Least Squares Support Vector Machine Classifiers
Neural Processing Letters
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Selection of significant independent components for ECG beat classification
Expert Systems with Applications: An International Journal
Optimal selection of wavelet basis function applied to ECG signal denoising
Digital Signal Processing
Expert Systems with Applications: An International Journal
ECG beat classification using particle swarm optimization and radial basis function neural network
Expert Systems with Applications: An International Journal
IEEE Transactions on Information Technology in Biomedicine
Block-Based Neural Networks for Personalized ECG Signal Classification
IEEE Transactions on Neural Networks
Automated detection of atrial fibrillation using Bayesian paradigm
Knowledge-Based Systems
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Electrocardiogram (ECG) is the P, QRS, T wave indicating the electrical activity of the heart. The subtle changes in amplitude and duration of ECG cannot be deciphered precisely by the naked eye, hence imposing the need for a computer assisted diagnosis tool. In this paper we have automatically classified five types of ECG beats of MIT-BIH arrhythmia database. The five types of beats are Normal (N), Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Atrial Premature Contraction (APC) and Ventricular Premature Contraction (VPC). In this work, we have compared the performances of three approaches. The first approach uses principal components of segmented ECG beats, the second approach uses principal components of error signals of linear prediction model, whereas the third approach uses principal components of Discrete Wavelet Transform (DWT) coefficients as features. These features from three approaches were independently classified using feed forward neural network (NN) and Least Square-Support Vector Machine (LS-SVM). We have obtained the highest accuracy using the first approach using principal components of segmented ECG beats with average sensitivity of 99.90%, specificity of 99.10%, PPV of 99.61% and classification accuracy of 98.11%. The system developed is clinically ready to deploy for mass screening programs.