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相关概念视频

Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

431
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...
431
Electrocardiogram01:29

Electrocardiogram

1.6K
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...
1.6K
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

270
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....
270
Electrophysiology of Normal Cardiac Rhythm01:19

Electrophysiology of Normal Cardiac Rhythm

1.6K
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...
1.6K
ECG Interpretation of Arrhythmias I: Sinus Arrhythmias01:16

ECG Interpretation of Arrhythmias I: Sinus Arrhythmias

150
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,...
150
Correlation between ECG and Cardiac Cycle01:24

Correlation between ECG and Cardiac Cycle

2.7K
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...
2.7K

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相关实验视频

Updated: May 10, 2025

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

179

使用格拉米安角场转换进行增强的心电图分类,并采用多导线分析和细分技术.

Gi-Won Yoon1, Segyeong Joo1

  • 1Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.

MethodsX
|April 28, 2025
PubMed
概括
此摘要是机器生成的。

将1D心电图信号转换为2D格拉米安角场 (GAF) 图像可以提高对心房 (AFib) 和左心室缩 (LVH) 等疾病的分类准确性. 512x512分辨率提供最佳的效率和性能.

关键词:
心房动是一种心房动.分类 分类 分类 分类.深度学习 (Deep Learning) 是一种深度学习.EGAFCov下一个电心电图 (ECG) 是一种心电图.格拉米安的角度场.

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Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis
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相关实验视频

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Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

179
Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis
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A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis

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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
  • 人工智能在医学中的应用

背景情况:

  • 传统的心电图 (ECG) 分析面临由于时间低效率和人为错误的可能性而受到限制.
  • 基于特征的心电图分析可能很复杂,可能无法捕获所有相关信号特征.
  • 开发自动化和准确的心电图分类方法对于及时诊断至关重要.

研究的目的:

  • 研究将1D心电图信号转化为2D格拉米安角场 (GAF) 图像的有效性,以提高分类.
  • 评估不同GAF图像分辨率 (5000x5000,512x512,256x256) 的性能,对四个心电图类别进行分类.
  • 通过深度学习评估细分方法对ECG分类准确性的影响.

主要方法:

  • 1D心电图信号被转换为2DGAF图像,分辨率为5000x5000,512x512和256x256像素.
  • 应用了细分技术,以改善GAF图像中的特征定位.
  • 使用ConvNext深度学习模型进行图像分类,其性能指标包括准确性,精度,回忆和F1分数.

主要成果:

  • 512x512的GAF图像分辨率显示了计算效率和分类准确性之间的最佳平衡.
  • 获得的F1分数为心房 (AFib) 的0.781分,左心室缩 (LVH) 的0.71分,右心室缩 (RVH) 的0.521分和正常心电图的0.792分.
  • 细分方法提高了分类性能,特别是用于检测LVH和RVH.
  • 5000x5000分辨率提供了高精度,但计算密集;256x256分辨率遭受了细节损失和精度降低.

结论:

  • GAF转换为改善心电图信号分析和分类提供了一个有希望的方法.
  • 512x512分辨率为基于深度学习的ECG分类提供了实用和有效的环境.
  • 将细分与GAF图像集成可以进一步提高特定心脏病的诊断准确度.