Prediction of Atrial Fibrillation from Sinus-Rhythm Electrocardiograms Based on Deep Neural Networks: Analysis of Time Intervals and Longitudinal Study

IF 5.6 4区 医学 Q1 ENGINEERING, BIOMEDICAL Irbm Pub Date : 2023-10-27 DOI:10.1016/j.irbm.2023.100811
Pietro Melzi , Ruben Vera-Rodriguez , Ruben Tolosana , Ancor Sanz-Garcia , Alberto Cecconi , Guillermo J. Ortega , Luis Jesus Jimenez-Borreguero
{"title":"Prediction of Atrial Fibrillation from Sinus-Rhythm Electrocardiograms Based on Deep Neural Networks: Analysis of Time Intervals and Longitudinal Study","authors":"Pietro Melzi ,&nbsp;Ruben Vera-Rodriguez ,&nbsp;Ruben Tolosana ,&nbsp;Ancor Sanz-Garcia ,&nbsp;Alberto Cecconi ,&nbsp;Guillermo J. Ortega ,&nbsp;Luis Jesus Jimenez-Borreguero","doi":"10.1016/j.irbm.2023.100811","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><p>Artificial Intelligence (AI) in electrocardiogram (ECG) analysis helps to identify persons at risk of developing atrial fibrillation (AF) and reduces the risk for severe complications. Our aim is to investigate the performance of AI-based methods predicting future AF from sinus rhythm (SR) ECGs, according to different characteristics of patients, time intervals for prediction, and longitudinal measures.</p></div><div><h3>Methods</h3><p>We designed a retrospective, prognostic study to predict AF occurrence in patients from 12-lead SR ECGs. We classified patients in two groups, according to their ECGs: 3,761 developed AF and 22,896 presented only SR ECGs. We assessed the impact of age on the overall performance of deep neural network (DNN)-based systems, which consist in a variation of Residual Networks for time series. Then, we analysed how much in advance our system can predict AF from SR ECGs and the performance for different categories of patients with AUC and other metrics.</p></div><div><h3>Results</h3><p>After balancing the age distribution between the two groups of patients, our model achieves AUC of 0.79 (0.72-0.86) without additional constraints, 0.83 (0.76-0.89) for ECGs recorded in the last six months before AF, and 0.87 (0.81-0.93) for patients with stable AF risk measures over time, with sensitivity of 90.62% (80.70-96.48) and diagnostic odd ratio of 20.49 (8.56-49.09).</p></div><div><h3>Conclusion</h3><p>This study shows the ability of DNNs to predict new onsets of AF from SR ECGs, with the best performance achieved for patients with stable AF risk score over time. The introduction of this time-based score opens new possibilities for AF prediction, thanks to the analysis of long-span time intervals and score stability.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"44 6","pages":"Article 100811"},"PeriodicalIF":5.6000,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S195903182300060X/pdfft?md5=029d208308cda42d40c652bd0a384bbe&pid=1-s2.0-S195903182300060X-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Irbm","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S195903182300060X","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0

Abstract

Objective

Artificial Intelligence (AI) in electrocardiogram (ECG) analysis helps to identify persons at risk of developing atrial fibrillation (AF) and reduces the risk for severe complications. Our aim is to investigate the performance of AI-based methods predicting future AF from sinus rhythm (SR) ECGs, according to different characteristics of patients, time intervals for prediction, and longitudinal measures.

Methods

We designed a retrospective, prognostic study to predict AF occurrence in patients from 12-lead SR ECGs. We classified patients in two groups, according to their ECGs: 3,761 developed AF and 22,896 presented only SR ECGs. We assessed the impact of age on the overall performance of deep neural network (DNN)-based systems, which consist in a variation of Residual Networks for time series. Then, we analysed how much in advance our system can predict AF from SR ECGs and the performance for different categories of patients with AUC and other metrics.

Results

After balancing the age distribution between the two groups of patients, our model achieves AUC of 0.79 (0.72-0.86) without additional constraints, 0.83 (0.76-0.89) for ECGs recorded in the last six months before AF, and 0.87 (0.81-0.93) for patients with stable AF risk measures over time, with sensitivity of 90.62% (80.70-96.48) and diagnostic odd ratio of 20.49 (8.56-49.09).

Conclusion

This study shows the ability of DNNs to predict new onsets of AF from SR ECGs, with the best performance achieved for patients with stable AF risk score over time. The introduction of this time-based score opens new possibilities for AF prediction, thanks to the analysis of long-span time intervals and score stability.

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
基于深度神经网络的窦性心律心电图房颤预测:时间间隔分析和纵向研究
目的:人工智能(AI)在心电图(ECG)分析中有助于识别有发生心房颤动(AF)风险的人,并降低严重并发症的风险。我们的目的是根据不同的患者特征、预测时间间隔和纵向测量,研究基于人工智能的方法从窦性心律(SR)心电图预测未来房颤的性能。方法我们设计了一项回顾性预后研究,通过12导联SR心电图预测患者房颤的发生。我们根据心电图将患者分为两组:3761例发生房颤,22896例仅出现SR心电图。我们评估了年龄对基于深度神经网络(DNN)的系统的整体性能的影响,该系统由时间序列的残差网络的变化组成。然后,我们分析了我们的系统可以提前多少时间从SR心电图预测房颤,以及不同类别AUC患者的表现和其他指标。结果在平衡两组患者的年龄分布后,我们的模型在没有额外约束的情况下获得了0.79(0.72-0.86)的AUC,对于房颤前6个月记录的心电图为0.83(0.76-0.89),对于长期稳定房颤风险测量的患者为0.87(0.81-0.93),敏感性为90.62%(80.70-96.48),诊断奇比为20.49(8.56-49.09)。结论:本研究显示dnn能够通过SR心电图预测AF的新发发病,对于长期稳定的AF风险评分患者表现最佳。由于对长时间间隔和评分稳定性的分析,这种基于时间的评分的引入为AF预测开辟了新的可能性。
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
Irbm
Irbm ENGINEERING, BIOMEDICAL-
CiteScore
10.30
自引率
4.20%
发文量
81
审稿时长
57 days
期刊介绍: IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux). As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in: -Physiological and Biological Signal processing (EEG, MEG, ECG…)- Medical Image processing- Biomechanics- Biomaterials- Medical Physics- Biophysics- Physiological and Biological Sensors- Information technologies in healthcare- Disability research- Computational physiology- …
期刊最新文献
Editorial Board Contents Potential of Near-Infrared Optical Techniques for Non-invasive Blood Glucose Measurement: A Pilot Study Corrigendum to “Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models” [IRBM (2023) 100725] Comprehensive Review of Feature Extraction Techniques for sEMG Signal Classification: From Handcrafted Features to Deep Learning Approaches
×
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