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Related Concept Videos

Pulse rhythm01:30

Pulse rhythm

947
Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
947
Assessment of apical radial pulse01:25

Assessment of apical radial pulse

895
Apical-Radial (A-R) Pulse Assessment
The A-R pulse assessment involves simultaneous evaluation of the apical and radial pulses. When the apical and radial pulse rates vary, this assessment helps identify a pulse deficit.
Pre-Procedural Preparation
895

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Related Experiment Video

Updated: Sep 24, 2025

High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation
09:17

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Motion-Robust Atrial Fibrillation Detection Based on Remote-Photoplethysmography.

Bing-Fei Wu, Bing-Jhang Wu, Shao-En Cheng

    IEEE Journal of Biomedical and Health Informatics
    |May 5, 2022
    PubMed
    Summary
    This summary is machine-generated.

    A new non-contact method using remote photoplethysmography (rPPG) effectively detects atrial fibrillation (AF), a common heart rhythm disorder linked to stroke, even with motion disturbances.

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    Area of Science:

    • Cardiology
    • Biomedical Engineering
    • Artificial Intelligence

    Background:

    • Atrial fibrillation (AF) affects over 43 million worldwide and is a major stroke risk factor.
    • Many AF patients are asymptomatic and undiagnosed due to lack of convenient screening tools.
    • Remote photoplethysmography (rPPG) offers a potential non-contact solution but is challenged by motion artifacts.

    Purpose of the Study:

    • To develop and validate a non-contact AF detection method using rPPG.
    • To address and mitigate motion disturbances inherent in rPPG signals.
    • To create and utilize a comprehensive AF dataset for algorithm verification.

    Main Methods:

    • Development of NR-Net, ATT-Net, and SQ-Mask modules to handle motion noise and signal quality issues in rPPG.
    • Utilizing a Convolutional Neural Network (CNN) for noise elimination.
    • Employing channel-wise and temporal attention mechanisms to reduce the impact of poor signal segments.

    Main Results:

    • The proposed rPPG method achieved high accuracy (95.69%), sensitivity (96.76%), and specificity (94.33%) in distinguishing AF from normal sinus rhythm.
    • The algorithm demonstrated superior performance against benchmark methods in AF vs. Non-AF and AF vs. Other arrhythmia scenarios.
    • Accuracy improved significantly on slight motion data (up to 95.82%) and full motion data (over 3% increase).

    Conclusions:

    • The developed non-contact rPPG approach offers a promising and convenient tool for AF screening.
    • The novel NR-Net, ATT-Net, and SQ-Mask modules effectively overcome motion-related challenges in rPPG-based AF detection.
    • The large, diverse hospital-ward dataset supports the robustness and generalizability of the proposed method.