Abstract The 3D Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM) are
used in this study to analyze and characterize Electrocardiogram (ECG) signals. This …

Electrocardiograms (ECG) are extensively used for the diagnosis of cardiac arrhythmias.
This paper investigates the use of machine learning classification algorithms for ECG …

Automated electrocardiogram (ECG) analysis cannot be employed in clinical practice due to
the accuracy of the present models. Deep Neural Networks (DNNs) are models made up of …

In medical science, the scrutiny of blood smears for the abnormality in erythrocyte, leads to
decisive determination of several ailments like Thalasemia, Liver disease, Sickle cell …

Ventricular fibrillation and ventricular tachycardia are shockable cardiac symptoms. These
ventricular arrhythmias (VAs) raise the risk of heart failure by causing heart attacks and other …

An electrocardiogram (ECG) is a diagnostic tool that captures the electrical activity of the
heart. Any irregularity in the heart's electrical system is referred to as an arrhythmia, which …

In general, heart disease is considered as the most severe disease that results in high
mortality rate globally. An arrhythmia is one type of heart disease caused due to …

An intelligence-based electrocardiogram (ECG) signal classification algorithm is very
effective in monitoring Cardiac arrhythmias and helps the specialist make a decision and …

Objective Arrhythmias are prevalent symptoms of cardiovascular disease, necessitating
accurate and timely detection to mitigate associated risks. Detecting arrhythmias from ECGs …

ECG recordings has been commonly used biological experiment for analysing the lot of
heart issues. Moreover, timely identification of arrhythmia will help to recognise the …