Artif Intell Med - Selection of effective features for ECG beat recognition based on nonlinear correlations.

Tópicos

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Resumo

JECTIVE: 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.

Resumo Limpo

jectiv object studi develop featur selector base nonlinear correl order select effect least redund featur ecg beat classif system base higher order statist subband compon feedforward backpropag neural network denot hosdwtffbnnmethod materi three correlationbas filter ncbfs propos two ncbf ncbf appli featurefeatur correl remov redund featur prior featur select process base featureclass correl sufco skip redund reduct process select featur base featureclass correl perform filter compar anoth common use nonlinear featur select method relieff discrimin redund retain featur evalu quantit perform effect ncbf compar linear correlationbas filter lcbf repres heartbeat classifi literatureresult result demonstr two ncbfs base featurefeatur featureclass correl method ie ncbf ncbf outperform two method ie sufco relieff accuraci high can attain eight featur test statist method retain featur select ncbfncbf approach demonstr discrimin less redund compar featur select method compar lcbf heartbeat classifi literatur propos ncbfncbf approach conjunct hosdwtffbnn structur outperform improv perform allow discrimin beat type fewer featur dimensionsconclus studi demonstr effect superior propos approach ecg beat recognit

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