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Breathing

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The process of breathing, inhaling and exhaling, involves the coordinated movement of the chest wall, the lungs, and the muscles that move them. Two muscle groups with important roles in breathing are the diaphragm, located directly below the lungs, and the intercostal muscles, which lie between the ribs. When the diaphragm contracts, it moves downward, increasing the volume of the thoracic cavity and creating more room for the lungs to expand. When the intercostal muscles contract, the ribs...

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Related Experiment Video

Updated: Feb 7, 2026

Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis

Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis

Published on: August 9, 2024

Novel approach to computerized breath detection in lung function diagnostics.

Jaroslav Horáček¹, Václav Koucký², Milan Hladík¹

¹Department of Applied Mathematics, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic.

Computers in Biology and Medicine

|August 7, 2018

Summary

This summary is machine-generated.

A new algorithm accurately detects breaths in distorted lung function data, outperforming existing methods. This robust approach is vital for precise infant breathing analysis and may replace current diagnostic techniques.

Keywords:

Automated breath detection Breath end Lung function testing Medical algorithm design Multiple breath washout test Tidal breathing

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Published on: January 7, 2019

Area of Science:

Pulmonary Medicine
Biomedical Engineering
Signal Processing

Background:

Accurate breath detection is critical for lung function analysis, but current algorithms struggle with poor data quality, especially in infants.
Existing threshold or smoothing algorithms lack accuracy in suboptimal data conditions, hindering reliable clinical index calculation.
Precise breath end localization is paramount for meaningful interpretation of respiratory data.

Purpose of the Study:

To develop a novel algorithm for accurate breath detection in severely distorted respiratory data.
To address the limitations of current methods in handling suboptimal data quality, particularly for infant lung function analysis.
To create a universal and robust breath detection solution applicable across various patient groups and breathing disorders.

Main Methods:

Utilized flow and gas concentration data from multiple breath washout tests as input.
Designed a novel breath detection algorithm based on universal physiological characteristics of the respiratory tract.
Validated the algorithm's accuracy on severely distorted data from 19 patients with diverse breathing disorders, comparing it to existing algorithms and expert estimations.

Main Results:

The novel algorithm demonstrated superior accuracy compared to traditional threshold algorithms.
Its performance in detecting breaths was comparable to that of human experts.
The algorithm proved highly robust and efficient, even with severely distorted data from patients with various pulmonary conditions.

Conclusions:

The proposed algorithm is highly robust, universal, and accurate, even in severely distorted data where other methods fail.
It eliminates the need for pre-set thresholds or patient-specific inputs, allowing for broad patient applicability.
This algorithm has the potential to supersede current breath detection approaches in pulmonary function diagnostics.