计算机辅助诊断模型助力基层医疗机构诊断阵发性心房颤动

doi:10.12114/j.issn.1007-9572.2020.00.552

中国全科医学 ›› 2021, Vol. 24 ›› Issue (2): 143-147.DOI: 10.12114/j.issn.1007-9572.2020.00.552

所属专题：心房颤动最新文章合集；心血管最新文章合集

计算机辅助诊断模型助力基层医疗机构诊断阵发性心房颤动

姚易1，廖晓阳1*，李志超2*

1.610041 四川省成都市，四川大学华西医院特需医疗中心全科医学病房　2.610041 四川省成都市，四川大学华西医院日间服务中心
*通信作者：廖晓阳，主任医师，教授； E-mail：625880796@qq.com　李志超，主治医师；E-mail：5348203@qq.com

出版日期:2021-01-15 发布日期:2021-01-15
基金资助:
四川省社会科学研究规划项目（18TJ031）；四川大学华西医院研究孵化项目（2018HXF005）；四川省科技计划项目软科学研究（2019JDR0277）；成都市医学科研课题（2019010）；四川省卫生厅（19PJ094）

Computer-aided Diagnosis Model Assists Paroxysmal Atrial Fibrillation Diagnosis in Primary Care　

YAO Yi1，LIAO Xiaoyang1*，LI Zhichao2*

1.International Medical Center，General Practice Unit，West China Hospital，Sichuan University，Chengdu 610041，China
2.Day Surgery Center，West China Hospital，Sichuan University，Chengdu 610041，China
*Corresponding authors：LIAO Xiaoyang，Chief physician，Professor；E-mail：625880796@qq.com
LI Zhichao，Attending physician；E-mail：5348203@qq.com

Published:2021-01-15 Online:2021-01-15

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摘要/Abstract

摘要： 心房颤动（简称房颤）是一种可导致多种严重并发症的心律失常。其中，阵发性房颤具有阵发性和无症状性特点，故难以诊断。长程心电所获得心电大数据可以提高阵发性房颤的检出率。但心电大数据的判读却成为基层医疗机构的负担和难题。为解决以上问题，多种基于心电特征的浅层学习模型不断出现，这些模型高度依赖人工提取特征，均有局限性。深度学习是一种数据驱动的自动特征学习算法，弥补浅层学习的不足。Lorenz散点图作为心电大数据快速分析的新兴方法，其输出的二维图形是深度学习的优质素材。本文综述房颤计算机辅助诊断模型的心电特征研究进展以及机器学习在房颤诊断中的应用现状，为辅助诊断模型构建提供新思路，同时为解决基层心电大数据的判读难题提供新视角。

关键词: 心房颤动；诊断, 计算机辅助；基层医疗机构；心电描记术；综述

Abstract: Atrial fibrillation is a common arrhythmia that can cause many serious complications.Among them，paroxysmal atrial fibrillation is difficult to diagnose due to paroxysmal and asymptomatic characteristics.Big ECG data obtained by long-term ECG can improve the detection rate of paroxysmal atrial fibrillation.However，the interpretation of ECG big data has become a burden and problem for primary medical institutions.To solve the problems，a variety of shallow learning models based on ECG features have been developed，which highly rely on manual feature extraction and have limitations.Deep learning is a data-driven automatic feature learning algorithm，which can make up for the shortcomings of shallow learning.As an emerging method for rapid analysis of ECG big data，the Lorenz scatterplots using two-dimensional graphs is high-quality materials for deep learning.This paper reviews the latest advances in ECG features of atrial fibrillation using computer-aided model for diagnosis，and the application of machine learning in atrial fibrillation diagnosis，providing new insights into the development of a good computer-aided diagnosis model，and a new perspective for the interpretation of ECG big data in primary care.

Key words: Atrial fibrillation；Diagnosis, computer-assisted；Grassroots medical institutions；Electrocardiography；Review

姚易,廖晓阳,李志超. 计算机辅助诊断模型助力基层医疗机构诊断阵发性心房颤动[J]. 中国全科医学, 2021, 24(2): 143-147. DOI: 10.12114/j.issn.1007-9572.2020.00.552.
YAO Yi,LIAO Xiaoyang,LI Zhichao. Computer-aided Diagnosis Model Assists Paroxysmal Atrial Fibrillation Diagnosis in Primary Care　[J]. Chinese General Practice, 2021, 24(2): 143-147. DOI: 10.12114/j.issn.1007-9572.2020.00.552.

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计算机辅助诊断模型助力基层医疗机构诊断阵发性心房颤动

Computer-aided Diagnosis Model Assists Paroxysmal Atrial Fibrillation Diagnosis in Primary Care

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