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相关概念视频

ECG Interpretation of Arrhythmias I: Sinus Arrhythmias01:16

ECG Interpretation of Arrhythmias I: Sinus Arrhythmias

164
Arrhythmias are disturbances in the heart's rhythm that lead to abnormal heartbeats. These irregularities can originate from different parts of the heart and are classified based on their origin and nature.
Types of Arrhythmias
Sinus Node Arrhythmias
Sinus Bradycardia: Originating from the sinoatrial (SA) node, sinus bradycardia involves slower impulses, resulting in a heart rate of less than 60 beats per minute (bpm). Causes include sleep, vagal stimulation, beta-blockers, hypothyroidism,...
164
Disturbances in Heart Rhythm01:28

Disturbances in Heart Rhythm

857
Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
Arrhythmias are categorized by their speed, rhythm, and origin. A slow...
857
Mechanism of Cardiac Arrhythmias01:28

Mechanism of Cardiac Arrhythmias

871
Arrhythmias are irregular heart rhythms occurring when the heart's electrical impulses become abnormal. These disturbances can lead to various symptoms, depending on their severity and the underlying cause. Some common factors contributing to arrhythmias include hypoxia, ischemia, electrolyte imbalances, excessive catecholamine exposure, drug toxicity, and muscle overstretching. Arrhythmias can be classified into two main types based on the rate and site of origin of abnormal heart rhythms.
871
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
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

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相关实验视频

Updated: May 24, 2025

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

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Published on: January 8, 2013

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一个混合GCN-LSTM模型用于基于心电图样式相似性的心室失常症分类.

Qing Lin, Dino Oglic, Hak-Keung Lam

    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

    这项研究引入了一个新的深度学习模型,将图形卷积网络 (GCN) 和长短期记忆 (LSTM) 结合起来,以改进心律不整的检测. GCN-LSTM模型准确地区分了心室高心率 (VT) 和心室动 (VF) 与其他心律.

    更多相关视频

    Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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    Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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    Published on: April 11, 2025

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    相关实验视频

    Last Updated: May 24, 2025

    Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
    12:09

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    Published on: January 8, 2013

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    Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
    06:07

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    Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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    科学领域:

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

    背景情况:

    • 对于患者的治疗结果来说,精确区分心室高心率 (VT) 和心室动 (VF) 非常重要.
    • 传统的心电图 (ECG) 分析依赖于手动的特征提取,这可能耗时且容易出现错误.
    • 深度学习模型提供了自动化的心律失常识别,超越了传统方法.

    研究的目的:

    • 开发一种先进的深度学习模型,用于对心律失常的自动分类.
    • 为了提高区分VT和VF与非心室节律的准确性.
    • 利用基于图形的学习来捕捉心电图数据中的时间依赖性.

    主要方法:

    • 开发了一种新型模型,将图形卷积网络 (GCN) 与长短期内存 (LSTM) 网络合并.
    • 使用可训练加权的e-邻近图表来建模ECG时间序列段内的相似性.
    • 该模型经过训练并对其在VT,VF和非心室节律上的分类性能进行了评估.

    主要成果:

    • GCN-LSTM模型在分类VT,VF和非心室节律方面取得了实质性的改进.
    • 这种方法有效地捕捉到ECG细分市场内的复杂模式和相似之处.
    • 该模型在准确性和自动化方面超过了传统的ECG分析方法.

    结论:

    • 开发的GCN-LSTM模型为心律失常检测提供了高度准确和自动化的解决方案.
    • 这种深度学习方法提高了区分像VT和VF这样的关键心律失常的能力.
    • 这些发现突显了基于图形的深度学习在心血管信号分析中的潜力.