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Related Concept Videos

Pulse rhythm01:30

Pulse rhythm

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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...
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A Real-Time Contact-Free Atrial Fibrillation Detection System for Mobile Devices.

Chih-Wei Tseng, Bing-Fei Wu, Yu Sun

    IEEE Journal of Biomedical and Health Informatics
    |July 2, 2024
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    Summary
    This summary is machine-generated.

    A new lightweight system uses smartphone cameras to detect Atrial Fibrillation (AF) non-invasively. This novel approach offers accurate and affordable early detection of AF, even in challenging real-world conditions.

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    Area of Science:

    • Biomedical Engineering
    • Artificial Intelligence in Healthcare
    • Cardiology

    Background:

    • Global aging populations are experiencing rising rates of Atrial Fibrillation (AF), a condition strongly linked to stroke and disability.
    • Early detection of AF is crucial for stroke prevention, but current methods struggle with accuracy in complex, real-world environments.
    • Existing detection techniques often face limitations in speed, cost-effectiveness, and adaptability to motion and lighting variations.

    Purpose of the Study:

    • To develop a novel, lightweight, non-contact system for Atrial Fibrillation (AF) detection.
    • To enable AF detection on edge computing devices such as smartphones and tablets.
    • To improve the accuracy and accessibility of AF screening in diverse environmental conditions.

    Main Methods:

    • A dataset of 7,216 30-second segments from 452 subjects (AF, Normal Sinus Rhythm, Other Arrhythmias) was collected to reflect real-world scenarios.
    • A lightweight Convolutional Neural Network (CNN) featuring a large receptive field, a bidirectional spatial mapping augmented attention module (BiSME-ATT), and a bidirectional feature pyramid network (BiFPN) was employed.
    • The system was optimized for mobile deployment by minimizing model parameters and Floating-Point Operations Per Second (FLOPs).

    Main Results:

    • The proposed system achieved high performance metrics in AF vs. Non-AF detection: 94.39% accuracy, 91.57% sensitivity, 95.44% specificity, 88.06% positive predictive value, and 96.93% negative predictive value.
    • Significant improvements in AF detection were observed across varying levels of motion interference and light intensity.
    • The system demonstrated robustness and effectiveness in complex, real-world conditions.

    Conclusions:

    • The developed lightweight, non-contact facial rPPG system offers a promising solution for accessible and accurate Atrial Fibrillation (AF) detection.
    • Deployment on edge devices like smartphones can facilitate widespread early screening and potentially reduce stroke-related disabilities.
    • The system's performance in diverse conditions highlights its potential for real-world clinical application.