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Identifying the mislabeled training samples of ECG signals using machine learning

机译：使用机器学习识别贴错标签的心电图训练样本

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摘要

The classification accuracy of electrocardiogram signal is often affected by diverse factors in which mislabeled training samples issue is one of the most influential problems. In order to mitigate this negative effect, the method of cross validation is introduced to identify the mislabeled samples. The method utilizes the cooperative advantages of different classifiers to act as a filter for the training samples. The filter removes the mislabeled training samples and retains the correctly labeled ones with the help of 10-fold cross validation. Consequently, a new training set is provided to the final classifiers to acquire higher classification accuracies. Finally, we numerically show the effectiveness of the proposed method with the MIT-BIH arrhythmia database. (C) 2018 Elsevier Ltd. All rights reserved.
机译：心电图信号的分类准确性通常受多种因素的影响，其中错误标记的训练样本的发布是最有影响力的问题之一。为了减轻这种负面影响，引入了交叉验证方法以识别标记错误的样品。该方法利用不同分类器的协作优势来充当训练样本的过滤器。过滤器会删除错误标记的训练样本，并借助10倍交叉验证保留正确标记的训练样本。因此，将新的训练集提供给最终分类器，以获取更高的分类精度。最后，我们通过MIT-BIH心律失常数据库以数值方式显示了该方法的有效性。 （C）2018 Elsevier Ltd.保留所有权利。

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