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相关概念视频

Disturbances in Heart Rhythm01:28

Disturbances in Heart Rhythm

945
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...
945
Mechanism of Cardiac Arrhythmias01:28

Mechanism of Cardiac Arrhythmias

917
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.
917
ECG Interpretation of Arrhythmias I: Sinus Arrhythmias01:16

ECG Interpretation of Arrhythmias I: Sinus Arrhythmias

213
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,...
213
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

572
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...
572
Pulse rhythm01:30

Pulse rhythm

787
Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
787

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相关实验视频

Updated: Jun 27, 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

Published on: May 23, 2021

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用先进的深度学习技术对数字化心电图数据集进行心律失常的分类.

Shoaib Sattar1, Rafia Mumtaz1, Mamoon Qadir2

  • 1School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan.

Sensors (Basel, Switzerland)
|April 27, 2024
PubMed
概括
此摘要是机器生成的。

这项研究将心电图像数字化为时间序列信号,用于深度学习分析. 一个卷积神经网络 (CNN) 在分类心律失常方面达到~92%的准确性,从而实现实时监测.

关键词:
在ECG分类中使用ECG分类.节律失常 (arrhythmia) 是一种心律失常.深度学习是一种深度学习.数字化 数字化自主监督学习学习

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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

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科学领域:

  • 心脏病学 心脏病学
  • 人工智能的人工智能
  • 生物医学信号处理

背景情况:

  • 电心电图 (ECG) 的分类对于诊断心脏病至关重要.
  • 深度学习 (DL) 提供了分析ECG信号的先进工具,以帮助专家诊断.
  • 将心电图记录数字化成时间序列数据使复杂的计算分析成为可能.

研究的目的:

  • 将一组ECG记录图像的数据集数字化成时间序列信号.
  • 应用和比较最先进的深度学习技术用于ECG信号分类.
  • 评估卷积神经网络 (CNN),长期短期记忆 (LSTM) 和自我监督学习 (SSL) 模型对心律不整的分类的性能.

主要方法:

  • 来自巴基斯坦医疗机构的心电图像被数字化.
  • II心跳被从数字化的心电图信号细分出来.
  • 训练和比较多个DL模型,包括CNN,LSTM和基于SSL的自动编码模型.

主要成果:

  • 拟议的CNN模型实现了最高的分类准确率,约为92%.
  • DL模型是根据来自不同患者心电图的数据集进行训练的.
  • 美国有线电视新闻网 (CNN) 模型展示了实时ECG监测的快速推断能力.

结论:

  • 通过DL模型处理的数字化心电图信号为基于图像的分析提供了可行的替代方案,用于心律失常的分类.
  • 开发的CNN模型为ECG信号提供了准确和高效的实时监控.
  • 这种方法可直接使用带有心电图机器输出的DL模型来加强心脏病患者监测.