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

Hunter, Laura B.; Baten, Abdul; Haskell, Marie J.; Langford, Fritha M.; O'Connor, C. E.; Webster, James R.; Stafford, Kevin J.

doi:10.1038/s41598-021-90416-y

Cited by 13 publications

(5 citation statements)

References 40 publications

(46 reference statements)

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“…Sleep has been analyzed by different authors for multiple contexts, that is, to avoid accidents during sleepwalking ( Damkliang et al, 2019 ) or on roads ( Chowdhury et al, 2019 ; Patrick et al, 2016 ) to understand sleep behavior and patterns ( Budak et al, 2019 ; Hunter et al, 2021 ; Zhang et al, 2022 ), to measure sleep quality ( Hunter et al, 2021 ), detection of sleep stages ( Gaiduk et al, 2018 ), related diseases ( Mitsukura et al, 2020 ; Zhang et al, 2022 ), etc . For example, Damkliang et al (2019) worked on the detection of the sleepwalking algorithm with three classes (No, Slow, Quick) that were part of the awake state of sleep.…”

Section: Literature Surveymentioning

confidence: 99%

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Sleep stage prediction using multimodal body network and circadian rhythm

Waqar,

Ghani Khan

2024

PeerJ Computer Science

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Quality sleep plays a vital role in living beings as it contributes extensively to the healing process and the removal of waste products from the body. Poor sleep may lead to depression, memory deficits, heart, and metabolic problems, etc. Sleep usually works in cycles and repeats itself by transitioning into different stages of sleep. This study is unique in that it uses wearable devices to collect multiple parameters from subjects and uses this information to predict sleep stages and sleep patterns. For the multivariate multiclass sleep stage prediction problem, we have experimented with both memoryless (ML) and memory-based models on seven database instances, that is, five from the collected dataset and two from the existing datasets. The Random Forest classifier outclassed the ML models that are LR, MLP, kNN, and SVM with accuracy (ACC) of 0.96 and Cohen Kappa 0.96, and the memory-based model long short-term memory (LSTM) performed well on all the datasets with the maximum attained accuracy of 0.88 and Kappa 0.82. The proposed methodology was also validated on a longitudinal dataset, the Multiethnic Study of Atherosclerosis (MESA), with ACC and Kappa of 0.75 and 0.64 for ML models and 0.86 and 0.78 for memory-based models, respectively, and from another benchmarked Apple Watch dataset available on Physio-Net with ACC and Kappa of 0.93 and 0.93 for ML and 0.92 and 0.87 for memory-based models, respectively. The given methodology showed better results than the original work and indicates that the memory-based method works better to capture the sleep pattern.

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Section: Literature Surveymentioning

confidence: 99%

“…Dataset collection was a challenge, and the same data imbalance issue persisted. The results showed the classification accuracy of 82% both for RF and neural network ( Hunter et al, 2021 ).…”

Section: Literature Surveymentioning

confidence: 99%

Sleep stage prediction using multimodal body network and circadian rhythm

Waqar,

Ghani Khan

2024

PeerJ Computer Science

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“…Sleep in dairy cows transcends mere rest. It is intrinsically linked to processes such as memory consolidation, metabolic regulation, and overall homeostasis [29,30]. The ruminant digestive system, unique to cows, further accentuates the importance of sleep, with disruptions leading to diminished milk yield, compromised immunity, and increased susceptibility to pathologies.…”

Section: The Physiological Imperative Of Sleepmentioning

confidence: 99%

Decoding the Digital Barnyard - Cognitive Computing in Farm Animal Emotions and Welfare

Neethirajan

2023

Preprint

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In this paper, we critically examine the burgeoning role of advanced computational methodologies in deciphering the complex tapestry of farm animal behaviors and emotions. Leveraging digital imaging and artificial intelligence, we unearth nuanced behavioral patterns and micro-expressions, offering predictive insights into animal emotional states. Sound vocalization analysis, often overlooked, emerges as a pivotal tool, decoding intricate communicative nuances and emotional undertones. Cognitive tests, including mirror and bias assessments, challenge long-standing perceptions, revealing surprising depths of animal self-awareness and cognitive sophistication. However, the paper also underscores the imperative of integrating these tools with a profound understanding of animal psyche, ensuring technology serves as an enhancer, not a replacement, of traditional observational methods. This research not only highlights the transformative potential of cognitive computing in animal welfare but also calls for a judicious application, ensuring technology augments, not undermines, the intrinsic value of human-animal interactions and understanding.

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“…Time domain features included mean HR (in beats per minute-BPM), root mean square of successive differences of the R-R signal (RMSSD), and standard deviation of the R-R signal (SDRR) in 30 s epochs. [20]). Pre-gelled adhesive snap ECG electrodes (Natus Neurology, Ottawa, ON, Canada) were used to record four EEG, a reference (REF), patient grounding (PGND), and two EOG and two EMG channels from the cows.…”

Section: Heart Rate Recordingmentioning

confidence: 99%

Heart Rate and Heart Rate Variability Change with Sleep Stage in Dairy Cows

Hunter

Haskell

Langford

et al. 2021

Animals

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

Cited by 13 publications

References 40 publications

Sleep stage prediction using multimodal body network and circadian rhythm

Sleep stage prediction using multimodal body network and circadian rhythm

Decoding the Digital Barnyard - Cognitive Computing in Farm Animal Emotions and Welfare

Heart Rate and Heart Rate Variability Change with Sleep Stage in Dairy Cows

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