[1]赖杰伟,陈韵岱,韩宝石,等.基于DenseNet的心电数据自动诊断算法[J].南方医科大学学报,2019,(01):69.[doi:10.12122/j.issn.1673-4254.2019.01.11]
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基于DenseNet的心电数据自动诊断算法()
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《南方医科大学学报》[ISSN:1673-4254/CN:44-1627/R]

卷:
期数:
2019年01期
页码:
69
栏目:
出版日期:
2019-01-26

文章信息/Info

Title:
A DenseNet-based diagnosis algorithm for automated diagnosis using clinical ECG data
作者:
赖杰伟陈韵岱韩宝石季磊石亚君黄志聪阳维冯前进
关键词:
心电预处理信号分帧深度可分离卷积密集连接型卷积网络
Keywords:
ECG data preprocessing signal framing depth-wise separable convolutions densely connected convolutional network
DOI:
10.12122/j.issn.1673-4254.2019.01.11
摘要:
目的使用卷积网络训练多导联心电图数据,并将新的心电数据准确地分类,为医生提供可靠的辅助诊断信息。方法先 用带通滤波器对数据进行预处理,使用信号分帧的方式调整不同长度的数据处于同样的大小,便于网络的训练测试;同时采用 增加样本的方法扩充数据整体,增加异常样本的检出率;针对不同导联的差异性使用深度可分离卷积更有针对性地提取不同通 道的特征。使用基于DenseNet的分类模型对多个标签分别训练二分类器,完成多标签分类任务。结果对数据的正异常识别 准确率可以达到80.13%,灵敏度,特异度和F1分别为80.38%,79.91%和79.35%。结论本文提出的模型能快速并有效地对心 电数据进行预测,在GPU上单个数据的运行时间约在33.59 ms,实时预测结果能满足应用需求。
Abstract:
Objective To train convolutional networks using multi-lead ECG data and classify new data accurately to provide reliable information for clinical diagnosis. Methods The data were pre-processed with a bandpass filter, and signal framing was adopted to adjust the data of different lengths to the same size to facilitate network training and prediction. The dataset was expanded by increasing the sample size to improve the detection rate of abnormal samples. A depth-wise separable convolution structure was used for more specific feature extraction for different channels of twelve-lead ECG data. We trained the two classifiers for each label using the improved DenseNet to classify different labels. Results The propose model showed an accuracy of 80.13% for distinguishing between normal and abnormal ECG with a sensitivity of 80.38%, a specificity of 79.91% and a F1 score of 79.35%. Conclusion The model proposed herein can rapidly and effectively classify the ECG data. The running time of a single dataset on GPU is 33.59 ms, which allows real-time prediction to meet the clinical requirements.
更新日期/Last Update: 1900-01-01