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《南方医科大学学报》[ISSN:1673-4254/CN:44-1627/R]

卷:

期数:

2019年09期

页码:

1071

栏目:

出版日期:

2019-09-15

文章信息/Info

Title:

Heartbeat-based end-to-end classification of arrhythmias

作者:

邓力; 傅蓉

关键词:

心律失常; 分类; 深度学习; 卷积神经网络

Keywords:

arrhythmia;  classification;  deep learning;  convolution neural network

DOI:

10.12122/j.issn.1673-4254.2019.09.11

摘要:

目的提出一种端到端的心律失常分类方法，以提高计算机对室上性异位心搏（SVEB）和室性异位心搏（VEB）的分类性 能。方法首先对心电信号进行心拍分割、校正等预处理；然后通过卷积神经网络构建心律失常分类网络，最后结合新的损失函 数训练分类器模型。结果利用MIT-BIH心律失常数据集验证本文分类方法的性能，其中在SVEB和VEB上的AUC分别达到 了0.77和0.98。在引入前5 min片段作为局部数据的情况下，SVEB和VEB的灵敏度分别达到了78.28%和98.88%；而在引入0、 50、100、150个样本作为局部数据时，SVEB和VEB的灵敏度最高分别达到82.25%和93.23%。结论本文提出的方法与现有的 方法相比，有效改善了样本类别不平衡带来的消极影响，SVEB和VEB灵敏度均有一定程度的提升，为心律失常的自动分类提 供了新的技术方案。

Abstract:

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. Conclusion 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.

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