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.
Highlights
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
Most of the sleep time consists of NREM sleep stages and it is likely that these stages serve important functions for cows as they do humans
EEG is the recognized ‘gold standard’ to determine sleep stages a complicated and painstaking setup is required which makes it prohibitive to use for determining sleep stages in cows
Summary
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 aim of this project was to determine if we could accurately differentiate between different stages of light and deep sleep in dairy cows using only HR and neck muscle EMG data, compared to visual scoring of the PSG, and to compare the success of two machine learning algorithms in this task.
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