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

471
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...
471
Instrumentation Amplifier01:25

Instrumentation Amplifier

412
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...
412
Electrocardiogram01:29

Electrocardiogram

1.9K
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...
1.9K

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

The optical origin of the human skin color 'banana' in CIELAB space.

bioRxiv : the preprint server for biology·2026
Same author

Treatment times for rural and urban patients with aneurysmal subarachnoid hemorrhage in a provincial hub-and-spoke network.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia·2026
Same author

The Evolving Clinical Utility of Gene Expression Profiling in Cutaneous Melanoma.

Journal of the National Comprehensive Cancer Network : JNCCN·2026
Same author

Melanoma: Cutaneous, Version 2.2026, NCCN Clinical Practice Guidelines In Oncology.

Journal of the National Comprehensive Cancer Network : JNCCN·2026
Same author

Performance of a large language model in the informed consent process for participation in a clinical trial.

NPJ digital medicine·2026
Same author

Rapid Sequence Induction Practices and Outcomes in Abdominal Surgery Patients: A Multicenter Observational Cohort Study.

Anesthesiology·2026
Same journal

Poisoning the Genome: Targeted Backdoor Attacks on DNA Foundation Models.

ArXiv·2026
Same journal

Mechanistic mathematical model of the in vitro infection dynamics of Bunyamwera and Batai viruses including MOI-dependent shortening of the eclipse phase.

ArXiv·2026
Same journal

AI-Driven Lumped-Element Modeling of Human Respiratory System for Studying Voice Mechanics.

ArXiv·2026
Same journal

Beyond Algorithms: Conceptual Innovation in Medical Imaging AI.

ArXiv·2026
Same journal

Feynman Kac Reweighted Schrödinger Bridge Matching for Surface-Based Tau PET Harmonization.

ArXiv·2026
Same journal

Agentic Discovery of Non-Canonical Antimicrobial Peptides with AMPGAN v3.

ArXiv·2026
查看所有相关文章

相关实验视频

Updated: May 22, 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

1.6K

ProtoECGNet:基于案例的可解释深度学习,用于多标签心电图分类与对比学习.

Sahil Sethi, David Chen, Thomas Statchen

    ArXiv
    |May 21, 2025
    PubMed
    概括
    此摘要是机器生成的。

    ProtoECGNet 为心电图 (ECG) 分类提供透明的深度学习. 这种基于原型的模型提供了忠实,基于案例的解释,提高了对AI在临床决策支持方面的信任.

    更多相关视频

    Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy
    11:54

    Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy

    Published on: January 29, 2018

    25.3K
    Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
    11:25

    Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

    Published on: July 26, 2013

    43.2K

    相关实验视频

    Last Updated: May 22, 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

    1.6K
    Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy
    11:54

    Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy

    Published on: January 29, 2018

    25.3K
    Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
    11:25

    Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

    Published on: July 26, 2013

    43.2K

    科学领域:

    • 人工智能在医学中的应用
    • 生物医学信号处理
    • 机器学习用于医疗保健

    背景情况:

    • 对于心电图 (ECG) 分类的深度学习模型实现了高性能,但缺乏临床透明度.
    • 像突出地图这样的后期解释方法可能不能准确地代表模型决策过程.
    • 基于原型的推理提供了一个透明的替代方案,通过将决策与已学习的ECG分段表示联系起来.

    研究的目的:

    • 引入ProtoECGNet,一种基于原型的深度学习模型,用于可解释的多标签心电图分类.
    • 开发一个模型,为临床决策支持提供忠实,基于案例的解释.
    • 通过透明的ECG分析,使医疗保健中可靠的AI成为可能.

    主要方法:

    • 开发了ProtoECGNet,这是一个多分支深度学习架构,将1D和2D CNN与全球和时间本地化的原型集成在一起.
    • 采用了结构化的原型损失函数用于多标签学习,包括集群,分离,多样性和新鲜的对比损失.
    • 评估了PTB-XL数据集的性能,评估了所有71个诊断标签.

    主要成果:

    • 与多标签心电图分类上的最先进的黑盒模型相比,ProtoECGNet取得了竞争性表现.
    • 该模型提供了结构化,基于案例的解释,提高了可解释性.
    • 临床审查证实,该模型的原型具有代表性和清晰性,验证了它们的质量.

    结论:

    • 原型学习对于复杂的,多标签的时间序列分类任务,如心电图分析是有效的.
    • ProtoECGNet提供了一种实际的方法,用于建立透明和值得信赖的深度学习模型,用于临床决策支持.
    • 该模型的可解释性促进了临床采用,并增强了对人工智能驱动的诊断工具的信心.