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相关概念视频

Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

7.4K
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...
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Electrocardiogram01:29

Electrocardiogram

2.5K
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...
2.5K
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

650
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...
650
Electrophysiology of Normal Cardiac Rhythm01:19

Electrophysiology of Normal Cardiac Rhythm

6.6K
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...
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Classification of Signals01:30

Classification of Signals

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
556
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

1.6K
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....
1.6K

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相关实验视频

Updated: Jul 25, 2025

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
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卷积神经网络通过微分演变优化,用于心电图分类.

Shan Wei Chen1,2, Shir Li Wang1,3, XiuZhi Qi4

  • 1Faculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak Malaysia.

Multimedia tools and applications
|June 26, 2023
PubMed
概括
此摘要是机器生成的。

这项研究优化了心电图 (ECG) 分类,使用一维卷积神经网络 (1D-CNN),增强了微分演化 (DE) 算法. 优化的1D-CNN显著提高了准确性,并减少了在远程医疗心血管护理中检测心律失常的培训时间.

关键词:
卷积神经网络是一种卷积神经网络.不同进化的差异进化.电心电图的分类方法

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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
  • 生物医学工程 生物医学工程

背景情况:

  • COVID-19大流行加速了远程医疗的采用,使远程患者监测变得至关重要.
  • 自动心电图 (ECG) 分类是心血管疾病管理的关键远程医疗干预.
  • 卷积神经网络 (CNN) 显示了对心电图分析的潜力,但需要优化精度和速度.

研究的目的:

  • 提出和评估一维CNN (1D-CNN),通过微分演变 (DE) 算法优化,用于心律失常的分类.
  • 在远程医疗框架内提高ECG分类的准确性和减少培训时间.
  • 使用标准心律失常数据库,比较优化的1D-CNN与未优化的版本的性能.

主要方法:

  • 开发了一个1D-CNN模型用于ECG心律失常的分类.
  • 利用差异演化 (DE) 算法来优化1D-CNN的激活函数和参数.
  • 在MIT-BIH和SCDH心律失常数据库上评估了优化的1D-CNN,将结果与未优化的模型进行比较.

主要成果:

  • 优化DE的1D-CNN在心律分类方面取得了更高的准确性,在MIT-BIH数据库中从97.6%提高到99.5%,在SCDH数据库中从80.2%提高到88.5%.
  • 与未优化的模型相比,优化的1D-CNN显示了训练时间的显著减少,MIT-BIH的训练时间减少了67.2%,SCDH的训练时间减少了64.2%.
  • 使用ReLU激活功能和10个时代,使用9.22s (MIT-BIH) 和10.35s (SCDH) 的训练时间,注意到了具体的改进.

结论:

  • 不同进化算法有效优化1D-CNNs,提高了ECG失常症分类的准确性和效率.
  • 这种优化的方法代表了远程医疗心血管保健的重大进步,使得远程诊断更可靠,更快速.
  • 优化1D-CNN的增强性能支持其在现实世界远程医疗系统中的应用,以获得更好的患者结果.