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Electrocardiogram01:29

Electrocardiogram

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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...
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Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

543
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...
543
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

550
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....
550
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

3.5K
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...
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相关实验视频

Updated: Jun 12, 2025

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

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人工智能使用心电图数据预测年龄:探索生物年龄差异.

Shaun Evans1, Sarah A Howson2, Andrew E C Booth1

  • 1Royal Adelaide Hospital, Adelaide, South Australia, Australia; University of Adelaide, Adelaide, South Australia, Australia.

Heart rhythm
|September 28, 2024
PubMed
概括
此摘要是机器生成的。

人工智能 (AI) 可以从心电图 (ECG) 预测生物年龄. 这种AI-ECG模型显示女性和老年人的生物学年龄较小,年轻患者的年龄更高.

关键词:
心脏病学 心脏病学卷积神经网络是一种卷积神经网络.机器学习 机器学习预测 预测 预测 预测深度学习是一种深度学习.

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相关实验视频

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科学领域:

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

背景情况:

  • 在心电图 (ECG) 上训练的人工智能 (AI) 模型可以预测生物年龄.
  • 来自ECG的生物年龄预测是死亡率和心血管事件的预后指标.

研究的目的:

  • 开发一种人工智能模型,用ECG来预测生物年龄.
  • 为了比较基线特征并确定高级生物年龄的决定因素.

主要方法:

  • 从63,246名心脏病住院患者 (20至90岁) 的心电图上训练了一个卷积神经网络AI模型.
  • 在内部验证模型 (80:20分) 和外部使用英国生物银行数据.
  • 使用相关性,差异和平均绝对差异指标分析性能.

主要成果:

  • 在内部验证中,AI-ECG模型实现了0.72的相关系数,平均绝对年龄差异为9.1年.
  • 外部验证显示了类似的性能.
  • 观察到显著的分组差异:年轻患者 (20-29岁) 的生物年龄较高 (14.3岁),而老年患者 (80-89岁) 的生物年龄较低 (10.5岁).
  • 女性和患有单个心电图的患者分别比男性和患有多个心电图的患者生物学上更年轻.

结论:

  • 人工智能-心电图预测的生物年龄显示了患者子组之间的显著差异.
  • 生物学年龄偏离时间学年龄,特别是在年轻的住院患者 (较年长的生物学年龄) 和较年长的住院患者 (较年轻的生物学年龄) 中.
  • 性别和记录的心电图数量与生物年龄的差异有关.