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

Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

3.7K
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...
3.7K
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

551
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...
551
Classification of Signals01:30

Classification of Signals

424
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
424
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
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

105
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
105
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

605
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....
605

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

Gastric ultrasound reveals no association between prolonged fasting duration and gastric residual volume in pediatric patients.

Scientific reports·2026
Same author

SERS Facemask for Rapid and Portable Sensing Mycobacterium Tuberculosis Antigens for TB Screening.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Therapeutic Effects of Shengdu Pingmu Formula on Loperamide-Induced Constipation in Rats via PI3K/AKT Signaling and Gut Microbiota Regulation.

Journal of visualized experiments : JoVE·2026
Same author

Physio-transcriptomic perspectives on midgut structural disruption and metabolic imbalance underlying neodymium oxide toxicity in Bombyx mori.

Toxicology and applied pharmacology·2026
Same author

Bilateral SERS-Microneedle Patch for Co-Diagnosis of Diabetes Mellitus and Tuberculosis Comorbidity.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

A polysaccharide from Pueraria lobata ameliorates hepatic fibrosis via gut microbiota-dependent suppression of ferroptosis.

Phytomedicine : international journal of phytotherapy and phytopharmacology·2026

相关实验视频

Updated: Jun 16, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

6.9K

为多标签ECG分类提供领先集群的多分支网络.

Feiyan Zhou1, Lingzhi Chen1

  • 1Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, 541004, PR China; Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin, 541004, PR China.

Medical engineering & physics
|August 19, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的多分支深度学习网络,用于分类心电图 (ECG) 信号,通过考虑关系和心脏结构来提高准确性,以便更好地诊断心血管疾病.

关键词:
这是一个ECGECGECGECGECG.以为导向的聚类.多分支网络的多分支网络.多个标签分类的分类.多个尺度的多个尺度.

更多相关视频

Automatic Identification of Dendritic Branches and their Orientation
06:08

Automatic Identification of Dendritic Branches and their Orientation

Published on: September 17, 2021

1.9K
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

1.7K

相关实验视频

Last Updated: Jun 16, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

6.9K
Automatic Identification of Dendritic Branches and their Orientation
06:08

Automatic Identification of Dendritic Branches and their Orientation

Published on: September 17, 2021

1.9K
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

1.7K

科学领域:

  • 心脏病学 心脏病学
  • 人工智能的人工智能
  • 医疗信号处理 医疗信号处理

背景情况:

  • 12导电心电图 (ECG) 对于诊断心血管疾病至关重要.
  • 深度学习模型显示了自动ECG信号分类的前景.
  • 当前的方法在心电图分析中往往忽略了内在的关系和心脏结构.

研究的目的:

  • 开发一种深度学习模型,更好地利用医学领域的知识进行多标签心电图分类.
  • 引入有效的领先分组策略和多分支网络架构.
  • 通过整合不同线路的特征来提高自动ECG分类的准确性.

主要方法:

  • 提出了一个多分支网络,每个分支使用多个规模的卷积结构来提取特定组合的特征.
  • 引入了一个特征加权的融合模块,以整合来自不同网络分支的信息.
  • 对PTB-XL和CPSC2018数据集的模型进行了评估,以对心律失常进行分类.

主要成果:

  • 拟议的多部门网络在多标签心电图分类任务上表现优于最先进的方法.
  • 领先的分组策略和特征融合模块有效地提高了分类准确性.
  • 该模型成功地分类了不同数据集中的多种心律失常类型和正常节奏.

结论:

  • 这种新型的多分支网络有效地利用了心电图的领先关系和心脏结构,以改进分类.
  • 这种方法提供了一种更复杂的方法,用于在临床环境中进行自动化心电图分析.
  • 这些发现表明,深度学习在心血管诊断中的应用是一个有希望的方向.