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Imbalanced ECG data classification using a novel model based on active training subset selection and modified broad learning system
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DOI：10.1016/j.measurement.2022.111412.png)
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摘要
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This paper classifies non-ectopic (N), supraventricular ectopic (S), ventricular ectopic (V), and fusion (F) beats in the MIT-BIH arrhythmia database. The classification encounters serious class imbalance since the number of beats in N (majority class) with sample number above the average per class is heavily outnumbered than that in S, V, and F (minority classes) with sample number below the average per class. To address the class imbalance, a novel model based on active training subset selection and modified broad learning system (MBLS) is proposed. In each iteration, the MBLS trained with the current training subset is used to predict the class label of the test sample and actively select a new training subset for the next iteration. Finally, the class of the test sample is determined by voting on the predictions of all iterations. The experimental results show that our method has excellent performance and outperforms the existing methods.
Keyword:
ECG arrhythmia classification
Class imbalance
Active training subset selection
Modified broad learning system
Voting methods
期刊
IF:
5.6
论文数:
2.0W
被引数:
5.4W
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引用论文
Heart Disease and Stroke Statistics-2020 Update: A Report From the American Heart Association心脏病和中风统计-2020更新: 来自美国心脏协会的报告.png)
CIRCULATION
OAAI
CIRCULATIONIF38.6
OAAI分享收藏Classification of imbalanced ECG beats using re-sampling techniques and AdaBoost ensemble classifier
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