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Related Concept Videos

Assessment of Ventilation I: Respiratory Rate01:20

Assessment of Ventilation I: Respiratory Rate

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Assessment of Ventilation
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.
Critical Guidelines for Assessing Ventilation:
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Special considerations while measuring oxygen saturation01:19

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Assessing respiratory rate concurrently with pulse measurement is fundamental to patient care, providing valuable insights into the patient's respiratory function. The normal breathing rate for an adult usually falls within a normal range of 12 to 20 breaths per minute. Abnormal respiratory rates can signal underlying health conditions or the need for immediate intervention.
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Factors Affecting Respiration01:24

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Respiration is a crucial physiological function involving exchanging oxygen (O2) and carbon dioxide (CO2) between an organism and its environment. Various factors can impact this essential process:
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Assessment of Ventilation II: Respiratory Depth and Rhythm01:29

Assessment of Ventilation II: Respiratory Depth and Rhythm

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Respiratory Depth
Respiratory depth measures the volume of air inhaled or exhaled during a breath. It can vary from shallow to deep and typically remains consistent when a person is at rest or asleep. Occasionally, individuals will automatically inhale deeply, known as sighing, which inflates the lungs with more air than normal breathing.
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Respiratory Volumes and Capacities I01:26

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Assessing the respiratory rate and rhythm for a complete minute is crucial for evaluating the breathing pattern. Even a minor increase in the patient's average respiratory rate, by as little as three to five breaths per minute, is an early and vital indicator of respiratory distress. Patients with a respiratory rate exceeding twenty-four breaths per minute require close monitoring to determine the physiological alterations. This careful observation is essential for prompt recognition and...
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Alterations in Respiration II01:30

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There are numerous types of normal and abnormal respiration. Based on ventilatory movements, breathing patterns are classified as regular, deep, or shallow. Examples include Biot's breathing, Cheyne-Stokes respiration, Kussmaul's breathing, hyperventilation, and hypoventilation. Each pattern is clinically significant and aids in evaluating patients.
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  3. Respiratory Rate Estimation From Thermal Video Data Using Spatio-temporal Deep Learning.
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  3. Respiratory Rate Estimation From Thermal Video Data Using Spatio-temporal Deep Learning.

Related Experiment Video

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

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Respiratory Rate Estimation from Thermal Video Data Using Spatio-Temporal Deep Learning.

Mohsen Mozafari1, Andrew J Law1,2, Rafik A Goubran1

  • 1Department of Systems and Computer Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada.

Sensors (Basel, Switzerland)
|October 16, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel deep learning method for accurate respiration rate (RR) estimation from thermal videos. The approach achieves state-of-the-art accuracy, enabling privacy-preserving remote health monitoring.

Keywords:
deep learningface detectionrespiration rate estimationthermal video

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Area of Science:

  • Biomedical Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Remote health monitoring requires privacy-preserving methods.
  • Respiration rate (RR) estimation is crucial for health assessment.
  • Thermal imaging offers a non-intrusive data source for physiological monitoring.

Purpose of the Study:

  • To develop an end-to-end deep learning model for accurate RR estimation using thermal video data.
  • To introduce a novel loss function addressing phase shifts in respiration measurement.
  • To evaluate the model's performance across various conditions, including face mask usage.

Main Methods:

  • Utilized a detection transformer (DeTr) for facial region identification.
  • Employed 3D convolutional neural networks and bi-directional LSTMs for respiratory signal extraction.
  • Introduced a novel loss function combining negative maximum cross-correlation and absolute frequency peak difference.

    • Main Results:

    • The proposed method achieved an average error of 1.6 breaths per minute.
    • Outperformed existing RR estimation models across four tested conditions (sitting/standing, with/without mask).
    • Demonstrated state-of-the-art accuracy for RR estimation from thermal video.

    Conclusions:

    • The developed deep learning approach offers highly accurate and privacy-preserving RR estimation.
    • The method is suitable for real-time applications in remote health monitoring.
    • Thermal video analysis presents a promising avenue for non-contact physiological monitoring.