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相关概念视频

Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

13.7K
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...
13.7K
Reducing Line Loss01:18

Reducing Line Loss

406
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
406
Instrumentation Amplifier01:25

Instrumentation Amplifier

1.2K
An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
To overcome this challenge, an ECG machine utilizes an instrumentation amplifier. This specialized amplifier is...
1.2K
Lossy Lines and Overvoltages01:22

Lossy Lines and Overvoltages

374
Transmission-line series resistance and shunt conductance cause three primary effects: attenuation, distortion, and power losses.
Attenuation
When constant series resistance and shunt conductance are present, voltage and current equations are modified. The propagation constant indicates that voltage and current waves consist of both forward and backward traveling components. These waves attenuate as they propagate, with the attenuation factor related to the resistance and conductance. In a...
374
Electrocardiogram01:29

Electrocardiogram

7.1K
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...
7.1K
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

1.7K
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...
1.7K

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相关实验视频

Updated: Mar 1, 2026

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

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基于深度学习的方法用于无损的ECG压缩.

Anumita Mitra1, Palash Kundu2, Rajarshi Gupta3

  • 1Electrical Engineering Department, Jadavpur University, Kolkata, India. mitraanumita.ee@gmail.com.

Cardiovascular engineering and technology
|February 27, 2026
PubMed
概括
此摘要是机器生成的。

这项研究引入了无损心电图 (ECG) 压缩的自适应深度学习模型,显著改善了远程心脏病患者监测的数据减少. 这种新的方法实现了高压缩比,损失微不足道,提高了远程监控的效率.

关键词:
适应式ARIMA模型模型节拍特定的节拍.深度学习是一种深度学习.高压缩质量的高压缩质量.没有损失的ECG压缩.

相关实验视频

Last Updated: Mar 1, 2026

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

3.2K

科学领域:

  • 生物医学工程 生物医学工程
  • 信号处理 信号处理
  • 人工智能的人工智能

背景情况:

  • 远程监控对于心脏病患者的护理至关重要,需要高效的数据处理.
  • 电心电图 (ECG) 数据的压缩减少了带宽和存储需求.

研究的目的:

  • 开发一种使用深度学习的新型无损ECG压缩方法.
  • 为了提高远程心脏病患者监测系统的效率.

主要方法:

  • 电脑心电图信号被拒绝并预先处理成节拍细胞.
  • 适应性自回归集成移动平均线 (ARIMA) 模型用于压缩.
  • 一个深度自编码器和MLPNN回归器组合预测了最佳模型超参数,通过粒子集群优化 (PSO) 调整.

主要成果:

  • 该方法实现了41.51的平均压缩比 (CR) 和0.209%的平均百分比根-平方平均差异 (PRD%).
  • 在46个PhysioNet记录中观察到高压缩质量与微不足道的损失,包括异常节拍.
  • 与原始信号相比,重建的节拍没有显示临床特征的偏差.

结论:

  • 拟议的自适应心电图压缩模型适合实时远程监控.
  • 它可以有效地存储和传输关键患者数据.
  • 这有助于持续监测,并改善心脏病患者的医疗保健服务.