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Related Concept Videos

Special considerations while measuring pulse01:13

Special considerations while measuring pulse

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Assessing a patient's pulse is a fundamental skill in healthcare, but certain situations require special attention:
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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.
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Pulse oximetry, or SpO2, is a non-invasive method for continuously monitoring arterial oxygen saturation (SaO2). This procedure involves attaching a probe or sensor to the patient's fingertip, forehead, earlobe, or nose bridge. The sensor works by detecting changes in oxygen saturation levels through light signals generated by the oximeter and reflected by the pulsing blood under the probe.
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When the heart pumps blood out, arterial elastic fibers play a crucial role in sustaining a high-pressure gradient. They expand to accommodate the received blood and then recoil - a process known as the pulse that can be either manually palpated or electronically quantified. Despite a reduction in its effect with increased distance from the heart, elements of the pulse's systolic and diastolic components persist, observable even at the arteriole level.
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Apical-Radial (A-R) Pulse Assessment
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  3. Pulsegan: Learning To Generate Realistic Pulse Waveforms In Remote Photoplethysmography.
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  3. Pulsegan: Learning To Generate Realistic Pulse Waveforms In Remote Photoplethysmography.

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PulseGAN: Learning to Generate Realistic Pulse Waveforms in Remote Photoplethysmography.

Rencheng Song, Huan Chen, Juan Cheng

    IEEE Journal of Biomedical and Health Informatics
    |January 12, 2021

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    PulseGAN generates realistic remote photoplethysmography (rPPG) pulse signals by denoising facial chrominance (CHROM) data. This method enhances heart rate (HR) and heart rate variability (HRV) accuracy, improving non-contact health monitoring.

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    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Computer Vision

    Background:

    • Remote photoplethysmography (rPPG) non-invasively measures cardiac signals from facial videos, crucial for health monitoring and emotion recognition.
    • Existing rPPG methods often yield inaccurate pulse signals, limiting their use to average heart rate (HR) estimation.
    • High-quality rPPG signals are needed to derive precise physiological metrics.

    Purpose of the Study:

    • To introduce PulseGAN, a novel generative adversarial network framework for generating realistic rPPG pulse signals.
    • To improve the accuracy of rPPG-derived features, including HR, interbeat interval (IBI), and heart rate variability (HRV).
    • To enhance the quality of rPPG waveforms by denoising chrominance (CHROM) signals.

    Main Methods:

  • Utilized a generative adversarial network (GAN) architecture, PulseGAN, for rPPG signal generation.
  • Employed denoising of chrominance (CHROM) signals from facial videos as input.
  • Incorporated error losses in both time and spectrum domains, alongside adversarial loss, to ensure accurate pulse waveform generation.

    • Main Results:

    • PulseGAN significantly improved rPPG waveform quality compared to input CHROM signals.
    • Demonstrated substantial reductions in mean absolute error for AVNN (average of normal-to-normal intervals) by 40.45-41.63% and SDNN (standard deviation of NN intervals) by 37.53-58.41% across three public databases.
    • Validated the framework's effectiveness in enhancing the accuracy of HR, IBI, and HRV features.

    Conclusions:

    • PulseGAN effectively generates high-fidelity rPPG pulse signals, overcoming limitations of existing methods.
    • The framework enhances the accuracy of critical cardiac metrics derived from rPPG.
    • PulseGAN offers a versatile solution that can be integrated with other rPPG techniques to improve reliability and expand applications.