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

Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

1.4K
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...
1.4K
Electrocardiogram01:29

Electrocardiogram

5.4K
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...
5.4K

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

Conceptual thermal constraints on the growth of the first tree on a terraformed Mars.

PloS one·2026
Same author

A hybrid approach for forecasting peak expiratory flow rate in asthma patients using combined linear regression and random forest model.

PloS one·2025
Same author

Utilizing IoT Sensors and Spatial Data Mining for Analysis of Urban Space Actors' Behavior in University Campus Space Design.

Sensors (Basel, Switzerland)·2025
Same author

Pre-Processing techniques and artificial intelligence algorithms for electrocardiogram (ECG) signals analysis: A comprehensive review.

Computers in biology and medicine·2024
Same author

A Denoising and Fourier Transformation-Based Spectrograms in ECG Classification Using Convolutional Neural Network.

Sensors (Basel, Switzerland)·2022
Same author

Using multiagent modeling to forecast the spatiotemporal development of the COVID-19 pandemic in Poland.

Scientific reports·2022

相关实验视频

Updated: Jan 15, 2026

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

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

Published on: April 26, 2024

3.0K

一个集成的算法,用于单线电心电图信号分析,使用深度学习和12线数据分析.

Muhammad Farhan Safdar1, Robert Marek Nowak2, Piotr Pałka2

  • 1Faculty of Electronics and Information Technology, Warsaw University of Technology, Warsaw, Poland. mfarhan166@gmail.com.

Scientific reports
|October 7, 2025
PubMed
概括

这项研究开发了一种新的AI模型,用于改进使用12个导线心电图数据的单导线心电图 (SL-ECG) 分析. 该模型达到82%以上的准确性,增强了对心脏病的智能设备诊断.

关键词:
深度学习是一种深度学习.诊断信号 诊断信号 诊断信号电心电图 (ECG) 是一种心电图.医疗保健可穿戴技术 医疗保健可穿戴技术神经网络的神经网络

更多相关视频

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
06:07

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

4.3K
Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
05:03

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

Published on: December 11, 2019

9.0K

相关实验视频

Last Updated: Jan 15, 2026

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

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

Published on: April 26, 2024

3.0K
Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
06:07

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

4.3K
Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
05:03

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

Published on: December 11, 2019

9.0K

科学领域:

  • 生物医学工程 生物医学工程
  • 人工智能的人工智能
  • 心脏病学 心脏病学

背景情况:

  • 人工智能 (AI) 在分析12导电心电图 (ECG) 信号方面表现出色.
  • 人们越来越感兴趣在智能设备上使用单导电心电图 (SL-ECG) 来诊断心脏病.
  • 有限的公开SL-ECG数据集阻碍了可靠的AI模型开发.

研究的目的:

  • 引入一种新的方法,用于训练AI模型在SL-ECG数据上,使用易于获得的12ECG数据集.
  • 开发一种能够翻译SL-ECG数据的层次模型架构,以便与12导信号兼容.
  • 用智能设备提高人工智能驱动的心脏功能障碍诊断的可靠性.

主要方法:

  • 提出了一种具有三个翻译层的顺序卷积神经网络模型.
  • 该模型被训练在单独的12-lead临床心电图数据上,以改善SL-ECG分类.
  • 对PTB-XL,2017年心脏病计算挑战和2018年中国生理信号挑战数据集进行了实验.
  • 评估了除技术和极性的变化.

主要成果:

  • 该模型在未见的SL-ECG信号上实现了超过82%的测试准确性.
  • 接收器运行特征曲线下的面积为0.81,在I上训练时具有76.60%的灵敏度和83.44%的特异性.
  • 领先II,V4和V5显示了有效的AI模型培训的潜力.

结论:

  • 拟议的方法有效地弥合了SL-ECG数据可用于AI模型培训的差距.
  • 这一进步支持开发更智能的诊断设备,并帮助临床医生评估心脏异常.
  • 该模型展示了利用12导电图数据进行可靠的SL-ECG分析的可行性.