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相关概念视频

Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

1.4K
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...
1.4K
ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

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

469
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...
469
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

11.7K
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...
11.7K
Electrocardiogram01:29

Electrocardiogram

5.4K
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...
5.4K

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相关实验视频

Updated: Jan 16, 2026

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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从使用深度学习的ECG在社区预测心房动:一项跨国研究.

Luisa C C Brant1,2, Antônio H Ribeiro3, Oseiwe B Eromosele4

  • 1Faculty of Medicine & Hospital das Clínicas/EBSERH, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil (L.C.C.B., S.M.B., A.L.P.R.).

Circulation. Arrhythmia and electrophysiology
|September 30, 2025
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概括

使用心电图数据的深度神经网络模型有效预测心房动 (AF) 风险和心血管结果. 将这种ECG-AF模型与CHARGE-AF得分相结合,可以提高不同人群的预测准确性.

关键词:
心房动是心房动的一种.深度学习是一种深度学习.早期诊断 早期诊断 早期诊断电心电图 (ECG) 是一种心电图.有关风险因素的风险因素.

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

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

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相关实验视频

Last Updated: Jan 16, 2026

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
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Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
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Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

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

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

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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
  • 公共卫生 公共卫生

背景情况:

  • 心房动 (AF) 构成严重的健康风险.
  • 现有的AF风险评分有其局限性.
  • 多种数据集对于验证预测模型至关重要.

研究的目的:

  • 改进和验证深度神经网络模型 (ECG-AF) 以使用心电图数据预测AF风险.
  • 将ECG-AF模型的性能与已建立的CHARGE-AF风险评分进行比较.
  • 评估ECG-AF模型与其他心血管结果的关联.

主要方法:

  • 该ECG-AF模型是使用60%的Framingham心脏研究 (FHS) 样本开发的.
  • 用FHS,英国生物库和ELSA-巴西队列的曲线下面面积 (AUC) 来评估模型性能.
  • 考克斯的比例危险模型被用来评估与心血管结果的关联.

主要成果:

  • 在FHS中,ECG-AF模型显示了发生性AF (AUC,0.82) 的中度歧视,与CHARGE-AF (AUC,0.83) 相比.
  • 结合ECG-AF和CHARGE-AF在FHS和其他队列中改善了AF预测 (AUC,0.85).
  • 较高的ECG-AF得分与心力衰竭,心肌梗塞,中风和死亡率的风险增加有关.

结论:

  • 单输入ECG-AF深度神经网络模型在预测AF和心血管结果方面表现强.
  • 该模型可与多变量临床风险得分进行比较,并且在组合时提供更好的预测.
  • 这种人工智能驱动的方法有望提高不同人群的心血管风险评估.