Recording Heart Rate Variability of Dairy Cows to the Cloud—Why Smartphones Provide Smart Solutions

In the last decades, there has been an increasing interest in animal protection and welfare issues. Heart rate variability (HRV) measurement with portable heart rate monitors on cows has established itself as a suitable method for assessing physiological states. However, more forward-looking technologies, already successfully applied to evaluate HRV data, are pushing the market. This study examines the validity and usability of collecting HRV data by exchanging the Polar watch V800 as a receiving unit of the data compared to a custom smartphone application on cows. Therefore, both receivers tap one signal sent by the Polar H7 transmitter simultaneously. Furthermore, there is a lack of suitable methods for the preparation and calculation of HRV parameters, especially for livestock. A method is presented for calculating more robust time domain HRV parameters via median formation. The comparisons of the respective simultaneous recordings were conducted after artifact correction for time domain HRV parameters. High correlations (r = 0.82–0.98) for cows as well as for control data set in human being (r = 0.98–0.99) were found. The utilization of smart devices and the robust method to determine time domain HRV parameters may be suitable to generate valid HRV data on cows in field-based settings

Advanced Signal Processing Methods for Animal Electrocardiography

Tato diplomová práce se zaměřuje na zpracování elektrokardiogramu (EKG) různých druhů zvířat za účelem sestavení křivky tepové frekvence v čase, vytvoření GUI (grafické uživatelské rozhraní) a význam variability srdeční frekvence ve veterinární praxi. Analýza variability srdeční frekvence je nepříliš prozkoumanou oblastí, která ale může mít velmi významný vliv například při trénování závodních zvířat nebo může mít zásadní vliv na dojivost u skotu. Měření elektrokardiogramu zvířat je velmi významně znehodnoceno pohybovými artefakty, proto se tato práce zaměřuje také na metody filtrace elektrokardiogramu zvířat, jejich srovnání a vyhodnocení. Navržený systém pro zpracování EKG kombinuje filtrační metody a algoritmus založený na kontinuální vlnkové transformaci, který se zde používá pro detekci R vrcholu EKG signálu. Studie je realizována na reálných datech z veterinární praxe z pohledu neinvazivního monitorování tepové frekvence u zvířat, náměr dat byl rovněž součástí této diplomové práce. Tato práce také zkoumá jiné možnosti zaznamenávání tepové frekvence u zvířat, mezi tyto metody patří například metoda měření pomocí balistokardiografie či kardiotokografie. Signály BKG se zde prokázaly jako nevhodné pro detekci tepové frekvence, jelikož obsahují velké množství artefaktů, pocházejících z neklidu zvířat a nemožnosti správného přichycení senzoru, jako je to u elektrod EKG. Měření pomocí KTG prokázalo srovnatelnou kvalitu zaznamenávání srdeční frekvence v čase.This diploma thesis is focused on ECG (electrocardiography) signal processing of different animal species for creation of heart rate curve, GUI (Graphical User Interface) and importance of heart rate variability in the field of veterinary medicine. Analysis of heart rate variability is not explored very well, but It can have huge impact in the field of animal training or milkiness of cows. The Measurement of electrocardiogram is loaded with motion artifacts, that is why this diploma thesis is comparing Filtration Methods and its evaluation. Proposed system for ECG signal processing combines filtration methods and algoritm based on continual wavelet transformation, which is used for R peak detection in this case. Study is realised on real dataset as noninvasive measurement of animal's heart rate, the measurement of data set was also part of this thesis. This thesis explores aalso other Methods for heart rate Assessment, for example balistocardiography or cardiotocography. The result proved that balistocardiography is not right tool for heart rate curve creation but cardiotocography has comparable results as extraction of heart rate curve from ECG.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn

A Wearable System for the Monitoring of Vital Functions of the Animals Base on Virtual Bio‑Instruments

Tato bakalářská práce se zabývá možnostmi nositelných systémů pro monitorování vitálních funkcí u zvířat na bázi virtuální bioinstrumentace. Teoretická část pojednává o současném stavu problematiky nositelných zařízení a metod monitorování, jež jsou využívány u zvířat. V rámci praktické části práce byl navržen měřící systém sestávající se z jednokanálového EKG zařízení, monitorující elektrickou aktivitu srdce, mikrofonů pro BKG snímající mechanické pohyby srdce, za cílem dlouhodobého monitorování koní v klidu i zátěži. Vzhledem k tomu, že monitorování koní či zvířat obecně je velmi náchylné k pohybovým artefaktům z důvodu neklidu, nespolupráce zvířete či ze samotného zátěžového měření, bylo zapotřebí experimentálně posoudit vhodnost použití BKG senzoru. Experimentem byla potvrzena teze, že BKG se nejeví jako vhodná metoda pro použití u zvířat, zejména pak při monitorování srdeční funkce zvířat v pohybu. Oproti tomu elektrokardiografie poskytuje poměrně robustní měřící metodu a v kombinaci s vhodným předzpracováním a detekčním algoritmem významných vrcholů v morfologii signálu je schopno poskytnout relevantní informaci o HR zvířete v zátěži.This bachelors thesis is about different options for wearable systems which are used for monitoring vital signs of animals on the basis of virtual bioinstrumentation. The theoretical part is based on the current state of wearable systems and different methods of monitoring that are applied when it comes to animal examination. Practical part is centered around designing a measuring system which consists of single-channel ECG which monitors the electric aktivity of the heart. Secondly it consists of microphones for BCG that are used to scan the mechanical movement of the heart. This system is designed for long term monitoring of horses when they are stationary and also when the are in motion. Given that monitoring animals or horses is generally very prone to motion artefacts because of the animal not cooperating or because of the load measurement itself. I had to experimentally verify the suitability of BCG sensor. The experiment confirmed the theory that it is not suitable for use in animal monitoring especially when the animal is in motion. In contrast the ECG gives us a quite robust measuring method and when we use suitable preprocessing and a detection algorithm which monitors significant peaks in signal morphology. It is able to provide relevant information about the HR of the animal under effort.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn

The development and assessment of novel non-invasive methods of measuring sleep in dairy cows : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Animal Science at Massey University, Manawatū, New Zealand

Onet published article in Appendix C was removed for copyright reasons, but may be accessed via its source: Hunter, L.B., O’Connor, C., Haskell, M.J., Langford, F.M., Webster, J.R., & Stafford, K.J. 2021, September. Lying posture does not accurately indicate sleep stage in dairy cows. Applied Animal Behaviour Science, 242, 105427. https://doi.org/10.1016/j.applanim.2021.105427Sleep is important for animal health and welfare and there are many factors, for example, the environment, illness, or stress, that are likely to have an impact on cow sleep and consequently affect their welfare. Polysomnography (PSG) is considered the gold standard for precise identification of sleep stages. It consists of electrophysiological recordings of the brain activity, eye movements and muscle activity but is costly and difficult to use with cows on farm. Accordingly, the study of sleep in cows may be limited due to the impracticability of PSG. Alternative methods of assessing sleep have been developed for humans. Some such work has been conducted for cows, but this has yet to be validated with PSG. The main aim of this thesis was to investigate alternative methods to PSG to accurately detect sleep stages in dairy cows. Specifically, I aimed to develop a detailed 5-stage scoring system for assessing sleep stages from the cow PSG, to investigate the suitability of using lying postures and heart rate (HR) measures to assess sleep stages and to develop a model to accurately predict sleep stages using non-invasive measures in dairy cows compared with PSG. Two studies were conducted using 6 non-lactating dairy cows in an indoor housed environment in Scotland, and outdoors at pasture in New Zealand. PSG was recorded with each cow over a period of seven days. From these data a 5-stage sleep-scoring criteria with good reliability was developed which identified two stages of light sleep, two stages of deep sleep as well as awake and rumination stages. Video was recorded during sleep recordings and the cow’s behaviour was analysed and compared with the scored sleep stages from the PSG. Some sleep stages were found to occur mainly in specific lying postures; however, overall, postures were not useful indicators of sleep stages. Heart rate (HR) and heart rate variability (HRV) were measured using a Polar HR monitor ii and data logging device. Differences in the HR and HRV measures were found between the sleep stages, and the patterns of these changes were similar between both Scottish and NZ cows. Finally, machine learning models were developed using supervised learning methods to predict sleep stage from the HR and HRV measures as well as the surface EMG data recorded during PSG. The models were able to learn to recognize and accurately predict sleep stages compared with the PSG scoring. This research demonstrates that non-invasive alternatives can be used to identify sleep stages accurately in dairy cows compared with PSG. Further research is necessary with larger sample sizes and cows of various breeds and life stages; however, the success of the methods developed during this thesis demonstrates their suitability for the future measurement of sleep in cows and in the assessment of cow welfare