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相关概念视频

Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

600
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...
600
Electrocardiogram01:29

Electrocardiogram

2.3K
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...
2.3K
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

905
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....
905

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相关实验视频

Updated: Jul 5, 2025

Electrocardiogram Recordings in Anesthetized Mice using Lead II
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QTNet:深度学习用于估计使用单一引线心电图的QT间隔.

Ridwan Alam, Aaron D Aguirre, Collin M Stultz

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 23, 2024
    PubMed
    概括

    一个新的深度学习模型,QTNet,准确地估计单线心电图的QT间隔,使得自动化,医院外监测致命心律失常风险.

    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:

    • QT延长是致命心律失常和突然心脏死亡的危险因素.
    • 目前的QT间隔监测依赖于专家对12导电心电图的解释,限制了连续的医院外追踪.
    • 穿戴式心电图技术和机器学习的进步为自动化QT间隔评估提供了潜力.

    研究的目的:

    • 开发和验证一个深度学习模型 (QTNet) 来从单线心电图进行准确的QT间隔回归.
    • 评估QTNet在不同数据集中的通用性和稳定性.
    • 将QTNet的性能与现有的自动化心电图分析方法进行比较.

    主要方法:

    • 一个残留的神经网络,QTNet,是使用单导 (Lead-I) ECG数据开发的,用于 QT 间隔回归.
    • 在监督下,QTNet在美国一家医院的大型心电图数据集上接受了训练.
    • 模型的性能在四个独立的测试集上进行了评估,包括来自不同机构和公共数据集的数据.

    主要成果:

    • 在所有测试数据集中,QTNet 实现了 9 毫秒至 15.8 毫秒的平均绝对误差 (MAE).
    • 对于QTNet估计的皮尔森相关系数在0.899到0.914.9之间.

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    相关实验视频

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    Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice

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  • QTNet显著优于标准自动化方法 (NeuroKit2),该方法显示MAE从22.29ms到90.79ms.

    • 结论:

    • QTNet显示出高精度和QT间隔估计的概括性从单线心电图.
    • 该模型显示了在临床和门诊环境中实现自动化,无处不在的QT跟踪的巨大潜力.
    • QTNet促进了QT延长风险的患者的持续监测,改善了心律失常风险管理.