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

Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

642
Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
An ECG utilizes electrodes on the skin...
642
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

6.9K
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...
6.9K
Electrocardiogram01:29

Electrocardiogram

2.5K
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...
2.5K
ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias

45
Arrhythmia is a condition characterized by an irregular heart rhythm, with ECG changes that differ based on its origin and nature. The types of arrhythmias discussed below include atrial, junctional, and ventricular arrhythmias.Atrial ArrhythmiasPremature Atrial Complexes (PACs): PACs are early atrial beats caused by stress, caffeine, alcohol, electrolyte imbalances, hypoxia, hyperthyroidism, or certain medications (e.g., bronchodilators and decongestants). The ECG shows early P waves with an...
45
Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

42
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...
42
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

1.3K
An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
ECG waveforms are divided by vertical and horizontal lines at standard intervals.
The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage....
1.3K

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

Infrared spectral signatures of nucleobases in interstellar ices II: Pyrimidines.

Life sciences in space research·2026
Same author

Malignant arrhythmia risk assessment based on lead-I mobile ECG measurements using machine learning.

Journal of electrocardiology·2026
Same author

Efficacy, Safety, and Cost-Effectiveness of the Infliximab Biosimilar GP-1111 in Patients with Inflammatory Bowel Disease Who Underwent a Nonmedical Switch: A Prospective Cohort Study.

Biologics : targets & therapy·2025
Same author

Factors affecting response to furosemide stress test among critically ill hypoalbuminemic patients with AKI without prior albumin infusion.

BMC nephrology·2025
Same author

Reliable Single-Trial Detection of Saccade-Related Lambda Responses with Independent Component Analysis.

eNeuro·2025
Same author

An efficient approach for mathematical modeling and parameter estimation of PEM fuel based on Young's double-slit experiment algorithm.

Scientific reports·2025
Same journal

Novel Parent Survey Measures Sensory Behaviors Incorporating Sensory Modality and Stimulus Intensity.

Heliyon·2026
Same journal

Expression of concern: "SQSTM1/p62 promotes the progression of gastric cancer through epithelial-mesenchymal transition" [Heliyon 10 (2024) e24409].

Heliyon·2026
Same journal

Expression of concern: "TL1A promotes metastasis and EMT process of colorectal cancer" [Heliyon 10 (2024) e24392].

Heliyon·2026
Same journal

Expression of concern: "Factors affecting timing of surgery following neoadjuvant chemoradiation for esophageal cancer" [Heliyon 9 (2023) e23212].

Heliyon·2026
Same journal

Expression of concern: "On stratified single-valued soft topogenous structures" [Heliyon 10 (2024) e27926].

Heliyon·2026
Same journal

Expression of concern: "Artifact removal and motor imagery classification in EEG using advanced algorithms and modified DNN" [Heliyon 10 (2024) e27198].

Heliyon·2026
查看所有相关文章

相关实验视频

Updated: Jul 20, 2025

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

697

基于使用深度学习的单II心电图的心跳分类.

Mohamed F Issa1,2, Ahmed Yousry3, Gergely Tuboly2

  • 1Department of Scientific Computing, Faculty of Computers and Artificial Intelligence, Benha University, Benha, 13511, Egypt.

Heliyon
|August 4, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了带有残余块 (DNN-RB) 的深度神经网络,用于准确的心电图 (ECG) 信号分类. DNN-RB模型实现了高精度,超过了其他用于心血管疾病诊断的方法.

关键词:
心脏周期的心脏周期心血管疾病是什么心血管疾病深度神经网络是一个神经网络.电心电图 (ECG) 是一种心电图.剩余的块是剩余的块.

更多相关视频

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

8.7K
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

3.8K

相关实验视频

Last Updated: Jul 20, 2025

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

697
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

8.7K
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

3.8K

科学领域:

  • 生物医学工程 生物医学工程
  • 人工智能在医学中的应用
  • 心脏病学 心脏病学

背景情况:

  • 电心电图 (ECG) 信号分析对于诊断心血管疾病至关重要.
  • 手动ECG解释是复杂且耗时的.
  • 机器学习为自动ECG分类提供了潜力.

研究的目的:

  • 开发和验证一个具有残余块 (DNN-RB) 的深度神经网络模型,用于将心脏周期分为六个心电图节拍类别.
  • 评估DNN-RB模型的性能与最先进的算法对比.

主要方法:

  • 设计了一个包含残余块 (DNN-RB) 的深度神经网络模型.
  • 该模型使用MIT-BIH数据集进行了训练和验证.
  • 性能指标包括测试准确度,平均灵敏度和平均特异性.

主要成果:

  • DNN-RB模型的测试准确率达到了99.51%.
  • 平均灵敏度为99.7%,平均特异性为98.2%.
  • 拟议的方法在同一数据集上的其他最先进的算法相比显示出更高的性能.

结论:

  • DNN-RB模型对于自动ECG信号分类是有效的.
  • 该方法在使用移动心电图设备的临床和外科医院监测方面表现有前途.
  • 一个集成DNN-RB模型的Web应用程序方便了ECG分析和诊断.