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

Electrocardiogram01:29

Electrocardiogram

1.6K
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...
1.6K
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

434
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...
434
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

275
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....
275
Correlation between ECG and Cardiac Cycle01:24

Correlation between ECG and Cardiac Cycle

2.8K
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...
2.8K

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

Assessment of Maxillary Growth in Patients With an Isolated Cleft Palate at 5 and 10 Years of Age.

The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association·2026
Same author

Craniometaphyseal Dysplasia: A Multidisciplinary Approach and Orthognathic Management in an Adolescent Patient.

The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association·2026
Same author

The mitochondrial protease, LonP1, is a potential cardioprotective target for attenuating doxorubicin-induced cardiomyocyte death.

Journal of translational medicine·2026
Same author

Heterogeneity in proportional cardiovascular co-listing on cancer death certificates in the United States: a national multiple cause-of-death analysis, 1999-2020.

Cardio-oncology (London, England)·2026
Same author

Trabectedin-Associated Myocardial Infarction With Nonobstructive Coronary Arteries.

JACC. Case reports·2026
Same author

Multimodal Federated Learning in Healthcare: A Review.

Journal of healthcare informatics research·2026

相关实验视频

Updated: May 11, 2025

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
08:10

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

Published on: July 20, 2022

1.6K

人工智能分析用于从12导电心电图中估计射出分数.

Alina Devkota1, Rukesh Prajapati2, Amr El-Wakeel2

  • 1Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, USA. ad00139@mix.wvu.edu.

Scientific reports
|April 18, 2025
PubMed
概括

深度学习模型可以从心电图 (ECG) 信号中估计心脏喷射分数 (EF),为心力衰竭 (HF) 诊断提供一个具有成本效益的解决方案. 这项研究验证了农村人口的AI性能,达到0.86.8的AUROC.

关键词:
深度学习 (Deep Learning) 是一种深度学习.喷射分数 喷射分数电心电图 (ECG) 是一种心电图.这是心脏衰竭.机器学习 机器学习这就是ResNet ResNet.变压器 变压器 变压器

更多相关视频

Transthoracic Echocardiography to Assess Post-Resuscitation Left Ventricular Dysfunction After Acute Myocardial Infarction and Cardiac Arrest in Pigs
08:19

Transthoracic Echocardiography to Assess Post-Resuscitation Left Ventricular Dysfunction After Acute Myocardial Infarction and Cardiac Arrest in Pigs

Published on: July 12, 2022

2.7K
Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism
11:04

Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism

Published on: September 1, 2014

11.1K

相关实验视频

Last Updated: May 11, 2025

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
08:10

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

Published on: July 20, 2022

1.6K
Transthoracic Echocardiography to Assess Post-Resuscitation Left Ventricular Dysfunction After Acute Myocardial Infarction and Cardiac Arrest in Pigs
08:19

Transthoracic Echocardiography to Assess Post-Resuscitation Left Ventricular Dysfunction After Acute Myocardial Infarction and Cardiac Arrest in Pigs

Published on: July 12, 2022

2.7K
Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism
11:04

Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism

Published on: September 1, 2014

11.1K

科学领域:

  • 心脏病学 心脏病学
  • 人工智能的人工智能
  • 医疗信息学 医疗信息学

背景情况:

  • 心力衰竭 (HF) 是心血管死亡的主要原因,患病率正在上升.
  • 射出分数 (EF) 测量对于HF诊断和管理至关重要.
  • 心声图是黄金标准,但由于成本和可访问性受到限制,与心电图 (ECG) 不同.

研究的目的:

  • 探索使用机器学习 (ML) 和深度学习 (DL) 模型估计EF的12导电图信号的潜力.
  • 评估人工智能模型在代表性不足的阿巴拉契亚农村人口中用于EF估计的性能.
  • 评估不同的人口结构对AI公平性和心血管健康准确性的影响.

主要方法:

  • 利用了来自西弗吉尼亚州的55500名患者的12导电图数据集.
  • 应用了一系列人工智能算法,包括基于随机森林和变压器的DL模型,用于EF估计.
  • 分析了使用各种值,单线或多线心电图信号的模型性能,并进行了可解释性分析.

主要成果:

  • 深度学习算法实现了最高的性能,接收器运行特征曲线 (AUROC) 下的面积约为0.86,用于从12导心电图中估计EF.
  • 单个心电图线索不足以准确估计EF.
  • 特定的ECG组合可以显著提高分类性能.

结论:

  • DL模型显示了从12导电心电图估计EF的显著前景,为高频监测提供了可扩展的解决方案.
  • 人工智能模型在不同人群中的表现,如阿巴拉契亚农村地区,对于公平的医疗保健至关重要.
  • 在心电图分析中优化组合可以提高人工智能驱动的EF估计准确度.