Time-Localized Parametric Decomposition of Respiratory Airflow for Sub-Breath Analysis

TL;DR

This paper presents a physiologically grounded parametric framework for decomposing respiratory airflow into interpretable, time-localized components, enabling detailed analysis of sub-breath events and improving cognitive fatigue classification.

Contribution

The study introduces a novel, physiologically based parametric decomposition method for respiratory airflow that enhances sub-breath analysis and classification accuracy over traditional metrics.

Findings

High reconstruction accuracy with mean squared error < 0.001 for four-component models

Robust parameter estimation under moderate noise conditions

Improved cognitive fatigue classification by up to 30.7% using sub-breath features

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.