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相关概念视频

Electrocardiogram01:29

Electrocardiogram

2.5K
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...
2.5K
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

643
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...
643
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

7.0K
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...
7.0K
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

1.4K
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....
1.4K

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相关实验视频

Updated: Jul 21, 2025

High-Throughput Analysis of Optical Mapping Data Using ElectroMap
07:36

High-Throughput Analysis of Optical Mapping Data Using ElectroMap

Published on: June 4, 2019

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热图解释对于基于深度学习的心电图分析的有用性

Andrea M Storås1,2, Ole Emil Andersen3,4, Sam Lockhart5

  • 1Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering, 0167 Oslo, Norway.

Diagnostics (Basel, Switzerland)
|July 29, 2023
PubMed
概括
此摘要是机器生成的。

深度神经网络可以从心电图中预测性别,但Grad-CAM解释方法没有为医生提供有用的见解. 需要进一步的研究来解释AI在临床环境中.

关键词:
电心电图是指心电图.可解释的人工智能热图可以提供热图.

更多相关视频

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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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

Published on: April 26, 2024

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相关实验视频

Last Updated: Jul 21, 2025

High-Throughput Analysis of Optical Mapping Data Using ElectroMap
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High-Throughput Analysis of Optical Mapping Data Using ElectroMap

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

  • 人工智能在医学中的应用
  • 机器学习用于医疗保健
  • 心脏病学 信息学 信息学

背景情况:

  • 深度神经网络 (DNN) 提供了分析复杂医疗数据和帮助诊断的潜力.
  • DNN的"黑盒子"性质阻碍了理解和临床信任.
  • 可解释的人工智能 (XAI) 方法旨在消除DNN预测的神秘性.

研究的目的:

  • 从心电图 (ECG) 开发使用转移学习进行性别预测的DNN.
  • 评估Grad-CAM热图在ECG分析中解释DNN预测的临床实用性.
  • 评估XAI生成的ECG特征是否对医疗专业人员来说是可解释和有用的.

主要方法:

  • 使用转移学习来构建DNN模型.
  • 电心电图 (ECG) 数据用于性别预测.
  • 使用梯度加权类激活映射 (Grad-CAM) 来生成可视热图,以实现模型的可解释性.
  • 医生对Grad-CAM热图的有用性提供了反.

主要成果:

  • 一个DNN模型成功地开发出来,可以从心电图中预测性别.
  • 格拉德-CAM生成了热图,突出显示了DNN使用的ECG区域.
  • 医生发现Grad-CAM热图在临床上没有用处,也没有帮助人们理解性别预测.
  • 在热图中发现的心电图特征并未被临床医生认为对性别有歧视性.

结论:

  • 目前基于Grad-CAM的XAI方法并不能为基于DNN的ECG的性别预测提供有意义的临床见解.
  • 在这种情况下,开发的XAI方法目前不适合临床应用.
  • 需要在临床诊断中对DNN进行新的,医学上量身定制的解释技术.