When a person breathes, air doesn't flow in a steady stream but in waves. Previously, doctors and scientists only looked at overall numbers: how much air entered per breath, what the peak flow was. But within a single breath, there are nuances — small variations that speak to the workings of nerves and muscles. The new approach breaks a breath into parts, much like a musician breaks a chord into individual notes.
The authors devised three wave shapes: half a sine wave, a bell curve, and a curve with a gradual rise. The computer fits these waves so that their sum accurately replicates the actual airflow. On 8,276 breaths, the error was minuscule — less than one-thousandth. Even when noise is added to the measurements, the method remains reliable.
This decomposition of breathing into simple waves is like reading a musical score instead of listening to the whole piece. Doctors will be able to monitor how the brain controls breathing more precisely and spot disruptions earlier.
as written by the authors arXiv:2604.22695
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.
A person takes about 20,000 breaths a day, and each one can be decomposed into several simple waves.