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Videos de Conceptos Relacionados

Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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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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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...
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Instrumentation Amplifier01:25

Instrumentation Amplifier

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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...
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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...
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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...
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Video Experimental Relacionado

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

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Enfoque Basado en Aprendizaje Profundo para Compresión de ECG sin Pérdidas

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
Resumen
Este resumen es generado por máquina.

Este estudio presenta un modelo adaptativo de aprendizaje profundo para la compresión sin pérdidas de electrocardiogramas (ECG), mejorando significativamente la reducción de datos para la monitorización remota de pacientes cardíacos. El novedoso enfoque logra altas relaciones de compresión con una pérdida insignificante, mejorando la eficiencia de la telemonitorización.

Palabras clave:
Modelo ARIMA adaptativoEspecífico por latidoAprendizaje profundoAlta calidad de compresiónCompresión de ECG sin pérdidas

Videos de Experimentos Relacionados

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

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Published on: April 26, 2024

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Área de la Ciencia:

  • Ingeniería Biomédica
  • Procesamiento de Señales
  • Inteligencia Artificial

Sus antecedentes:

  • La telemonitorización es crucial para la atención al paciente cardíaco, requiriendo un manejo eficiente de los datos.
  • La compresión de datos de electrocardiograma (ECG) reduce las necesidades de ancho de banda y almacenamiento.

Objetivo del estudio:

  • Desarrollar un método novedoso de compresión de ECG sin pérdidas utilizando aprendizaje profundo.
  • Mejorar la eficiencia de los sistemas de monitorización remota de pacientes cardíacos.

Principales métodos:

  • Las señales de ECG se denoizificaron y preprocesaron en celdas de latidos.
  • Se empleó un modelo adaptativo Autoregressive Integrated Moving Average (ARIMA) para la compresión.
  • Una combinación de autoencoder profundo y regresor MLPNN predijo hiperparámetros óptimos del modelo, ajustados mediante Optimización por Enjambre de Partículas (PSO).

Principales resultados:

  • El método logró una Relación de Compresión (CR) media de 41.51 y una diferencia porcentual media de la raíz cuadrática media (PRD%) de 0.209%.
  • Se observó alta calidad de compresión con pérdida insignificante en 46 registros de PhysioNet, incluyendo latidos anormales.
  • Los latidos reconstruidos no mostraron desviaciones en las características clínicas en comparación con las señales originales.

Conclusiones:

  • El modelo propuesto de compresión adaptativa de ECG es adecuado para la telemonitorización en tiempo real.
  • Permite el almacenamiento y la transmisión eficientes de datos críticos del paciente.
  • Esto facilita la monitorización continua y mejora la prestación de atención médica para pacientes cardíacos.