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相关概念视频

Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

315
Dysrhythmias, also known as arrhythmias, are disturbances in the heart's rhythm that range from benign to life-threatening. A thorough evaluation is crucial for appropriate management and involves a comprehensive medical history, physical examination, and various diagnostic tests.Medical HistorySymptoms: Collect detailed information on palpitations, dizziness, syncope, chest pain, and fatigue. Note their onset, frequency, and triggers.Previous Cardiac Issues: Document any history of heart...
315
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

445
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...
445
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

11.6K
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...
11.6K
Mechanism of Cardiac Arrhythmias01:28

Mechanism of Cardiac Arrhythmias

1.6K
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.
1.6K
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

1.4K
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...
1.4K

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Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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一个可解释的深度学习框架,用于从心电图信号中可靠地检测心律失常.

Md Alamin Talukder1, Amira Samy Talaat2, Nusrat Jahan Muna3

  • 1Department of Computer Science and Engineering, International University of Business Agriculture and Technology, Dhaka, Bangladesh. alamin.cse@iubat.edu.

Scientific reports
|November 11, 2025
PubMed
概括

这项研究引入了一个可解释的深度学习框架,用于从心电图信号中准确检测心律失常. 该模型实现了高精度,同时提供了可解释的见解,增强了对AI诊断的临床信任.

关键词:
检测心律失常的检测方式卷积神经网络 (CNN) 是一种神经网络.数据平衡 (ROS) 是指数据的平衡.深度学习 (Deep Learning) 是一种深度学习.电心电图 (ECG) 是一种心电图.可解释的人工智能 (XAI)

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科学领域:

  • 生物医学工程 生物医学工程
  • 人工智能在医学中的应用
  • 心脏病学 心脏病学

背景情况:

  • 心血管疾病 (CVD) 是一个主要的全球健康问题,心律失常会增加死亡率和发病率.
  • 从心电图 (ECG) 信号中准确检测心律失常是至关重要的,但由于数据的复杂性而具有挑战性.
  • 目前用于ECG分析的深度学习 (DL) 模型缺乏解释性,并面临采用障碍.

研究的目的:

  • 开发一个可解释的深度学习 (DL) 框架,以准确可靠地检测心律失常.
  • 提高EDG分析中DL模型的可解释性,以便在临床上采用.
  • 通过先进的数据平衡技术,提高DL模型的概括性和性能.

主要方法:

  • 卷积神经网络 (CNN) 和密集神经网络 (DNN) 架构的集成.
  • 实施一个多阶段的管道,包括数据准备,信号预处理和多策略数据平衡 (ADASYN,SMOTE,SMOTETomek,随机过量采样).
  • 纳入可解释的人工智能 (XAI) 方法 (SHAP,LIME,特征重要性分析) 以实现模型透明度.

主要成果:

  • 随机过量采样与CNN (ROS+CNN) 模型相结合,实现了高分类准确率:99.74% (MITDB),99.43% (PTBDB) 和99.98% (NSTDB).
  • 该框架在基准ECG数据集上表现出卓越的表现.
  • XAI组件为模型的决策过程提供了可操作的见解.

结论:

  • 开发的可解释DL框架提供了准确可靠的心律失常检测.
  • 集成XAI促进了临床信任,并促进了AI在心血管诊断中的采用.
  • 这种方法为心脏病学中更具影响力的AI驱动解决方案铺平了道路.