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Related Concept Videos

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...
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Correlation between ECG and Cardiac Cycle01:25

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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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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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Electrocardiogram Fundamentals01:28

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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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Pulse amplitude and quality01:17

Pulse amplitude and quality

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Pulse amplitude is a crucial indicator of cardiac health because it provides valuable insights into the strength of left ventricular contractions and the overall uniformity of blood circulation within the vasculature. The strength of the pulse is directly related to the force with which the heart contracts and the volume of blood being pumped.
A weak or absent pulse may indicate reduced cardiac output or poor left ventricular contraction, which can be signs of cardiovascular dysfunction or...
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Related Experiment Video

Updated: Feb 28, 2026

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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Ultra-High Quality ECG Compression for IoMT Application Using Temporal Convolutional Auto-Encoder with Improved RVQ.

Yeyi Guan, Jingyuan Qiao, Kailin Zhuang

    IEEE Journal of Biomedical and Health Informatics
    |February 26, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel deep learning model for compressing electrocardiogram (ECG) data, achieving high compression ratios while maintaining diagnostic accuracy for remote cardiac monitoring. The advanced ECG compression framework significantly improves data handling for the Internet of Medical Things.

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    Area of Science:

    • Biomedical Engineering
    • Artificial Intelligence
    • Signal Processing

    Background:

    • Prolonged electrocardiogram (ECG) monitoring is crucial for cardiac diagnosis but generates massive data volumes, challenging storage and transmission.
    • Existing ECG compression methods struggle to achieve higher compression ratios without compromising signal fidelity.

    Purpose of the Study:

    • To develop an advanced, end-to-end ECG compression framework to overcome the limitations of conventional methods.
    • To enhance the efficiency of data handling for cardiac monitoring within the Internet of Medical Things (IoMT).

    Main Methods:

    • Proposed an architecture combining a multi-granularity temporal-convolutional auto-encoder with Residual Vector Quantization (RVQ).
    • Integrated RVQ into the encoder-decoder pipeline, introduced a codebook-projection layer, and incorporated periodicity-aware modeling.
    • Utilized the MIT BIH Arrhythmia Database for extensive experimentation and validation.

    Main Results:

    • Achieved a compression ratio of 88× with a Quality Score (QS) of 42.7.
    • Maintained low distortion with Percentage Root Mean Difference (PRD) at 2.36% and Percentage Root Mean Difference Normalized (PRDN) at 17.56%.
    • Demonstrated superior performance compared to existing ECG compression techniques and validated generalization on the PhysioNet-2017 dataset.

    Conclusions:

    • The proposed end-to-end ECG compression framework effectively addresses data volume challenges in IoMT cardiac monitoring.
    • The synergistic approach of temporal-convolutional auto-encoder and RVQ offers significant improvements in compression ratio and signal fidelity.
    • The open-sourced code and model facilitate further research and application in clinical settings.