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

552
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...
552
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

3.9K
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...
3.9K
Classification of Systems-I01:26

Classification of Systems-I

178
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
178
Classification of Signals01:30

Classification of Signals

427
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...
427
Classification of Systems-II01:31

Classification of Systems-II

138
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
138

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

Optimizing Trust and Safety Regions for Text-to-Image Generation in High-Dimensional Manifold Spaces.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Advanced Acoustic Monitoring Using Psychoacoustic Heatmap Machine Learning Models for Noise Impact Prediction in Air-Conditioned Building Environments.

Sensors (Basel, Switzerland)·2026
Same author

LMOD: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Models.

Findings of ACL. NAACL·2026
Same author

Automating expert-level medical reasoning evaluation of large language models.

NPJ digital medicine·2025
Same author

AdaGCL+: An Adaptive Subgraph Contrastive Learning Toward Tackling Topological Bias.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Surviving ChatGPT in healthcare.

Frontiers in radiology·2024
Same journal

Aggregating global-scale pixel-wise forgery cues within a graph.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Finite-Time intermittent control for secure synchronization of Neutral-Type stochastic delayed neural networks under aperiodic DoS attacks.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

FedCAD: Cross-modal semantic alignment and distillation for cross-domain heterogeneous federated learning.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Partial-encryption-decryption-based secure state estimation of singularly perturbed complex networks: A Paillier encryption approach.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

ResVaRe: Parameter-efficient fine-tuning for large language models via cross-layer residual vector adaptation and representation editing.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Brain network construction and analysis for epilepsy: A methodology review.

Neural networks : the official journal of the International Neural Network Society·2026
查看所有相关文章

相关实验视频

Updated: Jun 18, 2025

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

通过领域知识驱动的对比学习来进行开放世界的心电图分类.

Shuang Zhou1, Xiao Huang1, Ninghao Liu2

  • 1Department of Computing, Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China.

Neural networks : the official journal of the International Neural Network Society
|July 28, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了开放世界的心电图 (ECG) 分类,以识别已知和未知的心脏病类型. 这种新的方法提高了未见的心电图类的诊断准确性,提高了自动诊断的可靠性.

关键词:
相反的学习学习.深度学习是一种深度学习.域名知识 域名知识在ECG分类中使用ECG分类.开放世界的学习学习.

更多相关视频

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K
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

3.6K

相关实验视频

Last Updated: Jun 18, 2025

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
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K
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

3.6K

科学领域:

  • 心脏病学 心脏病学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 自动心电图 (ECG) 分类有助于诊断疾病,但在仅涵盖特定心电图类型的有限培训数据方面存在困难.
  • 传统模型无法识别现实数据中存在的未见或未知的心电图类型.
  • 这种限制阻碍了自动诊断工具在各种临床场景中的可靠性.

研究的目的:

  • 为了应对在开放世界的环境中分类有限的已知的ECG类型和识别未知的ECG类型的挑战.
  • 开发一种新的开放世界心电图分类方法,以提高观察到和未观察到的心电图类别的识别.
  • 提高自动化心电图诊断系统的稳定性和可靠性.

主要方法:

  • 提出了一个定制的方法,将临床知识整合到对比学习中.
  • 生成"硬负"样本,以指导可区分的诊断心电图特征的学习.
  • 采用多个超球体学习来进行紧的心电图表示和分类.

主要成果:

  • 拟议的方法在12个的ECG数据集上与最先进的方法相比显示出更高的性能 (CPSC2018,PTB-XL,格鲁吉亚).
  • 在识别未见的ECG类和某些可见的类方面取得了更高的准确性.
  • 优于比较方法,突出其在开放世界场景中的有效性.

结论:

  • 开放世界心电图分类对于提高自动心电图诊断的可靠性至关重要.
  • 拟议的方法有效地解决了分类已知的和未知的ECG类型的挑战.
  • 这些发现有助于更强大的和临床上适用的自动化心电图分析工具.