利用心电图加速记录分析人体活动的改进

Itaru Kaneko, Y. Yoshida, E. Yuda
{"title":"利用心电图加速记录分析人体活动的改进","authors":"Itaru Kaneko, Y. Yoshida, E. Yuda","doi":"10.5121/sipij.2019.10504","DOIUrl":null,"url":null,"abstract":"The use of Holter Electrocardiograph (Holter ECG) is rapidly spreading. It is a wearable electrocardiograph that records 24-hour electrocardiograms in a built-in flash memory, making it possible to detect atrial fibrillation (Atrial Fibrillation, AF) through all-day activities. It is also useful for screening for diseases other than atrial fibrillation and for improving health. It is said that more useful information can be obtained by combining electrocardiograph with the analysis of physical activity. For that purpose, the Holter electrocardiograph is equipped with heart rate sensor and acceleration sensors. If acceleration data is analysed, we can estimate activities in daily life, such as getting up, eating, walking, using transportation, and sitting. In combination with such activity status, electrocardiographic data can be expected to be more useful. In this study, we investigate the estimation of physical activity. For the better analysis, we evaluated activity estimation using machine learning as well as several different feature extractions. In this report, we will show several different feature extraction methods and result of human body analysis using machine learning.","PeriodicalId":90726,"journal":{"name":"Signal and image processing : an international journal","volume":"12 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Improvements of the Analysis of Human Activity Using Acceleration Record of Electrocardiographs\",\"authors\":\"Itaru Kaneko, Y. Yoshida, E. Yuda\",\"doi\":\"10.5121/sipij.2019.10504\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The use of Holter Electrocardiograph (Holter ECG) is rapidly spreading. It is a wearable electrocardiograph that records 24-hour electrocardiograms in a built-in flash memory, making it possible to detect atrial fibrillation (Atrial Fibrillation, AF) through all-day activities. It is also useful for screening for diseases other than atrial fibrillation and for improving health. It is said that more useful information can be obtained by combining electrocardiograph with the analysis of physical activity. For that purpose, the Holter electrocardiograph is equipped with heart rate sensor and acceleration sensors. If acceleration data is analysed, we can estimate activities in daily life, such as getting up, eating, walking, using transportation, and sitting. In combination with such activity status, electrocardiographic data can be expected to be more useful. In this study, we investigate the estimation of physical activity. For the better analysis, we evaluated activity estimation using machine learning as well as several different feature extractions. In this report, we will show several different feature extraction methods and result of human body analysis using machine learning.\",\"PeriodicalId\":90726,\"journal\":{\"name\":\"Signal and image processing : an international journal\",\"volume\":\"12 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Signal and image processing : an international journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5121/sipij.2019.10504\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Signal and image processing : an international journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5121/sipij.2019.10504","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

动态心电图仪(Holter ECG)的应用正在迅速普及。它是一种可穿戴的心电图仪,在内置的闪存中记录24小时的心电图,使通过全天活动检测心房颤动(房颤,AF)成为可能。它也可用于筛查心房颤动以外的疾病和改善健康状况。据说,将心电图与身体活动分析相结合可以获得更有用的信息。为此,霍尔特心电图仪配备了心率传感器和加速度传感器。如果对加速度数据进行分析,我们就可以估计日常生活中的活动,比如起床、吃饭、走路、乘坐交通工具和坐着。结合这些活动状态,可以预期心电图数据更有用。在这项研究中,我们调查了体力活动的估计。为了更好的分析,我们使用机器学习和几种不同的特征提取来评估活动估计。在这篇报告中,我们将展示几种不同的特征提取方法和使用机器学习进行人体分析的结果。
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
Improvements of the Analysis of Human Activity Using Acceleration Record of Electrocardiographs
The use of Holter Electrocardiograph (Holter ECG) is rapidly spreading. It is a wearable electrocardiograph that records 24-hour electrocardiograms in a built-in flash memory, making it possible to detect atrial fibrillation (Atrial Fibrillation, AF) through all-day activities. It is also useful for screening for diseases other than atrial fibrillation and for improving health. It is said that more useful information can be obtained by combining electrocardiograph with the analysis of physical activity. For that purpose, the Holter electrocardiograph is equipped with heart rate sensor and acceleration sensors. If acceleration data is analysed, we can estimate activities in daily life, such as getting up, eating, walking, using transportation, and sitting. In combination with such activity status, electrocardiographic data can be expected to be more useful. In this study, we investigate the estimation of physical activity. For the better analysis, we evaluated activity estimation using machine learning as well as several different feature extractions. In this report, we will show several different feature extraction methods and result of human body analysis using machine learning.
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
相关文献
二甲双胍通过HDAC6和FoxO3a转录调控肌肉生长抑制素诱导肌肉萎缩
IF 8.9 1区 医学Journal of Cachexia, Sarcopenia and MusclePub Date : 2021-11-02 DOI: 10.1002/jcsm.12833
Min Ju Kang, Ji Wook Moon, Jung Ok Lee, Ji Hae Kim, Eun Jeong Jung, Su Jin Kim, Joo Yeon Oh, Sang Woo Wu, Pu Reum Lee, Sun Hwa Park, Hyeon Soo Kim
具有疾病敏感单倍型的非亲属供体脐带血移植后的1型糖尿病
IF 3.2 3区 医学Journal of Diabetes InvestigationPub Date : 2022-11-02 DOI: 10.1111/jdi.13939
Kensuke Matsumoto, Taisuke Matsuyama, Ritsu Sumiyoshi, Matsuo Takuji, Tadashi Yamamoto, Ryosuke Shirasaki, Haruko Tashiro
封面:蛋白质组学分析确定IRSp53和fastin是PRV输出和直接细胞-细胞传播的关键
IF 3.4 4区 生物学ProteomicsPub Date : 2019-12-02 DOI: 10.1002/pmic.201970201
Fei-Long Yu, Huan Miao, Jinjin Xia, Fan Jia, Huadong Wang, Fuqiang Xu, Lin Guo
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Omni-Modeler: Rapid Adaptive Visual Recognition with Dynamic Learning A Comparative Study of Machine Learning Algorithms for EEG Signal Classification Combining of Narrative News and VR Games: Comparison of Various Forms of News Games Mixed Spectra for Stable Signals from Discrete Observations Fractional Order Butterworth Filter for Fetal Electrocardiographic Signal Feature Extraction
×
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