An electrocardiogram (ECG) measures the electric activity of the heart and has been widely
used for detecting heart diseases due to its simplicity and non-invasive nature. By analyzing�…

Classification is one of the most popular topics in healthcare and bioinformatics, especially
in relation to arrhythmia detection. Arrhythmias are irregularities in the rate or rhythm of the�…

In this paper, we propose a new approach for heartbeat classification based on a
combination of morphological and dynamic features. Wavelet transform and independent�…

A novel method for the electrocardiogram (ECG) beat classification according to a
combination of projected and dynamic features is presented. Projected features are derived�…

Cardiac arrhythmia is a condition when the heart rate is irregular either the beat is too slow
or too fast. It occurs due to improper electrical impulses that coordinates the heart beats�…

We present a fully automatic and fast ECG arrhythmia classifier based on a simple brain-
inspired machine learning approach known as Echo State Networks. Our classifier has a low�…

An adaptive system for the automatic processing of the electrocardiogram (ECG) for the
classification of heartbeats into one of the five beat classes recommended by ANSI/AAMI�…

Objective: This paper aims to prove that automatic beat classification on ECG signals can be
effectively solved with a pure knowledge-based approach, using an appropriate set of�…

Cardiovascular diseases affect approximately 50 million people worldwide; thus, heart
disease prevention is one of the most important tasks of any health care system. Despite the�…

Computer-assisted cardiac arrhythmia detection and classification can play a significant role
in the management of cardiac disorders. In this paper, we propose a new approach for�…