In this paper, we propose a novel approach based on deep learning for active classification
of electrocardiogram (ECG) signals. To this end, we learn a suitable feature representation …

A new semi-supervised approach based on deep learning and active learning for
classification of electrocardiogram signals (ECG) is proposed. The objective of the proposed …

Arrhythmias reflect electrical abnormalities of the heart, and they can lead to severe harm to
the heart. An electrocardiogram (ECG) is a useful tool to manifest arrhythmias. In this paper …

This paper presents an automatic wearable electrocardiogram (ECG) classification and
monitoring system with stacked denoising autoencoder (SDAE). We use a wearable device …

Cardiovascular disease (CVD) is the leading cause of death worldwide, and coronary artery
disease (CAD) is a major contributor. Early-stage CAD can progress if undiagnosed and left …

The electrocardiogram is the most widely used diagnostic tool that records the electrical
activity of the heart and, therefore, its use for identifying markers for early diagnosis and …

Electrocardiogram (ECG) is the most common and low‐cost diagnostic tool used in
healthcare institutes for screening heart electrical signals. The abnormal heart signals are …

In this paper, we present three active learning strategies for the classification of
electrocardiographic (ECG) signals. Starting from a small and suboptimal training set, these …

Arrhythmia classification is crucial in electrocardiogram (ECG) based automatic
cardiovascular disease diagnosis, eg, to help prevent stroke or sudden cardiac death …

Cardiovascular disease (CVD) is a general term for a series of heart or blood vessels
abnormality that serves as a global leading reason for death. The earlier the abnormal heart …