Standalone Heartbeat Extraction in SCG Signal Using Variational Mode Decomposition

Tilendra Choudhary, L. Sharma, M. Bhuyan
{"title":"Standalone Heartbeat Extraction in SCG Signal Using Variational Mode Decomposition","authors":"Tilendra Choudhary, L. Sharma, M. Bhuyan","doi":"10.1109/WISPNET.2018.8538723","DOIUrl":null,"url":null,"abstract":"In this paper, a variational mode decomposition (VMD) based heartbeat extraction framework is proposed for seismocardiogram (SCG) signal. A reference cardiac signal such as ECG is not needed in our proposed method. The proposed method consists of four major steps: signal decomposition using VMD algorithm, heart rate (HR) envelope construction, low pass filtering of constructed envelope, and annotation of smoothed envelope. The method annotates the HR envelope in terms of characteristic points such as PZCI, NZCI, PI, and TI. Each of the characteristic points can be used for SCG cycle extraction. The proposed method is tested and validated with CEBS database available at the Physionet archieve. Based on the experimental results, it is observed that the proposed method with peak instances (PI) achieves consistent results with good accuracy among all. The qualitative analysis of performance results shows good performance of the proposed method for healthy subjects.","PeriodicalId":6858,"journal":{"name":"2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)","volume":"32 1","pages":"1-4"},"PeriodicalIF":0.0000,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WISPNET.2018.8538723","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

Abstract

In this paper, a variational mode decomposition (VMD) based heartbeat extraction framework is proposed for seismocardiogram (SCG) signal. A reference cardiac signal such as ECG is not needed in our proposed method. The proposed method consists of four major steps: signal decomposition using VMD algorithm, heart rate (HR) envelope construction, low pass filtering of constructed envelope, and annotation of smoothed envelope. The method annotates the HR envelope in terms of characteristic points such as PZCI, NZCI, PI, and TI. Each of the characteristic points can be used for SCG cycle extraction. The proposed method is tested and validated with CEBS database available at the Physionet archieve. Based on the experimental results, it is observed that the proposed method with peak instances (PI) achieves consistent results with good accuracy among all. The qualitative analysis of performance results shows good performance of the proposed method for healthy subjects.
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
基于变分模态分解的SCG信号独立心跳提取
本文提出了一种基于变分模态分解(VMD)的地震心动图(SCG)信号心跳提取框架。该方法不需要参考心电图等心脏信号。该方法包括四个主要步骤:利用VMD算法对信号进行分解、构建心率包络、对构建的包络进行低通滤波、对平滑包络进行标注。该方法用PZCI、NZCI、PI、TI等特征点对HR包络进行标注。每个特征点都可以用于SCG循环提取。所提出的方法在Physionet存档的CEBS数据库中进行了测试和验证。实验结果表明,采用峰值实例(peak instance, PI)的方法得到的结果一致,且精度较高。定性分析结果表明,该方法对健康受试者具有良好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Deep Reinforcement Learning for the Capacitated Vehicle Routing Problem with Soft Time Window Integrated Interference Solutions Between 5G and Satellite Systems Modulation Recognition Method of MAPSK Signal Artificial Intelligence Routing Method in Wireless Sensor Network for Sewage Treatment Monitoring Electromagnetically Induced Transparency in a Coupled NV Spin-Mechanical Resonator System
×
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