[1]张明瑞,万相奎,陈瑞,等.心电信号的房颤自动识别算法[J].华侨大学学报(自然科学版),2021,42(5):670-675.[doi:10.11830/ISSN.1000-5013.202007026]
　ZHANG Mingrui,WAN Xiangkui,CHEN Rui,et al.Automatic Recognition Algorithm of Atrial Fibrillation Based on ECG[J].Journal of Huaqiao University(Natural Science),2021,42(5):670-675.[doi:10.11830/ISSN.1000-5013.202007026]
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心电信号的房颤自动识别算法()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第42卷
期数:
2021年第5期
页码:
670-675
栏目:
出版日期:
2021-09-20

文章信息/Info

Title:
Automatic Recognition Algorithm of Atrial Fibrillation Based on ECG
文章编号:
1000-5013(2021)05-0670-06
作者:
张明瑞;  万相奎;  陈瑞;  刘俊杰;  朱彬如
湖北工业大学 太阳能高效利用及储能运行控制湖北省重点实验室, 湖北 武汉 430068
Author(s):
ZHANG Mingrui;  WAN Xiangkui;  CHEN Rui;  LIU Junjie;  ZHU Binru
Key Laboratory of High Efficiency Utilization and Energy Storage Operation Control of Hubei Province, Hubei University of Technology, Wuhan 430068, China
关键词:
心电信号;  房颤;  一阶差分阈值;  二阶差分阈值;  指数移动平均
Keywords:
electrocardiosignal;  atrial fibrillation;  first-order differential threshold;  second-order differential threshold;  exponential moving average
分类号:
TP391.5
DOI:
10.11830/ISSN.1000-5013.202007026
文献标志码:
A
摘要:
为提高房颤识别的准确率和效率,提出基于心电信号的房颤自动识别算法.首先,采用二阶差分阈值法对R波进行初筛;然后,引入指数移动平均的方法动态验证初选出的R波,通过整体上移心电图、结合一阶差分阈值和实时调整窗口时间的方法解决影响R波准确率的问题;最后,基于临床经验,提出3条判别规则,从而筛选出相应的房颤片段.实验结果表明:文中算法的阴性预测率和特异性分别可达99.7%和99.8%,可有效帮助医生减少阅读量,提高工作效率和诊断准确率.
Abstract:
In order to improve the accuracy and efficiency of atrial fibrillation recognition, an automatic atrial fibrillation algorithm based on an electrocardiosignal is proposed. Firstly, the second-order difference threshold method is used to preliminary screening the R-wave. Then, the method of exponential moving average is introduced to verify the initial R-wave dynamically. The problem affecting the accuracy of R-wave is solved by moving the electrocardiograph(ECG)as a whole, combining the first-order differential threshold and adjusting the real-time window. Finally, based on clinical experience, three criteria are proposed to screen out the corresponding atrial fibrillation fragments. The experimental results show that the negative predictive rate and specificity of the proposed algorithm can reach 99.7% and 99.8% respectively, which can effectively help doctors reduce the amount of reading, improve work efficiency and diagnostic accuracy.

参考文献/References:

[1] 别立展,赵丹丹.心房颤动的流行病学研究现状及进展[J].现代生物医学进展,2015,15(13):2562-2568.DOI:10.13241/j.cnki.pmb.2015.13.044.
[2] 常爱娟.2014年度心房颤动病人发病及治疗情况调查研究[D].北京:北京中医药大学,2016.
[3] 刘明,万慧华,龚硕然,等.基于RR间期差的深神经网络房颤检测[J].激光杂志,2015(1):90-93.DOI:10.14016/j.cnki.jgzz.2015.01.090.
[4] 孟丹阳,戴敏.基于时域特征的房颤识别方法[J].天津理工大学学报,2019,35(4):29-33.DOI:10.3969/j.issn.1673-095X.2019.04.007.
[5] WANG Xiaojie,XIA Yunlong,HAN Xu,et al.GW28-e0546 QT measurement in the presence of atrial fibrillation: Minimizing the effects of RR fluctuationson QT interval[J].Journal of the American College of Cardiology,2017,70(16):118-125.DOI:10.1016/j.jacc.2017.07.419.
[6] 钟高艳,陆宏伟,谷雪莲,等.基于体表心电图的房颤自动识别算法综述[J].北京生物医学工程,2018,37(5):539-550.DOI:10.3969/j.issn.1002-3208.2018.05.016.
[7] 崔永华,梁正友.一种基于特征提取和多模板匹配的心律失常检测算法[J].广西科学院学报,2012,28(1):7-10.DOI:10.3969/j.issn.1002-7378.2012.01.004.
[8] 李坤阳,胡广书.基于心电图分析的心率失常分类[J].清华大学学报(自然科学版),2009,49(3):418-421.
[9] YEH Y C,WANG W J,CHIOU C W.Cardiac arrhythmia diagnosis method using lineardiscriminant analysis on ECG signals[J].Measurement,2009,42(5):778-789.DOI:10.1016/j.measurement.2009.01.004.
[10] 王海英,王远远,姚之琳.心电信号QRS波检测算法的设计与仿真[J].哈尔滨理工大学学报,2019,24(4):84-88.DOI:10.15938/j.jhust.2019.04.014.
[11] 许迎新.基于MATLAB的房颤自动识别[D].南京:东南大学,2012.
[12] HAYNES D,CORNS S,VENAYAGAMOORTHY G K.An exponential moving average algorithm[C]//IEEE Congress on Evolutionary Computation.Brisbane:IEEE Press,2012:1-8.DOI:10.1109/CEC.2012.6252962.
[13] LI Yanjun,TANG Xiaoying,WANG Ancong,et al.Probability density distribution of delta RR intervals: A novel method for the detection of atrial fibrillation[J].Australasian Physical and Engineering Sciences in Medicine,2017,40(3):707-716.DOI:10.1007/s13246-017-0554-2.
[14] 张鑫.基于心电图的房颤识别研究[D].秦皇岛:燕山大学,2018.
[15] 刘明,万慧华,龚硕然,等.基于RR间期差的深神经网络房颤检测[J].激光杂志,2015,36(1):90-93.DOI:10.14016/j.cnki.jgzz.2015.01.090.
[16] 王凯,杨枢,李超.一种基于ECG的多层共轭对称Hadamard特征变换的房颤异常信号分类方法[J].中国医学物理学杂志,2019,36(9):1068-1073.DOI:10.3969/j.issn.1005-202X.2019.09.014.
[17] 黄超.动态心电图中房颤自动检测算法研究及其临床应用[D].杭州:浙江大学,2013.
[18] 赵文哲,方滨,沈毅,等.心电信号中R波检测方法的比较研究[J].生物医学工程学杂志,2009,26(1):55-58.
[19] TATENO K,CLASS L.Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of RR and ΔRR intervals[J].Medical and Biological Engineering and Computing,2001,39(6):664-671.DOI:10.1007/BF02345439.
[20] TATENO K,CLASS L.Method for detection of atrial fibrillation using RR intervals[J].Computers in Cardiology 2000,27:391-394,858.DOI:10.1109/CIC.2000.898539.
　　

备注/Memo

备注/Memo:
收稿日期: 2020-07-20
通信作者: 万相奎(1976-),男,教授,博士,主要从事生物医学信号处理的研究.E-mail:xkwan@hbut.edu.cn.
基金项目: 国家自然科学基金资助项目(61571182)
更新日期/Last Update: 2021-09-20