Release Time:2019-07-01  Hits:

Indexed by: Conference Paper

Date of Publication: 2018-01-01

Included Journals: CPCI-S

Page Number: 2753-2755

Abstract: 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.