ECG Arrhythmia Classification Using Least Squares Twin Support Vector Machines

Abstract

<div> <div> <div> <div> <div> <p>Heart disease is one of the most common causes of death.‎ Rapid diagnosis of patients with these diseases can greatly prevent them from sudden death.‎ Today, the diagnosis of heart diseases is done by cardiologist, while achieving an automatic and accurate method for diagnosing has become a challenging issue in this area.‎ Because small changes in the electrocardiogram signals are not recognizable with eyes, and visual disorders may be affected, artificial intelligence and machine learning algorithms can be the solution.‎ In this paper, we use the Least Squares Twin Support Vector Machine, which unlike ordinary support vector machine, is based on a Non-parallel margin.‎ The results show that the method of this article is better than previous methods, and more accurate and faster for diagnosing arrhythmia.‎</p> </div> </div> </div> </div> </div>