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相关概念视频

Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

6.4K
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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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...
2.4K
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

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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...
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相关实验视频

Updated: Jul 15, 2025

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
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生物识别对比学习用于从心电图像获得数据效率高的深度学习.

Veer Sangha1, Akshay Khunte2, Gregory Holste3

  • 1Department of Engineering Science, Oxford University, Oxford, UK.

medRxiv : the preprint server for health sciences
|September 25, 2023
PubMed
概括

生物识别对比学习 (BCL) 显著改善了人工智能 (AI) 从心电图 (ECG) 图像中检测心脏病的功能. 这种自我监督的方法需要更少的标记数据,在医学诊断中推进AI.

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Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI
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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
  • 医疗成像医学成像

背景情况:

  • 在心电图 (ECG) 分析中用于AI的传统监督学习需要广泛的标记数据.
  • 开发用于ECG解释的AI模型受到数据限制的挑战.

研究的目的:

  • 引入生物识别对比学习 (BCL),一种自我监督的预训练方法,用于对心电图像进行标签效率高的深度学习.
  • 通过使用心电图像,评估BCL在检测心房动 (AF),性别和左心室射出小部分 (LVEF<40%) 的性能.

主要方法:

  • 训练了一个卷积神经网络,使用来自78,288个人的ECG对进行自我监督的预训.
  • 精心调整的BCL预训练模型在标记的心电图数据上用于特定的诊断任务.
  • 将BCL与随机初始化和SimCLR在内部和外部验证数据集中的比较.

主要成果:

  • BCL的表现始终优于随机初始化和SimCLR,特别是在有限的标记数据 (例如0.1%的数据) 时.
  • 在使用最少的数据的情况下,BCL实现了高性能 (AUROC 0.88/0.79/0.75),而其他方法表现不佳.
  • 在外部验证中,BCL表现优越,即使使用100%标记数据.

结论:

  • 使用来自同一患者心电图的生物识别签名的预训练策略提高了AI模型开发效率.
  • BCL代表了从心电图片中检测疾病的重大进步,特别是当标记数据稀缺时.