Automated evaluation of respiratory signals to provide insight into respiratory drive

The diaphragm muscle (DIAm) is the primary inspiratory muscle in mammals and is highly active throughout life displaying rhythmic activity. The repetitive activation of the DIAm (and of other muscles driven by central pattern generator activity) presents an opportunity to analyze these physiological...

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Bibliographic Details
Published in:Respiratory physiology & neurobiology 2022-06, Vol.300, p.103872-103872, Article 103872
Main Authors: Khurram, Obaid U., Gransee, Heather M., Sieck, Gary C., Mantilla, Carlos B.
Format: Article
Language:English
Subjects:
Animals
Diaphragm - physiology
Diaphragm muscle
Electromyography
Mammals
Motor control
Motor unit
Respiratory
Respiratory Muscles
Respiratory Rate
Respiratory System
Statistics
Variability
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Summary:The diaphragm muscle (DIAm) is the primary inspiratory muscle in mammals and is highly active throughout life displaying rhythmic activity. The repetitive activation of the DIAm (and of other muscles driven by central pattern generator activity) presents an opportunity to analyze these physiological data on a per-event basis rather than pooled on a per-subject basis. The present study highlights the development and implementation of a graphical user interface-based algorithm using an analysis of critical points to detect the onsets and offsets of individual respiratory events across a range of motor behaviors, thus facilitating analyses of within-subject variability. The algorithm is designed to be robust regardless of the signal type (e.g., EMG or transdiaphragmatic pressure). Our findings suggest that this approach may be particularly beneficial in reducing animal numbers in certain types of studies, for assessments of perturbation studies where the effects are relatively small but potentially physiologically meaningful, and for analyses of respiratory variability. •Inputting subject means to statistics models underrepresents real variability.•We present an algorithm to automatically detect individual respiratory events.•Statistical assessments should be performed by incorporating these per-event data.•Such statistical approaches allow detection of smaller effects across conditions.•Reductions in sample size or improved assessments post-perturbation may be possible
ISSN:1569-9048
1878-1519
1878-1519
DOI:10.1016/j.resp.2022.103872