A Hilbert-based method for processing respiratory timeseries

In this technical note, we introduce a new method for estimating changes in respiratory volume per unit time (RVT) from respiratory bellows recordings. By using techniques from the electrophysiological literature, in particular the Hilbert transform, we show how we can better characterise breathing rhythms, with the goal of improving physiological noise correction in functional magnetic resonance imaging (fMRI). Specifically, our approach leads to a representation with higher time resolution and better captures atypical breathing events than current peak-based RVT estimators. Finally, we demonstrate that this leads to an increase in the amount of respiration-related variance removed from fMRI data when used as part of a typical preprocessing pipeline. Our implementation will be publicly available as part of the PhysIO package, which is distributed as part of the open-source TAPAS toolbox (translationalneuromodeling.org/tapas). HighlightsO_LIWe introduce a new estimator for respiratory volume per unit time from respiratory recordings. C_LIO_LIWe demonstrate how this is able to accurately characterise atypical breathing events. C_LIO_LIThis removes significantly more variance when used as a confound regressor for fMRI data. C_LIO_LIOur implementation will be included in PhysIO, released as part of TAPAS: translationalneuromodeling.org/tapas. C_LI