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相关概念视频

Disturbances in Heart Rhythm01:28

Disturbances in Heart Rhythm

870
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...
870
Mechanism of Cardiac Arrhythmias01:28

Mechanism of Cardiac Arrhythmias

879
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.
879
Pulse rhythm01:30

Pulse rhythm

754
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...
754
Electrophysiology of Normal Cardiac Rhythm01:19

Electrophysiology of Normal Cardiac Rhythm

2.0K
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...
2.0K
ECG Interpretation of Arrhythmias I: Sinus Arrhythmias01:16

ECG Interpretation of Arrhythmias I: Sinus Arrhythmias

170
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,...
170
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

415
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....
415

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相关实验视频

Updated: May 28, 2025

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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使用基于深度学习和优化的方法论进行心律失常的分类.

Suvita Rani Sharma1, Birmohan Singh1, Manpreet Kaur2

  • 1Department of Computer Science and Engineering, Sant Longowal Institute of Engineering and Technology, Longowal, India.

Journal of medical engineering & technology
|February 14, 2025
PubMed
概括

这项研究引入了一种先进的电心电图 (ECG) 信号分类方法,通过深度学习和一种新的特征选择技术,在识别五种不同心律时达到99.31%的准确性.

关键词:
分类 分类 分类 分类.深度学习是一种深度学习.这种算法是Metaheuristic算法.标杆图是指一个标杆图.

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相关实验视频

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Semi-automated Optical Heartbeat Analysis of Small Hearts
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科学领域:

  • 生物医学工程 生物医学工程
  • 医疗保健中的人工智能
  • 信号处理 信号处理

背景情况:

  • 电心电图 (ECG) 信号分析对于诊断心脏病状况至关重要.
  • 传统方法面临着噪音和准确分类的挑战.
  • 深度学习为改进心电图解释提供了潜力.

研究的目的:

  • 开发一种强大的方法来分类五种类型的心电图信号.
  • 通过消除噪音和基线流动来提高心电图信号质量.
  • 为了实现高精度的自动化心电图节拍分类.

主要方法:

  • 使用移动平均线过器和离散波纹转换进行心电图信号预处理.
  • 从预处理的信号中形成图像 (灰度和刻度图).
  • 通过EfficientNet-B0深度学习模型提取功能.
  • 使用混合方法的特征选择,结合过方法和自适应搜索 (SABES) 优化.
  • 用于特征缩放的Z分数规范化.

主要成果:

  • 通过R峰检测成功对ECG信号进行细分.
  • 有效降低噪音,包括基线流浪和电源线干扰.
  • 五个ECG信号类别的高精度分类,达到99.31%的精度.
  • 证明了EfficientNet-B0模型和SABES优化的有效性.

结论:

  • 拟议的方法提供了对ECG信号分类的有效和准确方法.
  • 深度学习和高级功能选择的整合大大提高了诊断能力.
  • 这项工作为自动心律分析提供了有价值的工具.