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

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ObjectiveThe 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.

论文关键词：Nonlinear correlation,Mutual information,Higher order statistics,Wavelet transform,Electrocardiogram,Computer-aided diagnosis

论文评审过程：Received 5 April 2009, Revised 11 June 2011, Accepted 8 September 2011, Available online 1 October 2011.

论文官网地址：https://doi.org/10.1016/j.artmed.2011.09.004