Browsing by Author "O'Connor C"

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    Heart Rate and Heart Rate Variability Change with Sleep Stage in Dairy Cows
    (MDPI (Basel, Switzerland), 2021-07-14) Hunter LB; Haskell MJ; Langford FM; O'Connor C; Webster JR; Stafford KJ; Van Eerdenburg FJCM
    Changes to the amount and patterns of sleep stages could be a useful tool to assess the effects of stress or changes to the environment in animal welfare research. However, the gold standard method, polysomnography PSG, is difficult to use with large animals such as dairy cows. Heart rate (HR) and heart rate variability (HRV) can be used to predict sleep stages in humans and could be useful as an easier method to identify sleep stages in cows. We compared the mean HR and HRV and lying posture of dairy cows at pasture and when housed, with sleep stages identified through PSG. HR and HRV were higher when cows were moving their heads or when lying flat on their side. Overall, mean HR decreased with depth of sleep. There was more variability in time between successive heart beats during REM sleep, and more variability in time between heart beats when cows were awake and in REM sleep. These shifts in HR measures between sleep stages followed similar patterns despite differences in mean HR between the groups. Our results show that HR and HRV measures could be a promising alternative method to PSG for assessing sleep in dairy cows.
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    Indicators of dehydration in healthy 4- to 5-day-old dairy calves deprived of feed and water for 24 hours
    (Elsevier BV for the American Dairy Science Association, 2020-12) Kells NJ; Beausoleil NJ; Johnson CB; Chambers JP; O'Connor C; Webster J; Laven R; Cogger N
    Our objective was to identify practical indicators of calf dehydration that could be used in an industry context. Eleven healthy 4-d-old commercial dairy calves were fed 2 L of mixed colostrum, then deprived of food and water for 24 h. Total body water was determined in the fed state using the deuterium dilution method. Body weight, along with a range of behavioral and physiological variables, was recorded 1 h after feeding, then at 90-min intervals through to 24 h. Blood samples were collected at every second sampling to assess changes in plasma hemoglobin, hematocrit, and osmolality. Linear mixed-effects models were used to explore associations between hydration status (% body water) and outcome variables. All calves remained bright and alert with good suckling reflexes throughout the 24-h period. After 24 h, total body water had decreased by an average of 8.4% (standard error 1.18), consistent with mild to moderate dehydration. Skin tent return time, capillary refill time, and detectable enophthalmos were associated with hydration status. Calves with skin tent return times of 3 s or longer were 4.4 percentage points less hydrated than those with return times of less than 3 s. Similarly, a capillary refill time of 3 s or longer was associated with a 4.3 percentage point reduction in hydration compared with refill times of less than 3 s. Calves with detectable enophthalmos (≥1 mm) were 3.5 percentage points less hydrated than those without enophthalmos. The skin tent, capillary refill, and enophthalmos tests are all relatively simple to perform and, although requiring the calf to be briefly restrained, can easily be performed by a single operator. The outcome of these tests was relatively consistent, in that calves above the threshold in any test were 3.5 to 4.5% less hydrated than calves below the threshold. As such, these tests may be of practical utility to identify calves with mild to moderate dehydration in an industry setting.
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    Machine learning prediction of sleep stages in dairy cows from heart rate and muscle activity measures.
    (Springer Nature Limited, 2021-05-25) Hunter LB; Baten A; Haskell MJ; Langford FM; O'Connor C; Webster JR; Stafford K
    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.