Jove
Visualize
联系我们

相关概念视频

Pulse rhythm01:30

Pulse rhythm

782
Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
782

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Early Drowsiness Detection via Second-Order Derivative Analysis of Heart Rate Variability: A Non-Contact ECG Approach with Machine Learning.

Sensors (Basel, Switzerland)·2026
Same author

Assessment of KN95 Mask Filtering Degradation and Breathing Detection: A Pilot Study.

Sensors (Basel, Switzerland)·2025
Same author

Cardiac Monitoring with Textile Capacitive Electrodes in Driving Applications: Characterization of Signal Quality and RR Duration Accuracy.

Sensors (Basel, Switzerland)·2025
Same author

Exchange Bias in La<sub>0.67</sub>Sr<sub>0.33</sub>MnO<sub>3</sub>/YFeO<sub>3</sub> Ferromagnet/Antiferromagnet Multilayer Heterostructures.

Small (Weinheim an der Bergstrasse, Germany)·2025
Same author

Respiratory Monitoring with Textile Inductive Electrodes in Driving Applications: Effect of Electrode's Positioning and Form Factor on Signal Quality.

Sensors (Basel, Switzerland)·2025
Same author

Controlling the Optical and Electrical Properties of Perovskite Films and Enhancing Solar Cell Performance Using the Photonic Curing Process.

Nanomaterials (Basel, Switzerland)·2024
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关实验视频

Updated: Jun 21, 2025

Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice
10:56

Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice

Published on: August 2, 2017

10.0K

通过使用机器学习算法进行心率变化分析来检测睡眠和清醒状态的衍生方法.

Fabrice Vaussenat1, Abhiroop Bhattacharya1, Philippe Boudreau2

  • 1Department of Electrical Engineering, École de Technologie Supérieure, Université du Québec, Montréal, QC H3C 1K3, Canada.

Sensors (Basel, Switzerland)
|July 13, 2024
PubMed
概括
此摘要是机器生成的。

这项研究提出了一种简单,便携式的方法来预测睡眠状态. 使用心率变化,特别是RR间隔,可以准确地区分健康成年人的清醒和小睡期间.

关键词:
这就是为什么物联网物联网物联网.在RR间隔的RR间隔.中枢神经系统中枢神经系统深度学习是一种深度学习.衍生方法是一种衍生方法.嵌入式医疗器械 嵌入式医疗器械心率变化的心率变化.聚类人体图像 (polysomnography) 是一种多人体图像.睡眠障碍 睡眠障碍 睡眠障碍睡眠醒检测检测 睡眠醒检测

更多相关视频

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

1.8K
Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
08:12

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions

Published on: June 5, 2019

19.8K

相关实验视频

Last Updated: Jun 21, 2025

Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice
10:56

Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice

Published on: August 2, 2017

10.0K
Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

1.8K
Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
08:12

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions

Published on: June 5, 2019

19.8K

科学领域:

  • 心脏病学 心脏病学
  • 神经学 神经学
  • 睡眠医学 睡眠医学

背景情况:

  • 睡眠障碍对健康有显著的短期和长期后果,包括注意力缺陷和心脏问题.
  • 多睡眠学 (PSG) 是一种常见的睡眠评估工具,但由于众多的电缆而具有侵入性,通常需要临床设置,可能会影响准确性.
  • 通过便携式设备评估中枢神经系统 (CNS) 为睡眠障碍评估提供了一个更简单的替代方案.

研究的目的:

  • 开发和验证一种轻量级特征分类模型,用于预测清醒和小睡状态.
  • 调查使用RR间隔 (RRI) 和其第二导数用于睡眠状态预测的有效性.
  • 为了确定一个短的RRI时间序列窗口是否足以准确分类睡眠状态.

主要方法:

  • 实施了利用RR区间 (RRI) 和其第二个导数的特征分类模型.
  • 经过训练和验证,该模型使用了来自9名健康年轻成年人的心率变化 (HRV) 数据.
  • 分析了与开灯,关灯,入睡和睡眠抵消事件相关的HRV数据.

主要成果:

  • 一个30分钟的RRI时间序列窗口被发现是足够的模型.
  • 轻量级模型准确地预测了受试者是否清醒或午睡.
  • 该方法表明了对非侵入性睡眠状态评估的潜力.

结论:

  • 使用RRI的简单,便携式模型可以准确地预测清醒和小睡状态.
  • 这种方法提供了一个不那么侵入性的替代传统的多睡眠学 (PSG).
  • 进一步的研究可以探索这种方法用于更广泛的睡眠障碍诊断.