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Neural Networks: A Comprehensive Foundation
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Expert Systems with Applications: An International Journal
A switchable scheme for ECG beat classification based on independent component analysis
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Mathematical Morphology Based ECG Feature Extraction for the Purpose of Heartbeat Classification
CISIM '07 Proceedings of the 6th International Conference on Computer Information Systems and Industrial Management Applications
Evolving a Bayesian classifier for ECG-based age classification in medical applications
Applied Soft Computing
Integration of independent component analysis and neural networks for ECG beat classification
Expert Systems with Applications: An International Journal
A modified mixture of experts network structure for ECG beats classification with diverse features
Engineering Applications of Artificial Intelligence
Adaptable noise reduction of ECG signals for feature extraction
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
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An electrocardiogram (ECG) beat classification scheme based on multiple signal classification (MUSIC) algorithm, morphological descriptors, and neural networks is proposed for discriminating nine ECG beat types. These are normal, fusion of ventricular and normal, fusion of paced and normal, left bundle branch block, right bundle branch block, premature ventricular concentration, atrial premature contraction, paced beat, and ventricular flutter. ECG signal samples from MIT-BIH arrhythmia database are used to evaluate the scheme. MUSIC algorithm is used to calculate pseudospectrum of ECG signals. The low-frequency samples are picked to have the most valuable heartbeat information. These samples along with two morphological descriptors, which deliver the characteristics and features of all parts of the heart, form an input feature vector. This vector is used for the initial training of a classifier neural network. The neural network is designed to have nine sample outputs which constitute the nine beat types. Two neural network schemes, namely multilayered perceptron (MLP) neural network and a probabilistic neural network (PNN), are employed. The experimental results achieved a promising accuracy of 99.03% for classifying the beat types using MLP neural network. In addition, our scheme recognizes NORMAL class with 100% accuracy and never misclassifies any other classes as NORMAL.