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相关概念视频

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

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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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Predicting Molecular Geometry02:27

Predicting Molecular Geometry

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VSEPR Theory for Determination of Electron Pair Geometries
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Prediction Intervals01:03

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
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相关实验视频

Updated: Jan 28, 2026

Technique of Minimally Invasive Transverse Aortic Constriction in Mice for Induction of Left Ventricular Hypertrophy
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深度学习可以从心电图中预测左心室缩.

Hafiz Naderi1,2, Thomas Kaplan1, Stefan van Duijvenboden1,3

  • 1William Harvey Research Institute, Queen Mary University of London, Charterhouse Square, London, UK.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
|January 27, 2026
PubMed
概括

一种深度学习模型从心电图中准确地预测左心室缩 (LVH),优于以前的方法. 需要进一步开发各种数据集以确保广泛适用性.

关键词:
左心室过度缩小左心室过度缩小深度学习是一种深度学习.电心电图 (ECG) 是一种心电图.机器学习是机器学习.

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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:

  • 左心室缩 (LVH) 是心血管疾病的重要预测因素.
  • 以前使用心电图和临床数据进行LVH分类的监督机器学习模型实现了0.85的AUROC,但需要外部验证.
  • 外部验证对于评估预测模型的概括性至关重要.

研究的目的:

  • 开发一个深度学习 (DL) 模型,以改进心脏磁共振 (CMR) 衍生的LVH的分类.
  • 为了外部评估DL模型在波默兰的健康研究 (SHIP) 队列中的表现.
  • 评估基于DL的心电图查工具对LVH预测的可行性.

主要方法:

  • 开发了一个完全卷积网络DL模型,使用来自48,835名英国生物库参与者的12心电图和临床变量.
  • 该模型预测了索引左心室质量 (iLVM),用于重新校准的后勤回归.
  • 在训练,验证和测试组中使用接收操作曲线下的面积 (AUROC) 评估性能,并在SHIP队列中外部评估性能.

主要成果:

  • 在英国生物银行队列中,DL模型实现了0.97的AUROC,明显超过了以前的方法.
  • 在心电图上QRS复合和心室率被确定为LVH的关键预测指标.
  • DL模型对SHIP队列 (AUROC 0.78) 显示了适度的概括性,其中的变异归因于临床配置文件,心电图采集和CMR标签.

结论:

  • 基于DL的可扩展查工具可用于从心电图预测LVH是可行的.
  • 使用更大,更多样化的数据集进行进一步的模型开发是必要的,以提高概括性.
  • 在不同人群中,队列特征和数据采集的差异会影响模型性能.