基于类激活图的切分-合并和对比学习:记录级心房颤动检测的新策略

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Expert Systems with Applications Pub Date : 2024-10-28 DOI:10.1016/j.eswa.2024.125619
Qiang Zhu, Lingwei Zhang, Fei Lu, Luping Fang, Qing Pan
{"title":"基于类激活图的切分-合并和对比学习:记录级心房颤动检测的新策略","authors":"Qiang Zhu,&nbsp;Lingwei Zhang,&nbsp;Fei Lu,&nbsp;Luping Fang,&nbsp;Qing Pan","doi":"10.1016/j.eswa.2024.125619","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Deep learning-based models for atrial fibrillation (AF) detection require extensive training data, which often necessitates labor-intensive professional annotation. While data augmentation techniques have been employed to mitigate the scarcity of annotated electrocardiogram (ECG) data, specific augmentation methods tailored for recording-level ECG annotations are lacking. This gap hampers the development of robust deep learning models for AF detection.</div></div><div><h3>Methods</h3><div>We propose a novel strategy, a combination of Class Activation Map-based Slicing-Concatenation (CAM-SC) data augmentation and contrastive learning, to address the current challenges. Initially, a baseline model incorporating a global average pooling layer is trained for classification and to generate class activation maps (CAMs), which highlight indicative ECG segments. After that, in each recording, indicative and non-indicative segments are sliced. These segments are subsequently concatenated randomly based on starting and ending Q points of QRS complexes, with indicative segments preserved to maintain label correctness. Finally, the augmented dataset undergoes contrastive learning to learn general representations, thereby enhancing AF detection performance.</div></div><div><h3>Results</h3><div>Using ResNet-101 as the baseline model, training with the augmented data yielded the highest F1-score of 0.861 on the Computing in Cardiology (CinC) Challenge 2017 dataset, a typical AF dataset with recording-level annotations. The metrics outperform most previous studies.</div></div><div><h3>Conclusions</h3><div>This study introduces an innovative data augmentation method specifically designed for recording-level ECG annotations, significantly enhancing AF detection using deep learning models. This approach has substantial implications for future AF detection research.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":null,"pages":null},"PeriodicalIF":7.5000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Class activation map-based slicing-concatenation and contrastive learning: A novel strategy for record-level atrial fibrillation detection\",\"authors\":\"Qiang Zhu,&nbsp;Lingwei Zhang,&nbsp;Fei Lu,&nbsp;Luping Fang,&nbsp;Qing Pan\",\"doi\":\"10.1016/j.eswa.2024.125619\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>Deep learning-based models for atrial fibrillation (AF) detection require extensive training data, which often necessitates labor-intensive professional annotation. While data augmentation techniques have been employed to mitigate the scarcity of annotated electrocardiogram (ECG) data, specific augmentation methods tailored for recording-level ECG annotations are lacking. This gap hampers the development of robust deep learning models for AF detection.</div></div><div><h3>Methods</h3><div>We propose a novel strategy, a combination of Class Activation Map-based Slicing-Concatenation (CAM-SC) data augmentation and contrastive learning, to address the current challenges. Initially, a baseline model incorporating a global average pooling layer is trained for classification and to generate class activation maps (CAMs), which highlight indicative ECG segments. After that, in each recording, indicative and non-indicative segments are sliced. These segments are subsequently concatenated randomly based on starting and ending Q points of QRS complexes, with indicative segments preserved to maintain label correctness. Finally, the augmented dataset undergoes contrastive learning to learn general representations, thereby enhancing AF detection performance.</div></div><div><h3>Results</h3><div>Using ResNet-101 as the baseline model, training with the augmented data yielded the highest F1-score of 0.861 on the Computing in Cardiology (CinC) Challenge 2017 dataset, a typical AF dataset with recording-level annotations. The metrics outperform most previous studies.</div></div><div><h3>Conclusions</h3><div>This study introduces an innovative data augmentation method specifically designed for recording-level ECG annotations, significantly enhancing AF detection using deep learning models. This approach has substantial implications for future AF detection research.</div></div>\",\"PeriodicalId\":50461,\"journal\":{\"name\":\"Expert Systems with Applications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.5000,\"publicationDate\":\"2024-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Expert Systems with Applications\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0957417424024862\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Expert Systems with Applications","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0957417424024862","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

背景基于深度学习的心房颤动(AF)检测模型需要大量的训练数据,而这往往需要劳动密集型的专业注释。虽然数据扩增技术已被用于缓解心电图(ECG)注释数据稀缺的问题,但目前还缺乏针对记录级心电图注释的特定扩增方法。我们提出了一种新颖的策略,将基于类激活图的切片-合并(CAM-SC)数据增强和对比学习相结合,以应对当前的挑战。首先,对包含全局平均汇集层的基线模型进行分类训练,并生成类激活图(CAM),突出指示性心电图片段。然后,在每个记录中,对指示性和非指示性片段进行切片。随后,根据 QRS 波群的起始和终止 Q 点随机连接这些片段,并保留指示性片段以保持标签的正确性。结果使用 ResNet-101 作为基线模型,在 2017 年心脏病学计算(CinC)挑战赛数据集(一个具有记录级注释的典型房颤数据集)上,使用增强数据进行训练获得了最高的 F1 分数 0.861。结论 本研究介绍了一种专门针对记录级心电图注释的创新数据增强方法,显著增强了使用深度学习模型的房颤检测能力。这种方法对未来的房颤检测研究具有重大意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Class activation map-based slicing-concatenation and contrastive learning: A novel strategy for record-level atrial fibrillation detection

Background

Deep learning-based models for atrial fibrillation (AF) detection require extensive training data, which often necessitates labor-intensive professional annotation. While data augmentation techniques have been employed to mitigate the scarcity of annotated electrocardiogram (ECG) data, specific augmentation methods tailored for recording-level ECG annotations are lacking. This gap hampers the development of robust deep learning models for AF detection.

Methods

We propose a novel strategy, a combination of Class Activation Map-based Slicing-Concatenation (CAM-SC) data augmentation and contrastive learning, to address the current challenges. Initially, a baseline model incorporating a global average pooling layer is trained for classification and to generate class activation maps (CAMs), which highlight indicative ECG segments. After that, in each recording, indicative and non-indicative segments are sliced. These segments are subsequently concatenated randomly based on starting and ending Q points of QRS complexes, with indicative segments preserved to maintain label correctness. Finally, the augmented dataset undergoes contrastive learning to learn general representations, thereby enhancing AF detection performance.

Results

Using ResNet-101 as the baseline model, training with the augmented data yielded the highest F1-score of 0.861 on the Computing in Cardiology (CinC) Challenge 2017 dataset, a typical AF dataset with recording-level annotations. The metrics outperform most previous studies.

Conclusions

This study introduces an innovative data augmentation method specifically designed for recording-level ECG annotations, significantly enhancing AF detection using deep learning models. This approach has substantial implications for future AF detection research.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Expert Systems with Applications
Expert Systems with Applications 工程技术-工程:电子与电气
CiteScore
13.80
自引率
10.60%
发文量
2045
审稿时长
8.7 months
期刊介绍: Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.
期刊最新文献
Research on the generation and evaluation of bridge defect datasets for underwater environments utilizing CycleGAN networks Hybrid genetic algorithm with Wiener process for multi-scale colored balanced traveling salesman problem CE-DCVSI: Multimodal relational extraction based on collaborative enhancement of dual-channel visual semantic information Learning face super-resolution through identity features and distilling facial prior knowledge Identification of gene regulatory networks associated with breast cancer patient survival using an interpretable deep neural network model
×
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