A fast fixed-point algorithm for independent component analysis
Neural Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Diagnosis of Cardiovascular Abnormalities From Compressed ECG: A Data Mining-Based Approach
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
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
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Electrocardiogram (ECG) is widely used as a diagnostic tool to identify atrial tachyarrhythmias such as atrial fibrillation. The ECG signal is a P-QRS-T wave representing the cardiac function. The minute variations in the durations and amplitude of these waves cannot be easily deciphered by the naked eye. Hence, there is a need for computer aided diagnosis (CAD) of cardiac healthcare. The current paper presents a methodology for ECG based pattern analysis of normal sinus rhythm and atrial fibrillation (AF) beats. The denoised and registered ECG beats were subjected to independent component analysis (ICA) for data reduction. The weights of ICA were used as features for classification using Naive Bayes and Gaussian mixture model (GMM) classifiers. The performance and the upper bound on probability of error in classification were analyzed using Chernoff and Bhattacharyya bounds. The Naive Bayes classifier provided an average sensitivity of 99.32%, specificity of 99.33% and accuracy of 99.33%, while the GMM provided an average sensitivity of 100%, specificity of 99% and accuracy of 99.42%. The probability of error during classification was less for GMM compared to Naive Bayes classifier (NBC) as GMM provided higher performance than the NBC.