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
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
ECG beats classification using multiclass support vector machines with error correcting output codes
Digital Signal Processing
IEEE Transactions on Computers
Combining recurrent neural networks with eigenvector methods for classification of ECG beats
Digital Signal Processing
Optimal selection of wavelet basis function applied to ECG signal denoising
Digital Signal Processing
IEEE Transactions on Information Technology in Biomedicine
Application of principal component analysis to ECG signals for automated diagnosis of cardiac health
Expert Systems with Applications: An International Journal
Block-Based Neural Networks for Personalized ECG Signal Classification
IEEE Transactions on Neural Networks
Fuzzy expert system approach for coronary artery disease screening using clinical parameters
Knowledge-Based Systems
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Electrocardiogram is the P-QRS-T wave representing the cardiac depolarization and re-polarization, recorded at the body surface. The subtle changes in amplitude and duration of these waves indicate various pathological conditions. It is very difficult to decipher minute changes in the ECG wave by naked eye. Hence a computer aided diagnosis tool to classify various cardiac diseases will assist the doctors in their ECG reading. In this paper, five types of ECG beats (ANSI/AAMI EC57:1998 standard) of MIT-BIH arrhythmia database were automatically classified. Our proposed methodology involves computation of Discrete Cosine Transform (DCT) coefficients from the segmented beats of ECG, which were then subjected for principal component analysis for dimensionality reduction. Then the clinically significant principal components were fed to (i) feed forward neural network, (ii) least square support vector machine with different kernel functions, and (iii) Probabilistic Neural Network (PNN) for automatic classification. We have obtained the highest average sensitivity of 98.69%, specificity of 99.91%, and classification accuracy of 99.52% with the developed knowledge based system. The developed system is clinically ready to deploy for mass screening programs.