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Related Concept Videos

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:
Respiratory Volumes01:15

Respiratory Volumes

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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Related Experiment Video

Updated: Jun 21, 2026

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

An acoustical respiratory phase segmentation algorithm using genetic approach.

F Jin1, F Sattar, D Y T Goh

  • 1School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue 50, Singapore 639798, Singapore. jinf0001@ntu.edu.sg

Medical & Biological Engineering & Computing
|July 30, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method for segmenting respiratory phases from tracheal breath sounds (TBS). The novel approach accurately identifies breathing cycles, improving analysis for respiratory conditions.

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Published on: August 9, 2024

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Respiratory Medicine

Background:

  • Accurate respiratory phase segmentation is crucial for diagnosing and monitoring respiratory conditions.
  • Existing methods face challenges with clinical applications, particularly for respiratory dysfunctions.
  • Tracheal breath sounds (TBS) offer a valuable, non-invasive source for respiratory analysis.

Purpose of the Study:

  • To develop a robust and fully automated method for respiratory phase segmentation using single-channel TBS recordings.
  • To address the limitations of current segmentation techniques in clinical settings.
  • To improve the accuracy and reliability of respiratory phase boundary detection.

Main Methods:

  • A novel automated segmentation approach utilizing single-channel tracheal breath sounds (TBS).
  • Estimation of respiratory segments via noise estimation and nonlinear mapping.
  • Identification of respiratory phase boundaries using a multi-population genetic algorithm with a novel evaluation function based on sample entropy (SampEn) and heterogeneity.
  • Performance analysis across diverse TBS recording types.

Main Results:

  • The proposed method demonstrates high accuracy in respiratory phase segmentation.
  • Achieved an overall accuracy of 12 +/- 5 ms for normal TBS and 21 +/- 9 ms for adventitious sounds.
  • The method proved robust and effective across various TBS recording types.

Conclusions:

  • The developed automated TBS segmentation method is robust and effective.
  • It successfully addresses clinical application challenges posed by existing segmentation methods for respiratory dysfunctions.
  • This technique offers a promising advancement for respiratory monitoring and diagnosis.