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相关概念视频

Disturbances in Heart Rhythm01:29

Disturbances in Heart Rhythm

2.5K
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 heart...
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ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias

442
Arrhythmia is a condition characterized by an irregular heart rhythm, with ECG changes that differ based on its origin and nature. The types of arrhythmias discussed below include atrial, junctional, and ventricular arrhythmias.Atrial ArrhythmiasPremature Atrial Complexes (PACs): PACs are early atrial beats caused by stress, caffeine, alcohol, electrolyte imbalances, hypoxia, hyperthyroidism, or certain medications (e.g., bronchodilators and decongestants). The ECG shows early P waves with an...
442
Dysrhythmias II: Classification of Tachyarrhythmias01:28

Dysrhythmias II: Classification of Tachyarrhythmias

476
Tachyarrhythmias are a type of dysrhythmia where the heart rate exceeds 100 beats per minute. Here are some common types of tachyarrhythmias:Sinus TachycardiaSinus tachycardia originates from increased impulses from the sinus node, leading to an elevated heart rate. It is often triggered by stress, fever, or exercise.Patients may experience palpitations, a sensation of a racing heart, dizziness, and chest discomfort.Causes and Risk Factors: Common causes include physical exertion, emotional...
476

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相关实验视频

Updated: Jan 10, 2026

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
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短期心房的发病预测使用机器学习.

Jean-Marie Grégoire1,2, Cédric Gilon2, François Marelli3

  • 1Cardiology Department, Université de Mons, Avenue Maistriau , 25, Mons 7000, Belgium.

European heart journal. Digital health
|November 21, 2025
PubMed
概括

机器学习模型从心电图分析心率变化,可以提前几个小时预测心房动 (AF). 这使得早期干预策略成为可能,可能减少AF相关的健康问题.

关键词:
心房动是一种心房动.自主神经系统的自主神经系统.深度学习是一种深度学习.心率变化心率的变化.标识 识别 识别 识别机器学习 机器学习预测 预测 预测

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High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation
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High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation

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Catheter Ablation in Combination With Left Atrial Appendage Closure for Atrial Fibrillation
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Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
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High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation
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High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation

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Catheter Ablation in Combination With Left Atrial Appendage Closure for Atrial Fibrillation
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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
  • 数据科学数据科学数据科学

背景情况:

  • 前庭动 (AF) 对健康构成重大风险,早期检测对于预防策略至关重要.
  • 集成到可穿戴设备中的机器学习 (ML) 模型为实时AF预测提供了潜力.
  • 目前的方法往往缺乏及时干预所需的短期预测能力.

研究的目的:

  • 开发和评估ML模型,以使用霍尔特心电图记录预测迫在眉的阳性AF发作.
  • 为了确定在几个小时内会出现AF的鼻节律患者.

主要方法:

  • 分析了95871个霍尔特心电图记录的大数据库,确定了1319个AF事件.
  • 深度学习 (DL) 模型是使用原始心电图数据进行训练的.
  • 传统的ML模型,包括随机森林和XGBoost,被训练使用心率变化 (HRV) 参数进行比较.

主要成果:

  • 使用HRV参数的决策树模型显示出卓越的预测性能.
  • XGBoost模型实现了0.919的ROC曲线下的面积,用于预测持续超过5分钟的AF情节.
  • 在表现最好的模型中,报告了高精度 (84.5%),灵敏度 (83.0%) 和特异性 (86.6%).

结论:

  • HRV参数对于短期AF发病预测至关重要,支持预防性医疗保健.
  • 将这些预测模型集成到mHealth技术中可以实现AF管理的"口袋里的药丸"方法.
  • 需要进一步的前性研究来验证这些发现及其临床实用性.