Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Electrocardiogram01:29

Electrocardiogram

3.2K
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...
3.2K
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

8.4K
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...
8.4K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Prediction of appropriate implantable cardioverter-defibrillator therapy using machine learning and routinely available clinical data.

European heart journal. Digital health·2026
Same author

Right ventricular dysfunction: an overlooked predictor of sudden cardiac death and arrhythmic events-a meta-analysis.

Clinical research in cardiology : official journal of the German Cardiac Society·2026
Same author

[AI-Assisted ECG diagnostics : Classical test statistics still apply].

Herzschrittmachertherapie & Elektrophysiologie·2026
Same author

Genetic cardiomyopathy unmasked by pregnancy: X-linked dystrophinopathy presenting as peripartum cardiomyopathy-a case report.

European heart journal. Case reports·2026
Same author

Ventricular Fibrillation Without Hemodynamic Deterioration: An Electrocardiographic Paradox Explained by Heterotopic Heart Transplantation.

JACC. Case reports·2026
Same author

MIMIC-III-Ext-PPG, a PPG-based Benchmark Dataset for Cardiovascular and Respiratory Signal Analysis.

Scientific data·2026

相关实验视频

Updated: Sep 13, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

可解释的机器学习通过心电图诊断瘤:一个外部验证的研究.

Juan Miguel Lopez Alcaraz1, Wilhelm Haverkamp2, Nils Strodthoff3

  • 1AI4Health Division, Carl von Ossietzky Universität Oldenburg, Ammerländer Heerstraße 114-118, Oldenburg, Lower Saxony, 26129, Germany.

Cardio-oncology (London, England)
|July 27, 2025
PubMed
概括

这项研究表明,心电图 (ECG) 数据与机器学习相结合,可以非侵入性地诊断瘤. 这种具有成本效益的方法可以识别与癌症有关的心血管变化,改善早期检测,特别是在资源有限的环境中.

关键词:
电心电图 (ECG) 是一种心电图.可解释的人工智能 (XAI)机器学习 机器学习新生体诊断新生体的诊断

更多相关视频

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

941

相关实验视频

Last Updated: Sep 13, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

941

科学领域:

  • 心血管医学 心血管医学
  • 在瘤学瘤学.
  • 医疗保健中的人工智能

背景情况:

  • 新生体是全球主要的死亡原因,需要早期和可访问的诊断工具.
  • 目前用于瘤的诊断方法往往是侵入性的,昂贵的,并且无法广泛获得.
  • 电心电图 (ECG) 数据为瘤检测提供了一个非侵入性的,广泛可用的替代方案.

研究的目的:

  • 探索ECG数据对于非侵入性瘤诊断的潜力.
  • 开发和验证一种机器学习模型,用于使用心电图信号识别瘤.
  • 调查与瘤存在和治疗相关的心血管变化.

主要方法:

  • 开发了一个诊断管道,集成基于树的机器学习模型和Shapley价值分析以进行可解释性.
  • 该模型在大型数据集上进行了严格的内部验证,并在独立队列上进行了外部验证.
  • 确定了驱动诊断预测的关键心电图特征,并分析了临床相关性.

主要成果:

  • 开发的模型在内部和外部验证队列中都显示出高的诊断准确性.
  • 沙普利值分析发现了重要的心电图特征,包括新型瘤的新型预测因素.
  • 这种方法被证明是具有成本效益,可扩展性和适合资源有限的设置.

结论:

  • 该研究证实了使用心电图信号和机器学习用于非侵入性瘤诊断的可行性.
  • 该方法提供了对复杂的心脏-新细胞相互作用的可解释的见解.
  • 这种方法可以解决诊断缺口,并纳入现有的诊断和治疗框架.