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Updated: Jul 10, 2026
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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
Published on: June 21, 2024
Power-based segmentation of respiratory signals using forward-backward bank filtering
A A Aoude1, A L Motto, H L Galiana
1Dept. of Biomed. Eng., McGill Univ., Montreal, Que., Canada.
Summary
This study introduces an automated method to separate normal breathing from noisy data using respiratory inductance plethysmography (RIP) signals. This technique enhances the analysis of breathing patterns in long-term studies and clinical settings.
Area of Science:
- Biomedical Engineering
- Physiology
- Medical Signal Processing
Background:
- Noninvasive respiratory inductance plethysmography (RIP) is crucial for monitoring breathing.
- Accurate segmentation of respiratory data into breathing and artifact segments is challenging.
- Automated analysis is needed for long-term and clinical respiratory studies.
Purpose of the Study:
- To develop an automated method for segmenting respiratory signals from RIP.
- To distinguish between quiet breathing and artifact-corrupted segments.
- To enable reliable off-line analysis of cardiorespiratory data.
Main Methods:
- Implemented a forward-backward filtering procedure.
- Applied automated segmentation to ribcage and abdominal RIP signals.
- Validated the method on post-operative infant data.
Main Results:
- Successfully segmented respiratory signals into breathing and artifact segments.
- Demonstrated applicability to long-term off-line analysis.
- Validated the procedure on clinical data from infants.
Conclusions:
- The automated segmentation method is effective for RIP data.
- This technique improves the analysis of cardiorespiratory signals.
- Applicable to home monitoring, sleep studies, and clinical recovery settings.
