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相关概念视频

Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

549
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...
549
ECG Interpretation of Arrhythmias I: Sinus Arrhythmias01:16

ECG Interpretation of Arrhythmias I: Sinus Arrhythmias

200
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,...
200
Electrocardiogram01:29

Electrocardiogram

2.2K
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...
2.2K
Mechanism of Cardiac Arrhythmias01:28

Mechanism of Cardiac Arrhythmias

901
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.
901
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

593
An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
ECG waveforms are divided by vertical and horizontal lines at standard intervals.
The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage....
593
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

3.6K
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...
3.6K

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相关实验视频

Updated: Jun 14, 2025

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

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一种基于图表的心律失常分类方法,使用单导电图记录.

Dorsa EPMoghaddam1, Ananya Muguli1, Mehdi Razavi2

  • 1Department of Electrical and Computer Engineering, Rice University, TX, United States of America.

Intelligent systems with applications
|August 29, 2024
PubMed
概括

这项研究引入了一种基于图形的新方法,用于从单线心电图中分类心律不整. 多层感知子模型实现了99.02%的准确性,证明了有效的心律失常检测.

关键词:
节律失常的分类类别是心律失常.电心电图 (ECG) 是一种心电图.图形卷积神经网络 (GCN) 的图形.多层感知 (MLP) 是一种多层感知.随机森林 (RF) 是一个随机的森林.可见度图 (VG) 是一个可见度图.

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Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
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Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

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相关实验视频

Last Updated: Jun 14, 2025

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
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Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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科学领域:

  • 生物医学工程 生物医学工程
  • 计算心脏病学 计算心脏病学
  • 医疗保健中的机器学习

背景情况:

  • 心律失常是心律不规律,可能导致严重的健康并发症.
  • 准确及时诊断心律失常对于有效的患者管理至关重要.
  • 单导电心电图 (ECG) 为心脏监测提供了一种便携且易于使用的方法.

研究的目的:

  • 开发和评估一种新的基于图形的方法,用于使用单导电心电图信号对心律失常疾病进行分类.
  • 为了比较不同机器学习模型在识别不同类型心律失常方面的表现.

主要方法:

  • 使用可见度图表技术将时间序列的心电图信号转换为图形表示.
  • 从这些图表中提取信息特征以进行后续分类.
  • 研究了三个分类器:图形卷积神经网络 (GCN),多层感知子 (MLP) 和随机森林 (RF).
  • 在培训和验证中使用了MIT-BIH心律失常数据库.

主要成果:

  • 多层感知子 (MLP) 模型实现了最高的分类准确率99.02%.
  • 随机森林 (RF) 模型也表现出强的性能,准确率为98.94%.
  • 提出的基于图形的方法被证明对准确的心律失常分类是有效的.

结论:

  • 这种基于图形的新方法提供了一个非常准确的方法,用于从单线心电图中对心律失常的分类.
  • 多层感知子 (MLP) 是这项任务的高效分类器,其性能优于其他模型.
  • 这种方法有望改善自动心律失常检测和诊断.