Abstract In this study, Random Forests (RF) classifier is proposed for ECG heartbeat signal
classification in diagnosis of heart arrhythmia. Discrete wavelet transform (DWT) is used to …

In this study, a new supervised noise-artifact-robust heart arrhythmia fusion classification
solution, is introduced. Proposed method consists of structurally diverse classifiers with a …

This work centers on a novel data mining technique we term supervised clustering. Unlike
traditional clustering, supervised clustering assumes that the examples are classified and …

This paper proposes a new power spectral-based hybrid genetic algorithm-support vector
machines (SVMGA) technique to classify five types of electrocardiogram (ECG) beats …

Automatic classification of electrocardiogram (ECG) signals is vital for clinical diagnosis of
heart disease. This paper investigates the design of an efficient system for recognition of the …

This paper introduces an electrocardiogram beat classification method based on deep belief
networks. This method includes two parts: feature extraction and classification. In the feature …

In order to detect the QRS complexes locally in the microcontroller-based embedded
system, a novel algorithm with lower computation burden is proposed by phase space …

Ventricular fibrillation (VF) is the primary arrhythmic event in the majority of patients suffering
from sudden cardiac arrest. Attention has been focused on this particular rhythm since it is …

This paper proposes a novel system to classify three types of electrocardiogram beats,
namely normal beats and two manifestations of heart arrhythmia. This system includes three …

This paper proposes a three stage technique for detection of premature ventricular
contraction (PVC) from normal beats and other heart diseases. This method includes a …