Classification of the electrocardiogram signals using supervised classifiers and efficient features

Authors:
Ataollah Ebrahim Zadeh;Ali Khazaee;Vahid Ranaee
Affiliations:
Faculty of Electrical and Computer Engineering, Babol University of Technology, Iran;Faculty of Electrical and Computer Engineering, Babol University of Technology, Iran;Faculty of Electrical and Computer Engineering, Babol University of Technology, Iran
Venue:
Computer Methods and Programs in Biomedicine
Year:
2010

Citing 11
Cited 5

Probabilistic neural networks

Neural Networks
First- and second-order methods for learning: between steepest descent and Newton's method

Neural Computation
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Advanced Methods And Tools for ECG Data Analysis

Advanced Methods And Tools for ECG Data Analysis
Frequency-domain features for ECG beat discrimination using grey relational analysis-based classifier

Computers & Mathematics with Applications
Automatic identification of cardiac health using modeling techniques: A comparative study

Information Sciences: an International Journal
Selection of significant independent components for ECG beat classification

Expert Systems with Applications: An International Journal
Features for analysis of electrocardiographic changes in partial epileptic patients

Expert Systems with Applications: An International Journal
De-noising by soft-thresholding

IEEE Transactions on Information Theory
Training feedforward networks with the Marquardt algorithm

IEEE Transactions on Neural Networks

Neural networks versus genetic algorithms as medical classifiers

IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
Application of statistics and machine learning for risk stratification of heritable cardiac arrhythmias

Expert Systems with Applications: An International Journal
Computer-aided diagnosis system: A Bayesian hybrid classification method

Computer Methods and Programs in Biomedicine
Real-time CHF detection from ECG signals using a novel discretization method

Computers in Biology and Medicine
Multistage approach for clustering and classification of ECG data

Computer Methods and Programs in Biomedicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

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 premature ventricular contraction from the normal beats and other heart diseases. This system includes three main modules: denoising module, feature extraction module and classifier module. In the denoising module, it is proposed the stationary wavelet transform for noise reduction of the electrocardiogram signals. In the feature extraction module a proper combination of the morphological-based features and timing interval-based features are proposed. As the classifier, several supervised classifiers are investigated; they are: a number of multi-layer perceptron neural networks with different number of layers and training algorithms, support vector machines with different kernel types, radial basis function and probabilistic neural networks. Also, for comparison the proposed features, we have considered the wavelet-based features. It has done comprehensive simulations in order to achieve a high efficient system for ECG beat classification from 12 files obtained from the MIT-BIH arrhythmia database. Simulation results show that best results are achieved about 97.14% for classification of ECG beats.