Jove
Visualize
联系我们

相关概念视频

Electrocardiogram01:29

Electrocardiogram

7.4K
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...
7.4K
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

14.1K
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...
14.1K
Instrumentation Amplifier01:25

Instrumentation Amplifier

1.2K
An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
To overcome this challenge, an ECG machine utilizes an instrumentation amplifier. This specialized amplifier is...
1.2K

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

When AI and Experts Agree on Error: Intrinsic Ambiguity in Dermatoscopic Images.

Journal of imaging·2026
Same author

Oncometabolite signatures from tumor-stroma crosstalk as potential non-invasive biomarkers.

Cell death discovery·2026
Same author

Gated Attention-Augmented Double U-Net for White Blood Cell Segmentation.

Journal of imaging·2025
Same author

Synchronous Acquisition and Processing of Electro- and Phono-Cardiogram Signals for Accurate Systolic Times' Measurement in Heart Disease Diagnosis and Monitoring.

Sensors (Basel, Switzerland)·2025
Same author

An Innovative IoT and Edge Intelligence Framework for Monitoring Elderly People Using Anomaly Detection on Data from Non-Wearable Sensors.

Sensors (Basel, Switzerland)·2025
Same author

Vision transformer distillation for enhanced gastrointestinal abnormality recognition in wireless capsule endoscopy images.

Journal of medical imaging (Bellingham, Wash.)·2025
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
Same journal

Three-Dimensional Modeling and Performance Analysis of Dynamic mmWave V2I Networks Based on Stochastic Geometry.

Sensors (Basel, Switzerland)·2026
查看所有相关文章
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

相关实验视频

Updated: Mar 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

3.3K

稀有时间自动编码器用于ECG异常检测.

Radia Daci1, Abdelmalik Taleb-Ahmed2, Luigi Patrono3

  • 1Institute of Applied Sciences and Intelligent Systems, Consiglio Nazionale delle Ricerche, 73100 Lecce, Italy.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
概括
此摘要是机器生成的。

本研究引入了一种稀疏时空自编码器 (STAE),用于无监督心电图 (ECG) 异常检测. 这种新方法只使用正常数据准确识别异常的心电图信号,实现了最先进的性能.

关键词:
时间卷积网络 时间卷积网络电心电图 (ECG) 是一种心电图.稀疏的注意力注意力很少.没有监督的异常检测检测.

相关实验视频

Last Updated: Mar 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

3.3K

科学领域:

  • 生物医学工程 生物医学工程
  • 医疗保健中的人工智能
  • 心脏病学 心脏病学

背景情况:

  • 电心电图 (ECG) 分析对于诊断心脏病状况至关重要.
  • 由于信号的复杂性和可变性,区分正常和异常的心电图信号是具有挑战性的.
  • 目前的方法通常需要标记异常数据,限制现实世界的适用性.

研究的目的:

  • 开发一种新的无监督模型,用于准确检测心电图异常.
  • 为了解决传统的ECG分析方法的局限性.
  • 创建一个强大的工具,用于自动化和智能心脏诊断.

主要方法:

  • 提出了一个稀疏时间自编码器 (STAE) 模型.
  • 利用时间卷积网络 (TCN) 来从时间和频率域中分层提取特征.
  • 集成的掩盖信号重建和混合稀疏注意力机制.

主要成果:

  • 在无监督方法中,在PTB-XL数据集上实现了0.872的最高ROC-AUC.
  • 演示了0.009秒的低推断时间.
  • 验证了模型在捕获关键时间和光谱模式方面的有效性.

结论:

  • 该STAE模型为无监督的ECG异常检测提供了最先进的性能.
  • 该模型在正常数据上进行训练的能力使其适合实际部署.
  • STAE显示出在推进自动化心电图分析和心脏诊断方面的巨大潜力.