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相关概念视频

Heart Sounds01:15

Heart Sounds

1.7K
Heart sounds are generated by the turbulence in blood flow due to the closing of heart valves. These sounds are best perceived slightly away from the valves, where the blood flow disseminates the sound.
Auscultation is the process of listening to these internal body sounds using a stethoscope. The heart produces four types of sounds, but only two—S1 and S2—can usually be heard with a stethoscope.
S1, also known as the "lub" sound, is caused by the closure of atrioventricular (A-V)...
1.7K
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

474
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...
474
Electrocardiogram01:29

Electrocardiogram

2.0K
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...
2.0K
Classification of Signals01:30

Classification of Signals

374
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...
374
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

3.2K
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...
3.2K
Electrophysiology of Normal Cardiac Rhythm01:19

Electrophysiology of Normal Cardiac Rhythm

1.9K
The normal cardiac rhythm is a synchronized electrical activity that facilitates the regular and coordinated contraction of the heart muscle. This process is essential for efficient blood circulation throughout the body. The fundamental elements involved in establishing and maintaining this rhythm include the unique electrical properties of cardiac muscle cells, the sinoatrial (SA) node's pacemaker function, the specialized conducting system, and the ionic mechanisms underlying each phase...
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相关实验视频

Updated: May 24, 2025

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
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Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

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可解释的心声和心电图分类的多模式深度学习.

Bruno Oliveira, Andre Lobo, Catia Isabel Costa

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
    概括
    此摘要是机器生成的。

    这项研究评估了五种分类心脏声音和心电图的模型. 一个早期的融合多式模式模型提高了性能,重点关注临床相关的ECG和PCG特征.

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    Semi-automated Optical Heartbeat Analysis of Small Hearts
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    Semi-automated Optical Heartbeat Analysis of Small Hearts

    Published on: September 16, 2009

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    相关实验视频

    Last Updated: May 24, 2025

    Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
    08:22

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    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
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    Semi-automated Optical Heartbeat Analysis of Small Hearts
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    科学领域:

    • 心脏病学 心脏病学
    • 生物医学工程 生物医学工程
    • 人工智能的人工智能

    背景情况:

    • 对心脏病的准确分类对于患者的治疗结果至关重要.
    • 对心电图 (PCG) 和心电图 (ECG) 的多式分析为改善诊断准确性提供了潜力.
    • 深度学习模型在分析复杂的生理信号方面表现有前途.

    研究的目的:

    • 评估五种不同的深度学习模型对同步心声和心电图的二元分类 (正常/异常) 的性能.
    • 评估不同模型架构的有效性,包括1D-CNN,2D-CNN和多式融合方法 (早期和晚期融合).
    • 使用梯度加权类激活映射 (Grad-CAM) 来解释模型决策并识别临床相关特征.

    主要方法:

    • 开发并比较了五种模型:ECG 1D-CNN,PCG 2D-CNN,ECG 2D-CNN,早期融合多式模式和晚期融合多式模式.
    • 应用Grad-CAM可视化和分析ECG和PCG信号中的感兴趣区域,这些信号有助于分类决策.
    • 使用ROC-AUC和F1-score等指标评估模型性能.

    主要成果:

    • 早期的融合多式模式模型与单个信号模型 (ECG 1D-CNN: 0.79,PCG 2D-CNN: 0.79) 相比,显示出更好的性能 (ROC-AUC 0.81).
    • 电图2D-CNN实现了较高的ROC-AUC (0.82),但F1得分较低 (0.85),而不是早期的融合模型 (0.86).
    • 格拉德-CAM分析显示,模型专注于临床意义上的ECG组件 (QRS复合物,T/P波) 和PCG声音 (S1,S2) 进行分类.

    结论:

    • 同步ECG和PCG信号的早期融合提高了对心脏病的二元分类性能.
    • 虽然ECG 2D-CNN显示出高分辨能力,但多式联接在准确性和稳定性方面提供了平衡的性能.
    • Grad-CAM证实,深度学习模型学会识别心脏信号中的诊断相关特征,与临床专业知识保持一致.