Reproducibility of Heart Rate Variability Characteristics Measured on Random 10-second ECG using Joint Symbolic Dynamics.

Computing in cardiology Pub Date : 2016-09-01 Epub Date: 2017-03-02 DOI:10.23919/CIC.2016.7868736
Muammar M Kabir, Golriz Sedaghat, Jason Thomas, Larisa G Tereshchenko
{"title":"Reproducibility of Heart Rate Variability Characteristics Measured on Random 10-second ECG using Joint Symbolic Dynamics.","authors":"Muammar M Kabir,&nbsp;Golriz Sedaghat,&nbsp;Jason Thomas,&nbsp;Larisa G Tereshchenko","doi":"10.23919/CIC.2016.7868736","DOIUrl":null,"url":null,"abstract":"<p><p>In this paper we employed joint symbolic dynamics (JSD) approach to study reproducibility of heart rate variability characteristics measured on 2 randomly selected 10-second segments within 3-minute resting orthogonal ECG in 170 healthy participants. First, the ECG R-peaks were detected using parabolic fitting. Second, the respiratory signal was derived from orthogonal ECG X-lead using QRS slopes. Third, time series of R-R intervals and respiratory phases (calculated using Hilbert transform), were transformed into tertiary symbol vectors based on their successive changes and words of length '3' were formed. Bland-Altman analysis was used to assess the agreement between measured log-transformed JSD characteristics of HRV, and their reproducibility. Traditional HRV measures such as RR' interval changes showed a very high reproducibility. However, agreement between two 10-second JSD indices of HRV was low. Interestingly, a significant decrease in low-high alterations of HRV dynamics measured using JSD was observed when respiratory phase transition intervals were excluded (10s: 4.7±9.4 vs. 24.8±21.0%, p<0.0001; 3min: 9.8±8.1 vs. 24.8±12.3%, p<0.0001).</p>","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":"2016 ","pages":"289-292"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5606201/pdf/nihms-849301.pdf","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computing in cardiology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/CIC.2016.7868736","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2017/3/2 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

In this paper we employed joint symbolic dynamics (JSD) approach to study reproducibility of heart rate variability characteristics measured on 2 randomly selected 10-second segments within 3-minute resting orthogonal ECG in 170 healthy participants. First, the ECG R-peaks were detected using parabolic fitting. Second, the respiratory signal was derived from orthogonal ECG X-lead using QRS slopes. Third, time series of R-R intervals and respiratory phases (calculated using Hilbert transform), were transformed into tertiary symbol vectors based on their successive changes and words of length '3' were formed. Bland-Altman analysis was used to assess the agreement between measured log-transformed JSD characteristics of HRV, and their reproducibility. Traditional HRV measures such as RR' interval changes showed a very high reproducibility. However, agreement between two 10-second JSD indices of HRV was low. Interestingly, a significant decrease in low-high alterations of HRV dynamics measured using JSD was observed when respiratory phase transition intervals were excluded (10s: 4.7±9.4 vs. 24.8±21.0%, p<0.0001; 3min: 9.8±8.1 vs. 24.8±12.3%, p<0.0001).

Abstract Image

Abstract Image

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
用关节符号动力学测量随机10秒心电图心率变异性特征的再现性。
在本文中,我们采用联合符号动力学(JSD)方法研究了170名健康受试者在3分钟静息正交心电图中随机选择的2个10秒段心率变异性特征的可重复性。首先,采用抛物线拟合检测心电图r峰。其次,利用QRS斜率提取正交心电图x导联的呼吸信号。第三,利用Hilbert变换计算R-R区间和呼吸相的时间序列,根据它们的连续变化将它们转换成三级符号向量,形成长度为“3”的词。采用Bland-Altman分析评估HRV对数变换JSD特征的一致性及其再现性。传统的HRV测量方法,如RR间隔变化,显示出非常高的可重复性。然而,两个10秒JSD HRV指标之间的一致性较低。有趣的是,当排除呼吸相转换间隔时,使用JSD测量的HRV动力学的高低变化显著减少(10秒:4.7±9.4比24.8±21.0%,p
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
1.10
自引率
0.00%
发文量
0
期刊最新文献
Transfer Learning for Improved Classification of Drivers in Atrial Fibrillation. Effects of Biventricular Pacing Locations on Anti-Tachycardia Pacing Success in a Patient-Specific Model. Deep Learning System for Left Ventricular Assist Device Candidate Assessment from Electrocardiograms. Capturing the Influence of Conduction Velocity on Epicardial Activation Patterns Using Uncertainty Quantification. Comparison of Machine Learning Detection of Low Left Ventricular Ejection Fraction Using Individual ECG Leads.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1