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Related Concept Videos

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

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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.
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Instrumentation Amplifier01:25

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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.
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Lossy Lines and Overvoltages01:22

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Transmission-line series resistance and shunt conductance cause three primary effects: attenuation, distortion, and power losses.
Attenuation
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Electrocardiogram01:29

Electrocardiogram

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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.
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Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

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Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
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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
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Related Experiment Video

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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Deep Learning Based Approach for Lossless ECG Compression.

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
Summary
This summary is machine-generated.

This study introduces an adaptive deep learning model for lossless electrocardiogram (ECG) compression, significantly improving data reduction for remote cardiac patient monitoring. The novel approach achieves high compression ratios with negligible loss, enhancing tele-monitoring efficiency.

Keywords:
Adaptive ARIMA modelBeat specificDeep-learningHigh compression qualityLossless ECG compression

Related Experiment Videos

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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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Tele-monitoring is crucial for cardiac patient care, requiring efficient data handling.
  • Electrocardiogram (ECG) data compression reduces bandwidth and storage needs.

Purpose of the Study:

  • To develop a novel, lossless ECG compression method using deep learning.
  • To enhance the efficiency of remote cardiac patient monitoring systems.

Main Methods:

  • ECG signals were denoised and preprocessed into beat-cells.
  • An adaptive Autoregressive Integrated Moving Average (ARIMA) model was employed for compression.
  • A deep autoencoder and MLPNN regressor combination predicted optimal model hyperparameters, tuned via Particle Swarm Optimization (PSO).

Main Results:

  • The method achieved a mean Compression Ratio (CR) of 41.51 and a mean Percent Root-Mean-Square Difference (PRD%) of 0.209%.
  • High compression quality with negligible loss was observed across 46 PhysioNet records, including abnormal beats.
  • Reconstructed beats showed no deviations in clinical features compared to original signals.

Conclusions:

  • The proposed adaptive ECG compression model is suitable for real-time tele-monitoring.
  • It enables efficient storage and transmission of critical patient data.
  • This facilitates continuous monitoring and improves healthcare delivery for cardiac patients.