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Lookup NU author(s): Dr Fritha LangfordORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2021, The Author(s). Sleep is important for cow health and shows promise as a tool for assessing welfare, but methods to accurately distinguish between important sleep stages are difficult and impractical to use with cattle in typical farm environments. The objective of this study was to determine if data from more easily applied non-invasive devices assessing neck muscle activity and heart rate (HR) alone could be used to differentiate between sleep stages. We developed, trained, and compared two machine learning models using neural networks and random forest algorithms to predict sleep stages from 15 variables (features) of the muscle activity and HR data collected from 12 cows in two environments. Using k-fold cross validation we compared the success of the models to the gold standard, Polysomnography (PSG). Overall, both models learned from the data and were able to accurately predict sleep stages from HR and muscle activity alone with classification accuracy in the range of similar human models. Further research is required to validate the models with a larger sample size, but the proposed methodology appears to give an accurate representation of sleep stages in cattle and could consequentially enable future sleep research into conditions affecting cow sleep and welfare.
Author(s): Hunter LB, Baten A, Haskell MJ, Langford FM, O'Connor C, Webster JR, Stafford K
Publication type: Article
Publication status: Published
Journal: Scientific Reports
Year: 2021
Volume: 11
Issue: 1
Online publication date: 25/05/2021
Acceptance date: 07/05/2021
Date deposited: 03/09/2024
ISSN (electronic): 2045-2322
Publisher: Nature Research
URL: https://doi.org/10.1038/s41598-021-90416-y
DOI: 10.1038/s41598-021-90416-y
PubMed id: 34035392
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