Premature Ventricular Contractions' Detection Based on Active Learning
Premature Ventricular Contractions' Detection Based on Active Learning
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DOI：
10.1155/2021/5556011
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Xianrong Zhang;M. Shafiq;Guijun Zheng;Junping Wan;Zhe Sun
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文献类型：
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作者：
Xianrong Zhang;M. Shafiq;Guijun Zheng;Junping Wan;Zhe Sun
室性早搏（室性早搏）是最常见的高危心血管疾病之一。已有研究表明，监督学习算法可以从心电数据中检测出室性早搏。然而，为了达到准确的检测率，需要大量的人力投入。在此工作中，以无监督的方式训练卷积自编码器，以零先验专业知识自动提取特征。采用随机森林作为监督算法，对自编码器生成的特征进行训练。在随机森林的基础上，研究了基于不确定性和基于多样性的主动学习选择策略。在主动学习的每次迭代中，训练数据被新选择的样本更新并输入到分类器中。在每次迭代中记录独立验证集上的性能。结果表明，在不同的不确定性采样策略中，最小置信度得分的F1得分为0.85，优于其他方法。在两种基于多样性的策略之间，具有代表性的聚类样本比k-center-greedy算法具有最好的F1得分。通过对比在原始数据大小的一半上训练的不同主动学习方法与在完整集上训练的相同分类器的性能，最小置信度的F1分数仍然优于完整集。这项研究表明，主动学习可以通过达到与在完全注释的训练数据上训练的分类器相同的性能水平来帮助减少人类标注的工作量。
Premature ventricular contractions (PVCs) are one of the most common cardiovascular diseases with high risk to a large population of patients. It has been shown that supervised learning algorithms can detect PVCs from beat-level ECG data. However, a huge human effort is needed in order to achieve an accurate detection rate. A convolutional autoencoder was trained in this work in an unsupervised fashion to extract features automatically with zero prior specialized knowledge. Random forest was adopted as a supervised algorithm trained on the features generated by the autoencoder. Various active learning selection strategies, uncertainty-based and diversity-based, were studied on top of the random forest. In each iteration of active learning, the training data are updated with newly selected samples and fed into the classifier. The performance on an independent validation set is recorded in each iteration. As a result, among the different uncertainty sampling strategies, the least confidence score shows a better F1 score of 0.85 than other methods. In between the two diversity-based strategies, the representative clustering sample had the best F1 score than the k-center-greedy algorithm. By comparing the performance of different active learning methods trained on half of the original data size with the same classifier trained on the full set, the F1 score of least confidence is still better than the full set. This study demonstrates that active learning could help reduce human annotation effort by achieving the same level of performance as the classifier trained on the fully annotated training data.