In this study, heartbeat time series are classified using support vector machines (SVMs).
Statistical methods and signal analysis techniques are used to extract features from the …

A method for the automatic classification of electrocardiograms (ECG) based on the
combination of multiple Support Vector Machines (SVMs) is presented in this work. The …

In this paper, we present a new system for the classification of electrocardiogram (ECG)
beats by using a fast least square support vector machine (LSSVM). Five feature extraction …

The aim of this paper is twofold. First, we present a thorough experimental study to show the
superiority of the generalization capability of the support vector machine (SVM) approach in …

OBJECTIVE: This paper presents an effective cardiac arrhythmia classification algorithm
using the heart rate variability (HRV) signal. The proposed algorithm is based on the …

A new approach based on the implementation of multiclass support vector machine (SVM)
with the error correcting output codes (ECOC) is presented for classification of …

This study proposed an ECG (Electrocardiogram) classification approach using machine
learning based on several ECG features. An electrocardiogram (ECG) is a signal that …

Time series classification is an important task in time series data mining, and has attracted
great interests and tremendous efforts during last decades. However, it remains a …

Arrhythmia is one of the major cause of deaths across the globe. Almost 17.9 million deaths
are caused due to cardiovascular diseases. In order to reduce this much mortality rate, the …

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