Machine learning prediction of sleep stages in dairy cows from heart rate and muscle activity measures.

Simple item page
dc.citation.issue1
dc.citation.volume11
dc.contributor.authorHunter LB
dc.contributor.authorBaten A
dc.contributor.authorHaskell MJ
dc.contributor.authorLangford FM
dc.contributor.authorO'Connor C
dc.contributor.authorWebster JR
dc.contributor.authorStafford K
dc.coverage.spatialEngland
dc.date.accessioned2024-11-06T19:59:30Z
dc.date.available2024-11-06T19:59:30Z
dc.date.issued2021-05-25
dc.description.abstractSleep 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.
dc.description.confidentialfalse
dc.format.pagination10938-
dc.identifier.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/34035392
dc.identifier.citationHunter LB, Baten A, Haskell MJ, Langford FM, O'Connor C, Webster JR, Stafford K. (2021). Machine learning prediction of sleep stages in dairy cows from heart rate and muscle activity measures.. Sci Rep. 11. 1. (pp. 10938-).
dc.identifier.doi10.1038/s41598-021-90416-y
dc.identifier.eissn2045-2322
dc.identifier.elements-typejournal-article
dc.identifier.issn2045-2322
dc.identifier.numberARTN 10938
dc.identifier.pii10.1038/s41598-021-90416-y
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/71939
dc.languageeng
dc.publisherSpringer Nature Limited
dc.publisher.urihttps://www.nature.com/articles/s41598-021-90416-y
dc.relation.isPartOfSci Rep
dc.rights(c) The author/sen
dc.rights.licenseCC BYen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectAnimals
dc.subjectCattle
dc.subjectHeart Rate
dc.subjectMachine Learning
dc.subjectModels, Biological
dc.subjectMyocardium
dc.subjectNeural Networks, Computer
dc.subjectPolysomnography
dc.subjectROC Curve
dc.subjectSleep Stages
dc.titleMachine learning prediction of sleep stages in dairy cows from heart rate and muscle activity measures.
dc.typeJournal article
pubs.elements-id445834
pubs.organisational-groupCollege of Health
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