Time-Localized Parametric Decomposition of Respiratory Airflow for Sub-Breath Analysis
2026-04-24 • Machine Learning
Machine Learning
AI summaryⓘ
The authors developed a new way to break down breathing airflow into smaller parts that show specific timing and strength within each breath. Instead of using general measures of breathing, their method uses special shapes based on real breathing patterns to better capture how the breath changes over time. They tested this on thousands of breaths and found it was both accurate and reliable, even with some noise. This detailed breakdown helped identify signs of mental tiredness better than traditional breathing measures. Overall, the authors show this approach can help study how breathing adapts during mental challenges.
respiratory airflowtidal volumeintrabreath waveformparametric modelingHalf-Sine functionGaussian functionBeta functionnonlinear optimizationcognitive fatiguerespiratory motor control
Authors
Victoria Ribeiro Rodrigues, Paul W. Davenport, Nicholas J. Napoli
Abstract
Respiratory airflow signals provide critical insight into breathing mechanics, yet conventional analysis methods remain limited in their ability to characterize the internal structure of individual breaths. Traditional approaches treat airflow as a quasi-periodic signal and rely on global descriptors such as tidal volume or peak flow, obscuring sub-breath events that reflect neuromuscular coordination and compensatory breathing strategies. This study introduces a parametric framework for decomposing inspiratory airflow into a small number of time-localized components with explicit amplitude, onset time, and duration parameters. Unlike spectral or data-adaptive methods, the proposed approach employs physiologically grounded basis functions, Half-Sine, Gaussian, and Beta, to represent intrabreath waveform morphology through constrained nonlinear optimization. Evaluation across 8,276 breaths demonstrates high reconstruction accuracy (mean squared error $<$ 0.001 for four-component models) and robust parameter precision under moderate noise. Component-derived features describing sub-breath timing and coordination improved classification of cognitive fatigue states arising from cognitive-respiratory competition by up to 30.7% in Matthews correlation coefficient compared with classical respiratory metrics. These results establish that modeling airflow as a sum of parameterized, time-localized primitives provides an interpretable and precise foundation for quantifying intrabreath organization, compensatory breathing dynamics, and respiratory motor control adaptation under cognitive-respiratory dual-task demands.