Background: Respiratory rate is an essential parameter in biomedical research and clinical applications. Most respiration measurement techniques in preclinical animal models require surgical implantation of sensors. Current clinical measurement modalities typically involve attachment of sensors to the patient, causing discomfort. We have previously developed a non-contact approach to measuring respiration phase in head-restrained rodents using infrared (IR) thermography. While the non-invasive nature of IR thermography offers many advantages, it also bears the complexity of extracting respiration signals from videos. Previously reported algorithms involve image segmentation to identify the nose in IR videos and extract breathing-relevant pixels which is particularly challenging if the videos have low contrast or suffer from suboptimal focusing.
New method: To address this challenge, we developed a novel algorithm, which extracts respiration signals based on pixel time series, removing the need for nose-tracking and image segmentation.
Results & comparison with existing methods: We validated the algorithm by performing respiration measurements in head-restrained mice and in humans with IR thermography in parallel with established standard techniques. We find the algorithm reliably detects inhalation onsets with high temporal precision.
Conclusions: The new algorithm facilitates the application of IR thermography for measuring respiration in biomedical research and in clinical settings.
Keywords: Exploratory sniffing; Inhalation; Patient monitoring.
Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.