Detection of congestive heart failure from RR intervals during long-term electrocardiographic recordings

IF 2.5 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS
Teemu Pukkila MSc , Matti Molkkari MSc , Jussi Hernesniemi MD, PhD , Matias Kanniainen MSc , Esa Räsänen PhD
{"title":"Detection of congestive heart failure from RR intervals during long-term electrocardiographic recordings","authors":"Teemu Pukkila MSc ,&nbsp;Matti Molkkari MSc ,&nbsp;Jussi Hernesniemi MD, PhD ,&nbsp;Matias Kanniainen MSc ,&nbsp;Esa Räsänen PhD","doi":"10.1016/j.hroo.2025.01.014","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Timely detection is crucial for managing cardiovascular diseases. Recently developed computational tools to analyze RR interval (RRI) sequences offer cost-effective means for early cardiac screening and monitoring with consumer-grade heart rate devices.</div></div><div><h3>Objective</h3><div>The purpose of this study was to demonstrate detection of congestive heart failure (CHF) from RRIs by discriminating CHF from both healthy controls and patients with present atrial fibrillation (AF). We also examined the detection’s consistency regarding CHF severity and AF episode frequency.</div></div><div><h3>Methods</h3><div>We analyzed RRIs extracted from several datasets of long-term electrocardiographic (ECG) recordings. We use detrended fluctuation analysis (DFA) to evaluate the correlations of RRI, that is, how changes in the RRIs affect changes at another time. Furthermore, we utilized dynamical detrended fluctuation analysis (DDFA), which provides further insights into how the correlations change over time and different time scales. The resulting (D)DFA scaling exponents are used as features in classification, distinguishing CHF, AF, and healthy controls using the XGboost ensemble learning technique.</div></div><div><h3>Results</h3><div>Our (D)DFA computations revealed distinct RRI characteristics for CHF and AF patients during long-term ECG recordings, aiding disease detection. The DDFA-based classification pipeline detects CHF/AF from healthy controls with 90% sensitivity and 92% specificity. The 3-class classification algorithm correctly detects 78% of AF cases, 78% of CHF cases, and 91% of healthy cases. The DDFA results show consistency regarding CHF severity and AF episode frequency.</div></div><div><h3>Conclusion</h3><div>We achieved high confidence in detecting CHF, with DDFA showing excellent classification accuracy, especially in multiclass tasks. This approach highlights the potential of noninvasive, cost-efficient RRI analysis for early detection of CHF and AF.</div></div>","PeriodicalId":29772,"journal":{"name":"Heart Rhythm O2","volume":"6 4","pages":"Pages 509-518"},"PeriodicalIF":2.5000,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Heart Rhythm O2","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666501825000236","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

Background

Timely detection is crucial for managing cardiovascular diseases. Recently developed computational tools to analyze RR interval (RRI) sequences offer cost-effective means for early cardiac screening and monitoring with consumer-grade heart rate devices.

Objective

The purpose of this study was to demonstrate detection of congestive heart failure (CHF) from RRIs by discriminating CHF from both healthy controls and patients with present atrial fibrillation (AF). We also examined the detection’s consistency regarding CHF severity and AF episode frequency.

Methods

We analyzed RRIs extracted from several datasets of long-term electrocardiographic (ECG) recordings. We use detrended fluctuation analysis (DFA) to evaluate the correlations of RRI, that is, how changes in the RRIs affect changes at another time. Furthermore, we utilized dynamical detrended fluctuation analysis (DDFA), which provides further insights into how the correlations change over time and different time scales. The resulting (D)DFA scaling exponents are used as features in classification, distinguishing CHF, AF, and healthy controls using the XGboost ensemble learning technique.

Results

Our (D)DFA computations revealed distinct RRI characteristics for CHF and AF patients during long-term ECG recordings, aiding disease detection. The DDFA-based classification pipeline detects CHF/AF from healthy controls with 90% sensitivity and 92% specificity. The 3-class classification algorithm correctly detects 78% of AF cases, 78% of CHF cases, and 91% of healthy cases. The DDFA results show consistency regarding CHF severity and AF episode frequency.

Conclusion

We achieved high confidence in detecting CHF, with DDFA showing excellent classification accuracy, especially in multiclass tasks. This approach highlights the potential of noninvasive, cost-efficient RRI analysis for early detection of CHF and AF.
从长期心电图记录的RR间隔检测充血性心力衰竭
及时发现心血管疾病对心血管疾病的管理至关重要。最近开发的计算工具用于分析RR间期(RRI)序列,为消费者级心率设备的早期心脏筛查和监测提供了经济有效的手段。目的本研究的目的是通过区分健康对照者和心房颤动(AF)患者的充血性心力衰竭(CHF),从RRIs中检测出CHF。我们还检查了检测在CHF严重程度和AF发作频率方面的一致性。方法对从多个长期心电图记录数据中提取的RRIs进行分析。我们使用去趋势波动分析(DFA)来评估RRI的相关性,即RRI的变化如何影响另一个时间的变化。此外,我们利用动态去趋势波动分析(DDFA),进一步了解相关性如何随时间和不同时间尺度变化。使用XGboost集成学习技术,将得到的(D)DFA缩放指数作为分类的特征,区分CHF、AF和健康对照组。结果我们的(D)DFA计算显示CHF和AF患者在长期心电图记录中具有明显的RRI特征,有助于疾病检测。基于ddfa的分类管道从健康对照中检测CHF/AF,灵敏度为90%,特异性为92%。3类分类算法正确检测出78%的AF病例、78%的CHF病例和91%的健康病例。DDFA结果显示CHF严重程度和AF发作频率的一致性。结论DDFA对CHF的检测具有较高的置信度,尤其在多类任务中具有较好的分类精度。该方法强调了无创、成本效益高的RRI分析在早期检测CHF和AF方面的潜力。
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 求助全文
来源期刊
Heart Rhythm O2
Heart Rhythm O2 Cardiology and Cardiovascular Medicine
CiteScore
3.30
自引率
0.00%
发文量
0
审稿时长
52 days
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信