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

2.3K
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...
2.3K
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

580
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...
580

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

An AI-Driven Interpretable Multiview Feature Learning Approach for EEG Based Epileptic Seizure Detection.

IEEE journal of biomedical and health informatics·2025
Same author

Utilization of a hierarchical electrocardiogram classification model for enhanced biometric identification.

Computers in biology and medicine·2024
Same author

Automated classification of liver fibrosis stages using ultrasound imaging.

BMC medical imaging·2024
Same author

System for automatically assessing the likelihood of inferior alveolar nerve injury.

Computers in biology and medicine·2024
Same author

Multi-modal Biometrics Based Implicit Driver Identification System Using Multi-TF Images of ECG and EMG.

Computers in biology and medicine·2023
Same author

Real-Time Context-Aware Recommendation System for Tourism.

Sensors (Basel, Switzerland)·2023
Same journal

Rapid personalisation of cardiovascular models using invasively measured right ventricular pressure.

Computers in biology and medicine·2026
Same journal

Biologically inspired mechanisms for enhancing robustness in EEG signal modeling: Challenges, opportunities, and perspectives.

Computers in biology and medicine·2026
Same journal

Machine learning-based detection of missed inspiratory efforts using esophageal pressure during noisy pressure support ventilation.

Computers in biology and medicine·2026
Same journal

A computational model of chemically- and mechanically-induced thrombus formation in cerebral aneurysms.

Computers in biology and medicine·2026
Same journal

An improved catch fish optimization based deep learning model for Parkinson disease classification using EEG signal.

Computers in biology and medicine·2026
Same journal

Assessing the robustness of evaluation metrics for synthetic ECG signal quality.

Computers in biology and medicine·2026
查看所有相关文章

相关实验视频

Updated: Jun 30, 2025

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

537

基于数据生成网络和非信托数据处理的电心图识别.

Ziyang Gong1, Zhenyu Tang2, Zijian Qin2

  • 1Department of Computer Engineering, Gachon University, Seongnam-si, 13120, Republic of Korea.

Computers in biology and medicine
|March 24, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种使用心电图 (ECG) 信号进行快速准确的生物识别识别的新方法. 这种方法显著降低了错误识别率,提高了基于ECG的身份验证的可靠性.

关键词:
扩散生成网络是一个扩散生成网络.这是一个ECGECGECGECGECG.基于ECG的身份识别功能电心电图 (ECG) 是一种心电图.生成性网络 生成性网络波段变换 消噪 波段变换 消噪

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

3.7K
Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI
10:35

Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI

Published on: June 3, 2013

32.7K

相关实验视频

Last Updated: Jun 30, 2025

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

537
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

3.7K
Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI
10:35

Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI

Published on: June 3, 2013

32.7K

科学领域:

  • 生物识别信息 生物识别信息
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 生物信号越来越多地用于身份识别.
  • 准确和快速的生物识别方法对组织至关重要.
  • 心电图 (ECG) 信号提供了一个有前途的生物识别模式.

研究的目的:

  • 开发一种新的算法,用于预处理心电图 (ECG) 数据以进行身份识别.
  • 提高基于心电图的生物识别系统的准确性和速度.
  • 通过数据增强来提高分类网络的性能.

主要方法:

  • 一个线性心电图数据预处理算法,使用卡尔曼波器来降低噪音.
  • 实施数据生成战略网络 (DRCN) 以增强培训数据.
  • 将DRCN与卷积分类网络集成.

主要成果:

  • 在基于ECG的身份识别中,平均错误识别率为2.5%.
  • 每个类别的平均认可率为98.7%.
  • 与之前的ECG身份识别方法相比,显著改进.

结论:

  • 拟议的方法为基于ECG的身份识别提供了一个快速而准确的解决方案.
  • DRCN有效地增加了数据,提高了分类性能.
  • 这种方法有可能在生物识别安全中得到广泛应用.