Selection of effective features for ECG beat recognition based on nonlinear correlations

Objective jective 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. s and materials 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. s sults 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. sion tudy demonstrates the effectiveness and superiority of the proposed approach for ECG beat recognition.