[基于心跳的心律失常端到端分类]
[Heartbeat-based end-to-end classification of arrhythmias].
作者信息
Deng Li, Fu Rong
机构信息
School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
出版信息
Nan Fang Yi Ke Da Xue Xue Bao. 2019 Sep 30;39(9):1071-1077. doi: 10.12122/j.issn.1673-4254.2019.09.11.
OBJECTIVE
We propose a heartbeat-based end-to-end classification of arrhythmias to improve the classification performance for supraventricular ectopic beat (SVEB) and ventricular ectopic beat (VEB).
METHODS
The ECG signals were preprocessed by heartbeat segmentation and heartbeat alignment. An arrhythmia classifier was constructed based on convolutional neural network, and the proposed loss function was used to train the classifier.
RESULTS
The proposed algorithm was verified on MIT-BIH arrhythmia database. The AUC of the proposed loss function for SVEB and VEB reached 0.77 and 0.98, respectively. With the first 5 min segment as the local data, the diagnostic sensitivities for SVEB and VEB were 78.28% and 98.88%, respectively; when 0, 50, 100, and 150 samples were used as the local data, the diagnostic sensitivities for SVEB and VEB reached 82.25% and 93.23%, respectively.
CONCLUSIONS
The proposed method effectively reduces the negative impact of class-imbalance and improves the diagnostic sensitivities for SVEB and VEB, and thus provides a new solution for automatic arrhythmia classification.
目的
我们提出一种基于心跳的心律失常端到端分类方法，以提高室上性异位搏动（SVEB）和室性异位搏动（VEB）的分类性能。
方法
通过心跳分割和心跳对齐对心电图信号进行预处理。基于卷积神经网络构建心律失常分类器，并使用所提出的损失函数训练该分类器。
结果
所提出的算法在MIT-BIH心律失常数据库上得到验证。所提出的损失函数对SVEB和VEB的AUC分别达到0.77和0.98。以前5分钟片段作为局部数据时，SVEB和VEB的诊断敏感性分别为78.28%和98.88%；当使用0、50、100和150个样本作为局部数据时，SVEB和VEB的诊断敏感性分别达到82.25%和93.23%。
结论
所提出的方法有效降低了类别不平衡的负面影响，提高了SVEB和VEB的诊断敏感性，从而为心律失常自动分类提供了一种新的解决方案。
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