Automated analysis of breathing waveforms using BreathMetrics: A respiratory signal processing toolbox

Torben Noto*, Guangyu Zhou, Stephan Schuele, Jessica Templer, Christina Zelano

*Corresponding author for this work

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

Nasal inhalation is the basis of olfactory perception and drives neural activity in olfactory and limbic brain regions. Therefore, our ability to investigate the neural underpinnings of olfaction and respiration can only be as good as our ability to characterize features of respiratory behavior. However, recordings of natural breathing are inherently nonstationary, nonsinusoidal, and idiosyncratic making feature extraction difficult to automate.The absence of a freely available computational tool for characterizing respiratory behavior is a hindrance to many facets of olfactory and respiratory neuroscience.To solve this problem, we developed BreathMetrics, an open-source tool that automatically extracts the full set of features embedded in human nasal airflow recordings. Here, we rigorously validate BreathMetrics’ feature estimation accuracy on multiple nasal airflow datasets, intracranial electrophysiological recordings of human olfactory cortex, and computational simulations of breathing signals. We hope this tool will allow researchers to ask new questions about how respiration relates to body, brain, and behavior.

Original languageEnglish (US)
Pages (from-to)583-597
Number of pages15
JournalChemical Senses
Volume43
Issue number8
DOIs
StatePublished - Jan 1 2018

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Keywords

  • Algorithm
  • Electrocorticography
  • Feature extraction
  • Humans
  • Olfaction
  • Respiration

ASJC Scopus subject areas

  • Physiology
  • Sensory Systems
  • Physiology (medical)
  • Behavioral Neuroscience

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