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发布时间：2019-07-01

论文类型：会议论文

发表时间：2018-01-01

收录刊物：CPCI-S

页面范围：2753-2755

摘要：ECG-based heartbeat classification is often accompanied with difficult feature extraction and imbalanced sampling data. In order to alleviate the bias in performance caused by imbalanced data, a Selective Ensemble Learning Framework based on sample Distribution and classifier Diversity (SELFrame-DD) is proposed for ECG-based heartbeat classification. In SELFrame-DD, an improved SMOTE algorithm is proposed to generate training sets by using a sample-distribution based resampling strategy, and the selective ensemble depends on the diversity of classifiers and the prediction accuracy of classifiers for minority classes. Besides, a multimodal ECG feature extraction is employed based on wavelet packet decomposition and 1-D convolutional neural network. Experimental studies on MIT-BIH arrhythmia database show that the proposed algorithm can achieve a high classification accuracy for imbalanced multi-category classification.