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Related Concept Videos

Respiratory Volumes01:15

Respiratory Volumes

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Respiratory volumes are crucial metrics, meticulously measured to quantify the air exchanged in and out of the lungs during various phases of the breathing cycle. These precise measurements are vital for assessing lung function, diagnosing respiratory conditions, and monitoring overall respiratory health. Each parameter provides specific insights into the mechanics of breathing and the functional capacity of the lungs.
Tidal Volume (TV) Tidal volume (TV) is the air inhaled or exhaled in a...
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The respiratory system is responsible for the intake of oxygen and the expulsion of carbon dioxide from the body. Respiratory volumes describe the volume of air in the lungs at different phases of the respiratory cycle. Tidal volume is the air breathed in and out during normal, quiet breathing. Inspiratory reserve volume is the air that can be forcefully inspired beyond the tidal volume. In contrast, expiratory reserve volume refers to the air that can be expelled from the lungs after a normal...
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A Ventilation assessment is critical for monitoring a patient's health status. Respiration, one of the most accessible vital signs, provides insights into the function of numerous body systems and can indicate serious health issues, such as brainstem injuries from head trauma.
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Lung Capacity01:47

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Assessing blood pressure is a standard procedure executed in virtually all medical environments. The method utilized today was established over a hundred years ago by an innovative Russian doctor, Dr. Nikolai Korotkoff. The soft ticking noise, known as Korotkoff sounds, heard while taking blood pressure readings results from turbulent blood flow within the vessels. The apparatus required for this procedure includes a sphygmomanometer, a blood pressure cuff attached to a gauge, and a...
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Related Experiment Video

Updated: Oct 7, 2025

Monitoring Lung Function with Electrical Impedance Tomography in the Intensive Care Unit
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Estimation of Tidal Volume Using Load Cells on a Hospital Bed.

Hewon Jung, Jacob P Kimball, Timothy Receveur

    IEEE Journal of Biomedical and Health Informatics
    |January 7, 2022
    PubMed
    Summary

    This study demonstrates that hospital bed load cell sensors can effectively monitor respiratory rate and tidal volume using machine learning. This offers a convenient, unobtrusive method for continuous respiratory monitoring in general care settings.

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    Area of Science:

    • Biomedical Engineering
    • Medical Device Technology
    • Machine Learning in Healthcare

    Background:

    • Respiratory failure is a critical cause for intensive care unit admission, yet respiratory monitoring often receives less focus than cardiac monitoring.
    • Existing unobtrusive respiratory monitoring technologies face challenges hindering widespread adoption.
    • There is a growing need for convenient, continuous, and quantifiable respiratory monitoring solutions in healthcare.

    Purpose of the Study:

    • To investigate the feasibility of using integrated hospital bed load cell sensors for respiratory rate (RR) and tidal volume (TV) monitoring.
    • To develop and validate a machine learning (ML)-based algorithm for estimating TV without subject-specific calibration.
    • To assess the efficacy of load cell sensors in capturing respiratory parameters across different postures.

    Main Methods:

    • Utilized four load cell channels embedded in a hospital bed and a reference spirometer for simultaneous data acquisition from 15 healthy subjects.
    • Developed a signal processing pipeline to extract features from load cell data, including respiratory movements and ballistocardiogram (BCG) signals.
    • Implemented a globalized ML algorithm for posture-independent estimation of RR and TV.

    Main Results:

    • The RR estimation algorithm achieved a root mean square error (RMSE) of 0.6 breaths per minute compared to spirometer measurements.
    • The TV estimation model, integrating three force signal axes and BCG features, showed a correlation of 0.85 and an RMSE of 0.23 L against true TV values.
    • The developed model demonstrated effectiveness in a posture-independent manner without requiring electrocardiogram (ECG) signals.

    Conclusions:

    • Hospital bed load cell sensors present a viable and convenient solution for continuous respiratory monitoring in general care settings.
    • The proposed ML-based approach enables accurate estimation of respiratory parameters without the need for individual subject calibration.
    • This technology has the potential to enhance patient care by providing unobtrusive and readily available respiratory data.