• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

50赫兹的心电图采样频率对于准确的心率变异性分析来说足够高吗?

Is 50 Hz high enough ECG sampling frequency for accurate HRV analysis?

作者信息

Mahdiani Shadi, Jeyhani Vala, Peltokangas Mikko, Vehkaoja Antti

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:5948-51. doi: 10.1109/EMBC.2015.7319746.

DOI:10.1109/EMBC.2015.7319746
PMID:26737646
Abstract

With the worldwide growth of mobile wireless technologies, healthcare services can be provided at anytime and anywhere. Usage of wearable wireless physiological monitoring system has been extensively increasing during the last decade. These mobile devices can continuously measure e.g. the heart activity and wirelessly transfer the data to the mobile phone of the patient. One of the significant restrictions for these devices is usage of energy, which leads to requiring low sampling rate. This article is presented in order to investigate the lowest adequate sampling frequency of ECG signal, for achieving accurate enough time domain heart rate variability (HRV) parameters. For this purpose the ECG signals originally measured with high 5 kHz sampling rate were down-sampled to simulate the measurement with lower sampling rate. Down-sampling loses information, decreases temporal accuracy, which was then restored by interpolating the signals to their original sampling rates. The HRV parameters obtained from the ECG signals with lower sampling rates were compared. The results represent that even when the sampling rate of ECG signal is equal to 50 Hz, the HRV parameters are almost accurate with a reasonable error.

摘要

随着移动无线技术在全球范围内的发展,医疗保健服务可以在任何时间、任何地点提供。在过去十年中,可穿戴无线生理监测系统的使用一直在广泛增加。这些移动设备可以持续测量例如心脏活动,并将数据无线传输到患者的手机。这些设备的一个重大限制是能量使用,这导致需要低采样率。本文旨在研究心电图(ECG)信号的最低适当采样频率,以获得足够准确的时域心率变异性(HRV)参数。为此,将最初以5kHz高采样率测量的ECG信号进行降采样,以模拟较低采样率的测量。降采样会丢失信息,降低时间精度,然后通过将信号插值到其原始采样率来恢复。比较了从较低采样率的ECG信号中获得的HRV参数。结果表明,即使ECG信号的采样率等于50Hz,HRV参数也几乎准确,误差合理。

相似文献

1
Is 50 Hz high enough ECG sampling frequency for accurate HRV analysis?50赫兹的心电图采样频率对于准确的心率变异性分析来说足够高吗?
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:5948-51. doi: 10.1109/EMBC.2015.7319746.
2
Wearable Noncontact Armband for Mobile ECG Monitoring System.可穿戴式非接触手臂带,用于移动心电图监测系统。
IEEE Trans Biomed Circuits Syst. 2016 Dec;10(6):1112-1118. doi: 10.1109/TBCAS.2016.2519523. Epub 2016 May 18.
3
A Low-Power High-Data-Transmission Multi-Lead ECG Acquisition Sensor System.一种低功耗、高数据传输的多导联 ECG 采集传感器系统。
Sensors (Basel). 2019 Nov 16;19(22):4996. doi: 10.3390/s19224996.
4
[Options and limitations of heart rate measurement and analysis of heart rate variability by mobile devices: A systematic review].[移动设备心率测量及心率变异性分析的选项与局限性:一项系统评价]
Herzschrittmacherther Elektrophysiol. 2016 Mar;27(1):38-45. doi: 10.1007/s00399-016-0419-5. Epub 2016 Feb 10.
5
A comprehensive ubiquitous healthcare solution on an Android™ mobile device.安卓(Android)移动设备上的全面普及医疗保健解决方案。
Sensors (Basel). 2011;11(7):6799-815. doi: 10.3390/s110706799. Epub 2011 Jun 29.
6
Recording of ECG signals on a portable MiniDisc recorder for time and frequency domain heart rate variability analysis.在便携式迷你光盘录音机上记录心电图信号,用于时域和频域心率变异性分析。
Physiol Behav. 2005 Jan 17;83(5):729-38. doi: 10.1016/j.physbeh.2004.09.007.
7
Methodological considerations in calculating heart rate variability based on wearable device heart rate samples.基于可穿戴设备心率样本计算心率变异性的方法学考虑。
Comput Biol Med. 2018 Nov 1;102:396-401. doi: 10.1016/j.compbiomed.2018.08.023. Epub 2018 Aug 22.
8
Activity aware energy efficient priority based multi patient monitoring adaptive system for body sensor networks.用于身体传感器网络的基于活动感知、节能、优先级的多患者监测自适应系统。
Technol Health Care. 2014 Jan 1;22(2):167-77. doi: 10.3233/THC-140782.
9
Electrocardiogram Sampling Frequency Range Acceptable for Heart Rate Variability Analysis.心率变异性分析可接受的心电图采样频率范围。
Healthc Inform Res. 2018 Jul;24(3):198-206. doi: 10.4258/hir.2018.24.3.198. Epub 2018 Jul 31.
10
WSN based mobile u-healthcare system with ECG, blood pressure measurement function.基于无线传感器网络的具有心电图、血压测量功能的移动医疗保健系统。
Annu Int Conf IEEE Eng Med Biol Soc. 2008;2008:1533-6. doi: 10.1109/IEMBS.2008.4649461.

引用本文的文献

1
Ten quick tips for electrocardiogram (ECG) signal processing.心电图(ECG)信号处理的十条快速提示。
PeerJ Comput Sci. 2024 Sep 3;10:e2295. doi: 10.7717/peerj-cs.2295. eCollection 2024.
2
Detection and classification of atrial and ventricular cardiovascular diseases to improve the cardiac health literacy for resource constrained regions.检测和分类心房和心室心血管疾病,以提高资源受限地区的心脏健康素养。
Healthc Technol Lett. 2023 Apr 10;10(3):35-52. doi: 10.1049/htl2.12043. eCollection 2023 Jun.
3
Humming (Simple Bhramari Pranayama) as a Stress Buster: A Holter-Based Study to Analyze Heart Rate Variability (HRV) Parameters During Bhramari, Physical Activity, Emotional Stress, and Sleep.
哼唱(简易蜂鸣式呼吸法)作为一种减压方式:一项基于动态心电图的研究,旨在分析蜂鸣式呼吸法、体育活动、情绪应激和睡眠期间的心率变异性(HRV)参数。
Cureus. 2023 Apr 13;15(4):e37527. doi: 10.7759/cureus.37527. eCollection 2023 Apr.
4
Designing an App to Promote Physical Exercise in Sedentary People Using a Day-to-Day Algorithm to Ensure a Healthy Self-Programmed Exercise Training.设计一个应用程序,使用日常算法促进久坐人群的体育锻炼,以确保健康的自我编程锻炼训练。
Int J Environ Res Public Health. 2023 Jan 14;20(2):1528. doi: 10.3390/ijerph20021528.
5
Understanding how virtual reality forest experience promote physiological and psychological health for patients undergoing hemodialysis.了解虚拟现实森林体验如何促进接受血液透析的患者的生理和心理健康。
Front Psychiatry. 2022 Dec 14;13:1007396. doi: 10.3389/fpsyt.2022.1007396. eCollection 2022.
6
Validity and reliability of short-term heart-rate variability from disposable electrocardiography leads.一次性心电图导联短期心率变异性的有效性和可靠性
Health Sci Rep. 2022 Dec 8;6(1):e984. doi: 10.1002/hsr2.984. eCollection 2023 Jan.
7
Differences in the Course of Physiological Functions and in Subjective Evaluations in Connection With Listening to the Sound of a Chainsaw and to the Sounds of a Forest.与电锯声音和森林声音相关的生理功能过程及主观评价的差异。
Front Psychol. 2022 Feb 21;13:775173. doi: 10.3389/fpsyg.2022.775173. eCollection 2022.
8
Atrial Fibrillation Classification with Smart Wearables Using Short-Term Heart Rate Variability and Deep Convolutional Neural Networks.基于短期心率变异性和深度卷积神经网络的智能可穿戴设备心房颤动分类。
Sensors (Basel). 2021 Oct 30;21(21):7233. doi: 10.3390/s21217233.
9
Deconstructing Commercial Wearable Technology: Contributions toward Accurate and Free-Living Monitoring of Sleep.解构商业可穿戴技术:对准确和自由睡眠监测的贡献。
Sensors (Basel). 2021 Jul 27;21(15):5071. doi: 10.3390/s21155071.
10
The Different Facets of Heart Rate Variability in Obstructive Sleep Apnea.阻塞性睡眠呼吸暂停中心率变异性的不同方面
Front Psychiatry. 2021 Jul 22;12:642333. doi: 10.3389/fpsyt.2021.642333. eCollection 2021.