Deep Learning for Personalized Electrocardiogram Diagnosis: A Review

Cheng Ding, Tianliang Yao, Chenwei Wu, Jianyuan Ni
{"title":"Deep Learning for Personalized Electrocardiogram Diagnosis: A Review","authors":"Cheng Ding, Tianliang Yao, Chenwei Wu, Jianyuan Ni","doi":"arxiv-2409.07975","DOIUrl":null,"url":null,"abstract":"The electrocardiogram (ECG) remains a fundamental tool in cardiac\ndiagnostics, yet its interpretation traditionally reliant on the expertise of\ncardiologists. The emergence of deep learning has heralded a revolutionary era\nin medical data analysis, particularly in the domain of ECG diagnostics.\nHowever, inter-patient variability prohibit the generalibility of ECG-AI model\ntrained on a population dataset, hence degrade the performance of ECG-AI on\nspecific patient or patient group. Many studies have address this challenge\nusing different deep learning technologies. This comprehensive review\nsystematically synthesizes research from a wide range of studies to provide an\nin-depth examination of cutting-edge deep-learning techniques in personalized\nECG diagnosis. The review outlines a rigorous methodology for the selection of\npertinent scholarly articles and offers a comprehensive overview of deep\nlearning approaches applied to personalized ECG diagnostics. Moreover, the\nchallenges these methods encounter are investigated, along with future research\ndirections, culminating in insights into how the integration of deep learning\ncan transform personalized ECG diagnosis and enhance cardiac care. By\nemphasizing both the strengths and limitations of current methodologies, this\nreview underscores the immense potential of deep learning to refine and\nredefine ECG analysis in clinical practice, paving the way for more accurate,\nefficient, and personalized cardiac diagnostics.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - EE - Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.07975","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The electrocardiogram (ECG) remains a fundamental tool in cardiac diagnostics, yet its interpretation traditionally reliant on the expertise of cardiologists. The emergence of deep learning has heralded a revolutionary era in medical data analysis, particularly in the domain of ECG diagnostics. However, inter-patient variability prohibit the generalibility of ECG-AI model trained on a population dataset, hence degrade the performance of ECG-AI on specific patient or patient group. Many studies have address this challenge using different deep learning technologies. This comprehensive review systematically synthesizes research from a wide range of studies to provide an in-depth examination of cutting-edge deep-learning techniques in personalized ECG diagnosis. The review outlines a rigorous methodology for the selection of pertinent scholarly articles and offers a comprehensive overview of deep learning approaches applied to personalized ECG diagnostics. Moreover, the challenges these methods encounter are investigated, along with future research directions, culminating in insights into how the integration of deep learning can transform personalized ECG diagnosis and enhance cardiac care. By emphasizing both the strengths and limitations of current methodologies, this review underscores the immense potential of deep learning to refine and redefine ECG analysis in clinical practice, paving the way for more accurate, efficient, and personalized cardiac diagnostics.
用于个性化心电图诊断的深度学习:综述
心电图(ECG)仍然是心脏诊断的基本工具,但其解读传统上依赖于心内科医生的专业知识。深度学习的出现预示着医疗数据分析领域的一场革命,尤其是在心电图诊断领域。然而,患者间的差异性使得在群体数据集上训练的心电图人工智能模型无法通用,从而降低了心电图人工智能在特定患者或患者群体上的性能。许多研究利用不同的深度学习技术来应对这一挑战。这篇综合性综述系统地综合了大量研究,深入探讨了个性化心电图诊断中的前沿深度学习技术。综述概述了选择相关学术文章的严格方法,并全面概述了应用于个性化心电图诊断的深度学习方法。此外,还研究了这些方法遇到的挑战以及未来的研究方向,最终深入探讨了深度学习的整合如何改变个性化心电图诊断并提高心脏护理水平。通过强调当前方法的优势和局限性,这篇综述强调了深度学习在完善和重新定义临床实践中的心电图分析方面的巨大潜力,为更准确、高效和个性化的心脏诊断铺平了道路。
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
自引率
0.00%
发文量
0
×
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