Electrocardiography is the gold standard technique for detecting abnormal heart conditions.
Automatic detection of electrocardiogram (ECG) abnormalities helps clinicians analyze the …

Electrocardiogram (ECG) and photoplethysmograph (PPG) are non-invasive techniques that
provide electrical and hemodynamic information of the heart, respectively. This information …

This paper introduces an innovative automatic K-means clustering algorithm, namely HGA-
FACO, which seamlessly integrates the noise algorithm, Genetic Algorithm (GA), Ant Colony …

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 …

The classification and identification of arrhythmias using ECG signals hold substantial
practical importance in the early prevention and detection of cardiac/cardiovascular …

This article presents an innovative genetic ensembles of classifiers applied to classification
of cardiac disorders (17 classes) based on electrocardiography (ECG) signal analysis. From …

Introduction The authors of this work propose an unsupervised machine learning model that
has the ability to identify real-world latent infectious diseases by mining social media data. In …

An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG)
signals, since the non-invasive nature and simplicity of the ECG exam. According to the …

This study developed an automatic heartbeat classification system for identifying normal
beats, supraventricular ectopic beats, and ventricular ectopic beats based on normalized RR …

This paper presents a method for electrocardiogram (ECG) beat classification based on
particle swarm optimization (PSO) and radial basis function neural network (RBFNN). Six …