A Hilbert-based method for processing respiratory timeseries
Samuel J. Harrison,
Samuel Bianchi,
Jakob Heinzle,
Klaas Enno Stephan,
Sandra Iglesias,
Lars Kasper
Affiliations
Samuel J. Harrison
Corresponding author at: Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Wilfriedstrasse 6, 8032 Zurich, Switzerland.; Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Zurich, Switzerland; FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
Samuel Bianchi
Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Zurich, Switzerland; Institute for Biomedical Engineering, ETH Zurich & University of Zurich, Zurich, Switzerland
Jakob Heinzle
Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Zurich, Switzerland
Klaas Enno Stephan
Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Zurich, Switzerland; Max Planck Institute for Metabolism Research, Cologne, Germany; Techna Institute, University Health Network, Toronto, Canada
Sandra Iglesias
Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Zurich, Switzerland
Lars Kasper
Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Zurich, Switzerland; Institute for Biomedical Engineering, ETH Zurich & University of Zurich, Zurich, Switzerland; Techna Institute, University Health Network, Toronto, Canada
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 is publicly available as part of the PhysIO package, which is distributed as part of the open-source TAPAS toolbox (https://translationalneuromodeling.org/tapas).
