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関連する概念動画

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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関連する実験動画

Updated: Mar 1, 2026

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
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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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損失なしECG圧縮のための深層学習ベースのアプローチ

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
まとめ
この要約は機械生成です。

この研究は、損失なし心電図（ECG）圧縮のための適応型深層学習モデルを導入し、リモート心臓患者モニタリングのためのデータ削減を大幅に改善する。この新しいアプローチは、無視できる損失で高い圧縮率を達成し、遠隔モニタリングの効率を高める。

キーワード:
適応型ARIMAモデルビート固有深層学習高圧縮品質損失なしECG圧縮

関連する実験動画

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

科学分野:

  • 生体医工学
  • 信号処理
  • 人工知能

背景:

  • 遠隔モニタリングは心臓患者のケアに不可欠であり、効率的なデータ処理が必要である。
  • 心電図（ECG）データの圧縮は、帯域幅とストレージのニーズを削減する。

研究 の 目的:

  • 深層学習を用いた新しい損失なしECG圧縮方法を開発すること。
  • リモート心臓患者モニタリングシステムの効率を向上させること。

主な方法:

  • ECG信号をノイズ除去し、ビートセルに前処理した。
  • 圧縮のために適応型自己回帰移動平均（ARIMA）モデルを採用した。
  • 深層オートエンコーダーとMLPNN回帰器の組み合わせにより、粒子群最適化（PSO）によって調整された最適なモデルハイパーパラメータを予測した。

主要な成果:

  • 提案手法は、平均圧縮率（CR）41.51、平均二乗誤差率（PRD%）0.209%を達成した。
  • 異常なビートを含む46のPhysioNet記録全体で、無視できる損失で高い圧縮品質が観察された。
  • 再構成されたビートは、元の信号と比較して臨床的特徴に偏差を示さなかった。

結論:

  • 提案された適応型ECG圧縮モデルは、リアルタイムの遠隔モニタリングに適している。
  • 重要な患者データの効率的な保存と送信を可能にする。
  • これにより、心臓患者の継続的なモニタリングが促進され、ヘルスケア提供が改善される。