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 …

With the alarming rise in the deaths due to cardiovascular diseases (CVD), present medical
research scenario places notable importance on techniques and methods to detect CVDs …

Fusion of feature descriptors extracted from a signal through different methods is an
important issue for the exploitation of representational power of each descriptor. In this …

This work presents a new modular and low-complexity algorithm for the delineation of the
different ECG waves (QRS, P and T peaks, onsets, and end). Involving a reduced number of …

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 …

Objective: Fragmented QRS (fQRS) is an accessible biomarker and indication of myocardial
scarring that can be detected from the electrocardiogram (ECG). Nowadays, fQRS scoring is …

From recent studies, it is observed that convolutional neural networks are proved to be
extremely successful in classification problems. Accurate and fast classification of …

Abstract Background and Objective Heart disease seriously threatens human life and health.
It has the character of abruptness and is necessary to accurately monitor and intelligently …

In this paper, we propose a novel method for extracting fiducial points (FPs) of
electrocardiogram (ECG) signals. We propose the use of multi hidden Markov model …

Electrocardiogram analysis for the classification of several cardiac arrhythmias has gained a
significant research importance in the medical field. Towards such objective, this paper …