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相关概念视频

Electrocardiogram01:29

Electrocardiogram

2.2K
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...
2.2K
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

524
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...
524
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

521
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....
521

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相关实验视频

Updated: Jun 8, 2025

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
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Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

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评估基于梯度的解释方法,用于使用热图进行神经网络心电图分析.

Andrea Marheim Storås1,2, Steffen Mæland3, Jonas L Isaksen4

  • 1Department of Holistic Systems, SimulaMet, 0170 Oslo, Norway.

Journal of the American Medical Informatics Association : JAMIA
|November 6, 2024
PubMed
概括
此摘要是机器生成的。

对心电图 (ECG) 分析的深度神经网络解释方法的评估表明没有单一的最佳方法. 建议应用多种解释方法,以获得医疗AI的最佳结果.

关键词:
可解释的人工智能机器学习是机器学习.

更多相关视频

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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相关实验视频

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科学领域:

  • 人工智能在医学中的应用
  • 生物医学信号处理

背景情况:

  • 深度神经网络 (DNN) 越来越多地用于心电图 (ECG) 分析.
  • 解释DNN预测对于临床采用和信任至关重要.
  • 热图可视化常用于解释DNN输出.

研究的目的:

  • 评估基于DNN的ECG分析的流行的解释方法.
  • 将定性专家评估与基于干扰的客观评估进行比较.
  • 为选择医学AI中的解释方法提供建议.

主要方法:

  • 一个残留的DNN被训练用于ECG间隔和幅度预测.
  • 评估了9种解释方法 (Saliency,Deconvolution,引导反向传播,梯度SHAP,SmoothGrad,输入×梯度,DeepLIFT,集成梯度,GradCAM).
  • 医疗专家对方法进行了定性评估,并通过扰动对方法进行了定量评估.

主要成果:

  • 没有一个单一的解释方法始终优于其他方法;有些方法比其他方法差.
  • 在人类专家评估和客观基于扰乱的评估之间观察到显著的分歧.
  • 方法性能因预测的特定ECG测量而有所不同.

结论:

  • 最佳的解释方法取决于ECG测量的上下文.
  • 数据科学家和医学专家之间的合作对于开发有用的解释方法至关重要.
  • 建议采用多种解释方法,以确保医疗AI的强大和可靠的解释.