[1]查雪帆,杨丰,吴俣南,等. 结合迁移学习与深度卷积网络的心电分类研究[J].中国医学物理学杂志,2018,35(11):1307-1312.[doi:DOI:10.3969/j.issn.1005-202X.2018.11.013]
　ZHA Xuefan,YANG Feng,WU Yunan,et al. ECG classification based on transfer learning and deep convolution neural network[J].Chinese Journal of Medical Physics,2018,35(11):1307-1312.[doi:DOI:10.3969/j.issn.1005-202X.2018.11.013]
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 结合迁移学习与深度卷积网络的心电分类研究()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
35卷
期数:
2018年第11期
页码:
1307-1312
栏目:
医学信号处理与医学仪器
出版日期:
2018-11-18

文章信息/Info

Title:
 ECG classification based on transfer learning and deep convolution neural network
文章编号:
1005-202X（2018）11-1307-06
作者:
 查雪帆1; 杨丰1; 2; 吴俣南1; 刘颖1; 袁绍锋1; 2
 1.南方医科大学生物医学工程学院， 广东 广州 510515； 2.南方医科大学广东省医学图像处理重点实验室， 广东 广州 510515
Author(s):
 ZHA Xuefan1;  YANG Feng1;  2;  WU Yu’nan1;  LIU Ying1;  YUAN Shaofeng1;  2
 1. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; 2. Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China
关键词:
心电节拍分类; 迁移学习; 深度学习; 二维深度卷积网络; 一维深度卷积网络; ImageNet数据集
Keywords:
 ; Keywords: electrocardiogram heartbeat classification;  transfer learning;  deep learning;  two-dimensional deep convolution neural network;  one-dimensional deep convolution neural network;  ImageNet dataset
分类号:
R318
DOI:
DOI:10.3969/j.issn.1005-202X.2018.11.013
文献标志码:
A
摘要:
 为解决一维深度卷积网络（1D-DCNN）在心电分类方面存在的多类疾病识别不准、难以提取最佳特征等问题，提出一种结合迁移学习与二维深度卷积网络（2D-DCNN）直接识别心电图像的方法。首先，截取R波前后75 ms内的心电信号，并将一维心电电压信号转化为二维灰度图像信号。接着，构建2D-DCNN对心电节拍样本进行分类训练，权值初始化采用在ImageNet大规模图像数据集上进行预训练的AlexNet参数值。本文提出方法在MIT-BIH心电数据库上进行性能验证，其准确率达到98%，并在不同信噪比下保持较高的准确率，证明了所述模型在心电分类上具有良好的鲁棒性。为了验证2D-DCNN的识别性能，实验部分与采用不同激活函数的1D-DCNN、近些年性能较好的深度学习方法进行比较。量化结果表明，结合迁移学习和2D-DCNN方法，比最优1D-DCNN算法，其准确率提升2%、敏感度提升0.6%、特异性提高4%；在二分类与多分类任务中，均好于现有的其他算法。
Abstract:
 Abstract: One-dimensional deep convolution neural networks (1D-DCNN) for electrocardiogram (ECG) classification shows limitations in identifying various diseases and extracting the best morphological features. Herein, a method combining transfer learning and two-dimensional deep convolution neural network (2D-DCNN), AlexNet, is proposed to identify ECG images directly. Firstly, the ECG signals within the 75 ms before and after R wave were intercepted, and one-dimensional ECG voltage signals were converted into two-dimensional grayscale image signals. Then, a 2D-DCNN based on AlexNet was established to classify the ECG heartbeat samples. The weights were initialized by parameters which were pre-trained on Alexnet using a large-scale dataset ImageNet. The proposed method achieved an accuracy of 98% on the MIT-BIH arrhythmia database, and maintained a high accuracy at different signal-to-noise ratios, which verified the good robustness of the proposed method in ECG classification. The proposed method was also compared with 1D-DCNN using different activation functions and other deep learning methods with favorable performances to evaluate the performance of 2D-DCNN. The quantitative results demonstrated that compared with the optimal 1D-DCNN, the proposed method combining transfer learning with 2D-DCNN improves the accuracy rate, sensitivity and specificity by 2%, 0.6% and 4%, respectively, and that the proposed algorithm is better than other existing algorithms in both binary/multi-class classification tasks.

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备注/Memo

备注/Memo:
 【收稿日期】2018-06-28
【基金项目】国家自然科学基金（61771233, 61271155）
【作者简介】查雪帆，研究方向：机器学习与医学图像处理，E-mail: xuefanzha.smu@gmail.com
【通信作者】杨丰，教授，博士生导师，研究方向：模式识别、机器学习、医学图像处理、医学信号处理，E-mail: yangf@smu.edu.cn
更新日期/Last Update: 2018-11-22