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Assessment of Ventilation II: Respiratory Depth and Rhythm01:29

Assessment of Ventilation II: Respiratory Depth and Rhythm

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.
To assess respiratory depth, observe the degree of chest excursion or movement:
Assessment of Respiration01:23

Assessment of Respiration

The respiratory system's basic structures and primary functions lay the foundation for nurses' comprehensive respiratory assessments. This assessment includes subjective and objective data to gauge the patient's respiratory health.
Subjective Assessment: Nurses interview the patient to gather information directly during the subjective assessment. It includes questions about the individual's medical history, medications, and symptoms, focusing on past respiratory conditions like asthma or COPD,...

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Related Experiment Video

Updated: Jun 18, 2026

An Instrumented Pull Test to Characterize Postural Responses
12:18

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Published on: April 6, 2019

Classification of breathing events using load cells under the bed.

Zachary T Beattie1, Chad C Hagen, Misha Pavel

  • 1Oregon Health & Science University, Portland, 97239, USA. beattiez@bme.ogi.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|December 8, 2009
PubMed
Summary

Researchers developed a novel method using under-mattress sensors to detect disordered breathing, offering a convenient alternative to polysomnography for diagnosing sleep apnea-hypopnea syndrome (OSAHS). This approach shows promise for improved sleep disorder diagnosis.

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Area of Science:

  • Biomedical Engineering
  • Sleep Medicine
  • Medical Devices

Background:

  • Sleep disturbances significantly impact health and quality of life.
  • Obstructive sleep apnea-hypopnea syndrome (OSAHS) is common but often undiagnosed.
  • Current diagnostic methods like polysomnography (PSG) are inconvenient and disruptive.

Purpose of the Study:

  • To investigate an alternative diagnostic method for OSAHS.
  • To utilize unobtrusive load cells for monitoring sleep-related physiological signals.
  • To differentiate between normal respiration and disordered breathing events.

Main Methods:

  • Employing load cells fitted under a bed to monitor movement, heart rate, and respiration.
  • Analyzing load cell data to identify patterns indicative of apneas and hypopneas.
  • Developing algorithms to classify breathing segments based on sensor data.

Main Results:

  • The load cell system successfully distinguished between normal respiration and disordered breathing.
  • The method achieved a sensitivity of 0.77 for detecting disordered breathing.
  • The method achieved a specificity of 0.91 for detecting disordered breathing.

Conclusions:

  • Load cell technology offers a promising, unobtrusive alternative to PSG for OSAHS diagnosis.
  • This non-invasive approach can aid in identifying patients with clinically relevant disordered breathing.
  • Further development could lead to more accessible sleep apnea screening and diagnosis.