[1]王新峰,漆梦玲,徐洪智.基于心电图的心肌梗死识别分类研究[J].中国医学物理学杂志,2022,39(8):992-997.[doi:DOI:10.3969/j.issn.1005-202X.2022.08.013]
 WANG Xinfeng,QI Mengling,et al.ECG-based identification and classification of myocardial infarction[J].Chinese Journal of Medical Physics,2022,39(8):992-997.[doi:DOI:10.3969/j.issn.1005-202X.2022.08.013]
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基于心电图的心肌梗死识别分类研究()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
39卷
期数:
2022年第8期
页码:
992-997
栏目:
医学信号处理与医学仪器
出版日期:
2022-08-04

文章信息/Info

Title:
ECG-based identification and classification of myocardial infarction
文章编号:
1005-202X(2022)08-0992-06
作者:
王新峰12漆梦玲3徐洪智1
1.吉首大学软件学院, 湖南 吉首 416000; 2.中山大学计算机学院, 广东 广州 510275; 3.中山大学孙逸仙纪念医院医学研究中心, 广东 广州 510120
Author(s):
WANG Xinfeng1 2 QI Mengling3 XU Hongzhi1
1. Software College, Jishou University, Jishou 416000, China2. School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510275, China 3. Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
关键词:
心肌梗死心电图机器学习深度学习可解释性
Keywords:
Keywords: myocardial infarction electrocardiogram machine learning deep learning interpretability
分类号:
R318;TP391.4
DOI:
DOI:10.3969/j.issn.1005-202X.2022.08.013
文献标志码:
A
摘要:
心肌梗死(MI)是一种严重的心脏病,症状前的健康检查可以发现早期的MI。心电图(ECG)是一种常用的无创健康检查诊断工具。一些使用ECG预测MI的研究存在基于私人数据集、样本量小、分析方法简单等不足。为了解决这些问题,本研究提出在英国最大的开放采集生物信息资源平台UK Biobank上进行MI的首次基准预测实验,涵盖基于临床特征的机器学习方法和基于ECG信号的深度学习方法。结果显示,基于临床特征的AUC为0.690,深度学习使用原始ECG信号的AUC为0.728,提升近4%。证明深度学习基于原始ECG信号能学习到比临床特征更多的信息。另外,对XGBoost和ResNet方法的结果进行了初步的可解释性分析,发现ST波与MI的关联更密切。
Abstract:
Abstract: Myocardial infarction (MI) is a kind of severe heart diseases. Pre-symptomatic health examination can detect MI diseases at early stage. Electrocardiogram (ECG) is a common-used non-invasive health examination and diagnostic tool. Some studies that use ECG to predict MI have some shortcomings such as private data sets-based, small sample sizes, simple analysis methods. To solve these issues, the first benchmark prediction experiment of MI on the available collection of biological information resource platform UK Biobank is proposed, which covers machine learning methods based on clinical features and deep learning methods based on ECG signals. The results show that the AUC based on clinical characteristics is 0.690, and the AUC of deep learning using the original ECG signals is 0.728, which is an increase of nearly 4%. It is proved that deep learning based on the original ECG can learn more information than clinical features. In addition, a preliminary interpretability analysis is performed on the results obtained by XGBoost and ResNet methods, and it is found that ST wave is more closely related to MI.

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备注/Memo

备注/Memo:
【收稿日期】2021-12-10 【基金项目】国家自然科学基金(62062036) 【作者简介】王新峰,博士,讲师,研究方向:深度学习、多基因组学与生物信号处理,E-mail: wangxf59@mail2.sysu.edu.cn
更新日期/Last Update: 2022-09-05