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

Abstract 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...

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Autores principales:	Laura B. Hunter, Abdul Baten, Marie J. Haskell, Fritha M. Langford, Cheryl O’Connor, James R. Webster, Kevin Stafford
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5eae7fc7b5dc4617a3bbad15e0984628
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spelling	oai:doaj.org-article:5eae7fc7b5dc4617a3bbad15e09846282021-12-02T15:00:55ZMachine learning prediction of sleep stages in dairy cows from heart rate and muscle activity measures10.1038/s41598-021-90416-y2045-2322https://doaj.org/article/5eae7fc7b5dc4617a3bbad15e09846282021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90416-yhttps://doaj.org/toc/2045-2322Abstract 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.Laura B. HunterAbdul BatenMarie J. HaskellFritha M. LangfordCheryl O’ConnorJames R. WebsterKevin StaffordNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Laura B. Hunter Abdul Baten Marie J. Haskell Fritha M. Langford Cheryl O’Connor James R. Webster Kevin Stafford Machine learning prediction of sleep stages in dairy cows from heart rate and muscle activity measures
description	Abstract 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.
format	article
author	Laura B. Hunter Abdul Baten Marie J. Haskell Fritha M. Langford Cheryl O’Connor James R. Webster Kevin Stafford
author_facet	Laura B. Hunter Abdul Baten Marie J. Haskell Fritha M. Langford Cheryl O’Connor James R. Webster Kevin Stafford
author_sort	Laura B. Hunter
title	Machine learning prediction of sleep stages in dairy cows from heart rate and muscle activity measures
title_short	Machine learning prediction of sleep stages in dairy cows from heart rate and muscle activity measures
title_full	Machine learning prediction of sleep stages in dairy cows from heart rate and muscle activity measures
title_fullStr	Machine learning prediction of sleep stages in dairy cows from heart rate and muscle activity measures
title_full_unstemmed	Machine learning prediction of sleep stages in dairy cows from heart rate and muscle activity measures
title_sort	machine learning prediction of sleep stages in dairy cows from heart rate and muscle activity measures
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5eae7fc7b5dc4617a3bbad15e0984628
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Machine learning prediction of sleep stages in dairy cows from heart rate and muscle activity measures

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