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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

215
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,...
215
Electrocardiogram01:29

Electrocardiogram

2.3K
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...
2.3K
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

599
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...
599
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

5.3K
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...
5.3K
Electrophysiology of Normal Cardiac Rhythm01:19

Electrophysiology of Normal Cardiac Rhythm

4.3K
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...
4.3K
Disturbances in Heart Rhythm01:28

Disturbances in Heart Rhythm

956
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...
956

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相关实验视频

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Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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使用深度卷积神经网络的非节律心电图信号来识别人.

Awabed Al-Jibreen1, Saad Al-Ahmadi2, Saiful Islam3

  • 1Computer Science Department, College of Computer and Information Sciences, King Saud University, 11543, Riyadh, Saudi Arabia. 439204595@student.ksu.edu.sa.

Scientific reports
|February 23, 2024
PubMed
概括

这项研究探讨了心律不规则 (心律失常) 如何影响心电图 (ECG) 生物识别. 一个新的轻量级深度学习模型在识别即使有心律失常的个体方面也实现了高准确度.

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Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy
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Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
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Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

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相关实验视频

Last Updated: Jul 2, 2025

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Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy
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科学领域:

  • 生物识别信息 生物识别信息
  • 心脏病学 心脏病学
  • 机器学习 机器学习

背景情况:

  • 电心电图 (ECG) 生物识别越来越多地用于识别.
  • 大多数心电图生物识别系统都会忽略个体健康状况和心律失常.
  • 健康状况注释的心电图数据对于可靠的识别至关重要.

研究的目的:

  • 调查心律失常对基于心电图的人身份识别的影响.
  • 提出一种新的,高效的深度学习模型,用于心律失常意识的心电图生物识别.
  • 为了评估模型在各种心律失常类型的表现.

主要方法:

  • 开发了一种轻量级的卷积神经网络 (CNN),使用深度可分离卷积 (DWSC).
  • 利用了MIT-BIH数据集,对健康状况和九种心律失常类型进行了注释.
  • 测试了系统区分正常心跳和心律失常心跳重叠的个体的能力.

主要成果:

  • 对于正常心跳达到99.28%的准确性,对于心律不整的心跳达到93.81%.
  • 证明不同类型心律失常对生物识别的不同影响.
  • 在基于心电图的人身份识别的平均准确性方面,超越了现有的模型.

结论:

  • 基于心电图的生物识别系统受到心律失常的显著影响.
  • 拟议的基于DWSC的CNN模型为心律失常意识的识别提供了高准确性和效率.
  • 考虑健康状况注释对于开发可靠的心电图生物识别系统至关重要.