Jove
Visualize
联系我们
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

相关概念视频

Holter Monitor: 24-Hour Monitoring01:23

Holter Monitor: 24-Hour Monitoring

1.9K
Holter monitoring is a continuous electrocardiography (ECG) recording that tracks the heart's electrical activity over an extended period, generally 24 to 48 hours. This noninvasive diagnostic tool detects irregular heart rhythms that may not be captured during a standard ECG performed in a clinical setting.DeviceThe Holter monitor is a portable, small device connected to several electrodes on the patient's chest. These electrodes detect the heart's electrical signals and transmit them to the...
1.9K
Pulse rhythm01:30

Pulse rhythm

1.3K
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...
1.3K
Electrocardiogram01:29

Electrocardiogram

5.3K
An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
5.3K
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

11.6K
The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
11.6K
Instrumentation Amplifier01:25

Instrumentation Amplifier

1.0K
An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
To overcome this challenge, an ECG machine utilizes an instrumentation amplifier. This specialized amplifier is...
1.0K
Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

315
Dysrhythmias, also known as arrhythmias, are disturbances in the heart's rhythm that range from benign to life-threatening. A thorough evaluation is crucial for appropriate management and involves a comprehensive medical history, physical examination, and various diagnostic tests.Medical HistorySymptoms: Collect detailed information on palpitations, dizziness, syncope, chest pain, and fatigue. Note their onset, frequency, and triggers.Previous Cardiac Issues: Document any history of heart...
315

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 Sepsis Detection Using Heterogeneous Structured ICU Data with Explainable Deep Learning.

Sensors (Basel, Switzerland)·2026
Same author

NURSE-AI: A Nurse-by-Design Framework for Multi-Sensor, AI-Enabled Chronic Wound Assessment in Community Healthcare.

Sensors (Basel, Switzerland)·2026
Same author

Reinforcement Learning-Based Management in IoT-Enabled Renewable Energy Communities: An Approach to Optimization for Comfort, Economy, and Sustainable Performance.

Sensors (Basel, Switzerland)·2026
Same author

AI-Enhanced Hybrid QAM-PPM Visible Light Communication for Body Area Networks.

Sensors (Basel, Switzerland)·2026
Same author

From Innovation to Integration: Bridging the Gap Between IoMT Technologies and Real-World Health Management Systems.

Sensors (Basel, Switzerland)·2025
Same author

A Home-Based Balance Exercise Training Program with Intermittent Visual Deprivation for Persons with Chronic Incomplete Spinal Cord Injury: A Pilot Study on Feasibility, Acceptability, and Preliminary Outcomes.

Sensors (Basel, Switzerland)·2025

相关实验视频

Updated: Jan 11, 2026

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
05:03

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

Published on: December 11, 2019

9.0K

在可穿戴式心电图监测中强大的节律失常检测的通用化混合波形深度学习架构.

Ukesh Thapa1, Bipun Man Pati1, Attaphongse Taparugssanagorn2

  • 1Advanced College of Engineering and Management, Tribhuvan University, Kathmandu 44600, Nepal.

Sensors (Basel, Switzerland)
|November 13, 2025
PubMed
概括

本研究引入了对心电图 (ECG) 节律分类的深度学习框架,将时间频率分析与手工制作的功能相结合. 该框架实现了可穿戴设备实时监控的高精度和效率.

关键词:
在ECG分类中使用ECG分类.心脏监测是指对心脏进行监测.混合信号处理器是指混合信号处理器.智能生物医学信号分析可穿戴式医疗保健

更多相关视频

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
06:07

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

4.3K
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

3.0K

相关实验视频

Last Updated: Jan 11, 2026

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
05:03

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

Published on: December 11, 2019

9.0K
Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
06:07

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

4.3K
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

3.0K

科学领域:

  • 心脏病学 心脏病学
  • 生物医学工程 生物医学工程
  • 人工智能的人工智能

背景情况:

  • 电心电图 (ECG) 分析对于诊断心脏病至关重要.
  • 由于信号变化和噪声,准确和高效的心电图节奏分类具有挑战性.
  • 深度学习为自动ECG解释提供了有前途的途径.

研究的目的:

  • 开发和评估用于ECG节律分类的渐进深度学习框架.
  • 研究将时间频率表示 (刻度图) 与手工制作的特征相结合的有效性.
  • 评估用于ECG分析的各种深度学习架构的性能和效率.

主要方法:

  • 电脑心电图信号被转化为头图,并由视觉转换器 (ViT) 和其他架构处理.
  • 尺度图与散射和统计特征融合,以提高稳定性.
  • 应用主要组件分析 (PCA) 来减少特征维度.
  • 训练时间的增加被用来解决阶级不平衡.

主要成果:

  • 视觉变压器 (ViT) 使用基于纯图像的心电图分析实现了高精度 (0.8590).
  • 具有融合特征的FusionViT产生了最好的性能 (精度=0.8623,F1得分=0.8528).
  • 融合ResNet-18提供了准确性和推断效率 (每样本0.016秒) 之间的平衡.
  • 在保持竞争性性能的同时,PCA减少了功能维度.

结论:

  • 拟议的框架证明了高准确性,稳定性和高效性,用于实际的心电图节奏分类.
  • 视觉和统计特征的组合,随着可选的PCA减少,是有效的.
  • 该框架适用于边缘设备 (如可穿戴设备和移动健康应用程序) 的实时监控.