-wise electrocardiography (ECG) signal analysis using iris …

Atrial fibrillation (AF) is the most common type of sustained arrhythmia. It results from
abnormal irregularities in the electrical performance of the atria, and may cause heart …