基于图双谱法的心电心律失常分类

Yang Shiyilin, Shao Jie, Yang Xin, Chen Xin, Wang Xingxing
{"title":"基于图双谱法的心电心律失常分类","authors":"Yang Shiyilin, Shao Jie, Yang Xin, Chen Xin, Wang Xingxing","doi":"10.1109/ISBP57705.2023.10061314","DOIUrl":null,"url":null,"abstract":"Heart disease is leading killers of human beings. Recognizing and categorizing Electrocardiogram (ECG) signals is crucial for early heart and cardiovascular disease prevention. A novel classification approach for ECG Arrhythmias based on Graph Bispectrum (GBispec) is proposed. First, the ECG signal is converted from the time domain to the Graph domain by using Graph Fourier Transform (GFT); Then, referring to the traditional bispectrum algorithm, the GFT results of ECG are converted into GBispec; Then, extract the graph features of Graph Integral Bispectrum (GIB), and use Deep Neural Networks(DNN) to process the GIB results. 4 different types of ECG signals are classified. Experiments results show that proposed method is effective in classification.","PeriodicalId":309634,"journal":{"name":"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ECG arrhythmias Classification with a Graph Bispectrum method\",\"authors\":\"Yang Shiyilin, Shao Jie, Yang Xin, Chen Xin, Wang Xingxing\",\"doi\":\"10.1109/ISBP57705.2023.10061314\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Heart disease is leading killers of human beings. Recognizing and categorizing Electrocardiogram (ECG) signals is crucial for early heart and cardiovascular disease prevention. A novel classification approach for ECG Arrhythmias based on Graph Bispectrum (GBispec) is proposed. First, the ECG signal is converted from the time domain to the Graph domain by using Graph Fourier Transform (GFT); Then, referring to the traditional bispectrum algorithm, the GFT results of ECG are converted into GBispec; Then, extract the graph features of Graph Integral Bispectrum (GIB), and use Deep Neural Networks(DNN) to process the GIB results. 4 different types of ECG signals are classified. Experiments results show that proposed method is effective in classification.\",\"PeriodicalId\":309634,\"journal\":{\"name\":\"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISBP57705.2023.10061314\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISBP57705.2023.10061314","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

心脏病是人类的头号杀手。识别和分类心电图信号对早期心脏和心血管疾病的预防至关重要。提出了一种新的基于图双谱(GBispec)的心电失常分类方法。首先,利用图傅里叶变换(GFT)将心电信号从时域转换到图域;然后,参照传统的双谱算法,将ECG的GFT结果转换为GBispec;然后,提取图积分双谱(GIB)的图特征,并使用深度神经网络(DNN)对GIB结果进行处理。分为4种不同类型的心电信号。实验结果表明,该方法具有较好的分类效果。
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
ECG arrhythmias Classification with a Graph Bispectrum method
Heart disease is leading killers of human beings. Recognizing and categorizing Electrocardiogram (ECG) signals is crucial for early heart and cardiovascular disease prevention. A novel classification approach for ECG Arrhythmias based on Graph Bispectrum (GBispec) is proposed. First, the ECG signal is converted from the time domain to the Graph domain by using Graph Fourier Transform (GFT); Then, referring to the traditional bispectrum algorithm, the GFT results of ECG are converted into GBispec; Then, extract the graph features of Graph Integral Bispectrum (GIB), and use Deep Neural Networks(DNN) to process the GIB results. 4 different types of ECG signals are classified. Experiments results show that proposed method is effective in classification.
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
自引率
0.00%
发文量
0
期刊最新文献
AI Technology for Anti-Aging: an Overview ConvE-Bio: Knowledge Graph Embedding for Biomedical Relation Prediction ISBP 2023 Cover Page Building Semantic Segmentation of High-resolution Remote Sensing Image Buildings Based on U-net Network Model Based on Pytorch Framework Hybrid Multistage Feature Selection Method and its Application in Chinese Medicine
×
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