Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
ECG beat classification using neuro-fuzzy network
Pattern Recognition Letters
IEEE Intelligent Systems
ECG beats classification using multiclass support vector machines with error correcting output codes
Digital Signal Processing
Pattern Recognition Letters
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
A modified mixture of experts network structure for ECG beats classification with diverse features
Engineering Applications of Artificial Intelligence
IEEE Transactions on Information Technology in Biomedicine
An arrhythmia classification system based on the RR-interval signal
Artificial Intelligence in Medicine
Block-Based Neural Networks for Personalized ECG Signal Classification
IEEE Transactions on Neural Networks
Artificial Intelligence in Medicine
Fuzzy logic-based diagnostic algorithm for implantable cardioverter defibrillators
Artificial Intelligence in Medicine
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Objective: The objective of this study is to develop feature selectors based on nonlinear correlations in order to select the most effective and least redundant features from an ECG beat classification system based on higher order statistics of subband components and a feed-forward back-propagation neural network, denoted as HOS-DWT-FFBNN. Methods and materials: Three correlation-based filters (NCBFs) are proposed. Two of them, NCBF1 and NCBF2, apply feature-feature correlation to remove redundant features prior to the feature selection process based on feature-class correlation. The other, SUFCO, skips the redundancy reduction process and selects features based only on feature-class correlation. The performance of these filters is compared to another commonly used nonlinear feature selection method, Relief-F. The discriminality and redundancy of the retained features are evaluated quantitatively. The performance of the most effective NCBF is compared with that of the linear correlation-based filter (LCBF) and other representative heartbeat classifiers in the literature. Results: The results demonstrate that the two NCBFs based on both feature-feature and feature-class correlation methods, i.e. NCBF1 and NCBF2, outperform the other two methods, i.e. SUFCO and Relief-F. An accuracy of as high as 96.34% can be attained with as few as eight features. When tested with statistical methods, the retained features selected by the NCBF1/NCBF2 approach are demonstrated to be more discriminative and less redundant when compared with those features selected by other methods. When compared with LCBF and other heartbeat classifiers in the literature, the proposed NCBF1/NCBF2 approach in conjunction with the HOS-DWT-FFBNN structure outperform them with improved performance that allows discrimination of more beat types and fewer feature dimensions. Conclusion: This study demonstrates the effectiveness and superiority of the proposed approach for ECG beat recognition.