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

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2541 ◽  
Author(s):  
Maren Wierig ◽  
Leonard Mandtler ◽  
Peter Rottmann ◽  
Viktor Stroh ◽  
Ute Müller ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Dairy Cows ◽  
Time Domain ◽  
Smartphone Application ◽  
Smart Devices ◽  
Control Data ◽  
Data Set ◽  
Heart Rate Monitors ◽  
Animal Protection

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.

Validity of a Smartphone Application and Chest Strap for Recording RR Intervals at Rest in Athletes

2020 ◽  
Vol 15 (6) ◽  
pp. 896-899
Author(s):  
Reabias de A. Pereira ◽  
José Luiz de B. Alves ◽  
João Henrique da C. Silva ◽  
Matheus da S. Costa ◽  
Alexandre S. Silva
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Frequency Domain ◽  
Time Domain ◽  
High Frequency ◽  
Low Frequency ◽  
Smartphone Application ◽  
Smartphone App ◽  
Heart Rate Variability Analysis ◽  
Rr Intervals

Objective: To evaluate the accuracy of the smartphone application (app) HRV Expert (CardioMood) and a chest strap (H10 Polar) for recording R-R intervals compared with electrocardiogram (ECG). Methods: A total of 31 male recreational runners (age 36.1 [6.3] y) volunteered for this study. R-R intervals were recorded simultaneously by the smartphone app and ECG for 5 minutes to analyze heart-rate variability in both the supine and sitting positions. Time-domain indexes (heart rate, mean R-R, SD of RR intervals, count of successive normal R-R intervals differing by more than 50 ms, percentage of successive normal R-R intervals differing by more than 50 ms, and root mean square of successive differences between normal R-R intervals), frequency-domain indexes (low frequency, normalized low frequency, high frequency, normalized high frequency, low-frequency to high-frequency ratio, and very low frequency), and nonlinear indexes (SD of instantaneous beat-to-beat variability and long-term SD of continuous R-R intervals) were compared by unpaired t test, Pearson correlation, simple linear regression, and Bland–Altman plot to evaluate the agreement between the devices. Results: High similarity with P value varying between .97 and 1.0 in both positions was found. The correlation coefficient of the heart-rate-variability indexes was perfect (r = 1.0; P = .00) for all variables. The constant error, standard error of estimation, and limits of agreement between ECG and the smartphone app were considered small. Conclusion: The smartphone app and chest strap provide excellent ECG compliance for all variables in the time domain, frequency domain, and nonlinear indexes, regardless of the assessed position. Therefore, the smartphone app replaces ECG for any heart-rate-variability analysis in runners.

scholarly journals Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3461
Author(s):  
Blake Anthony Hickey ◽  
Taryn Chalmers ◽  
Phillip Newton ◽  
Chin-Teng Lin ◽  
David Sibbritt ◽  
...  
Keyword(s):  
Mental Health ◽  
Systematic Review ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Electrodermal Activity ◽  
Smart Devices ◽  
Stress Detection ◽  
Wearable Technologies ◽  
Detection Of Stress ◽  
Anxiety Depression

Recently, there has been an increase in the production of devices to monitor mental health and stress as means for expediting detection, and subsequent management of these conditions. The objective of this review is to identify and critically appraise the most recent smart devices and wearable technologies used to identify depression, anxiety, and stress, and the physiological process(es) linked to their detection. The MEDLINE, CINAHL, Cochrane Central, and PsycINFO databases were used to identify studies which utilised smart devices and wearable technologies to detect or monitor anxiety, depression, or stress. The included articles that assessed stress and anxiety unanimously used heart rate variability (HRV) parameters for detection of anxiety and stress, with the latter better detected by HRV and electroencephalogram (EGG) together. Electrodermal activity was used in recent studies, with high accuracy for stress detection; however, with questionable reliability. Depression was found to be largely detected using specific EEG signatures; however, devices detecting depression using EEG are not currently available on the market. This systematic review highlights that average heart rate used by many commercially available smart devices is not as accurate in the detection of stress and anxiety compared with heart rate variability, electrodermal activity, and possibly respiratory rate.

scholarly journals Heart rate variability: Analysis of time-domain indices in patients with chronic Chagas disease before and after an exercise program

2013 ◽  
Vol 32 (3) ◽  
pp. 219-227 ◽  
Author(s):  
Marcus Vinicius Amaral da Silva Souza ◽  
Carla Cristiane Santos Soares ◽  
Juliana Rega de Oliveira ◽  
Cláudia Rosa de Oliveira ◽  
Paloma Hargreaves Fialho ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Chagas Disease ◽  
Time Domain ◽  
Exercise Program ◽  
Heart Rate Variability Analysis ◽  
Variability Analysis ◽  
Before And After ◽  
Chronic Chagas Disease

Effect of short-term mental stress on time- domain indices of heart rate variability in obese individuals – A case- control study

Biomedicine ◽  
2021 ◽  
Vol 41 (2) ◽  
pp. 274-277
Author(s):  
Priya S.A. ◽  
R. Rajalakshmi
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Neuronal Activity ◽  
Time Domain ◽  
Mental Stress ◽  
Mental Arithmetic ◽  
High Body Mass Index ◽  
Control Group ◽  
Stress Task ◽  
The Individual

  Introduction and Aim: Mental stress may impact dramatically on dynamic autonomic control on heart. Many studies have demonstrated association of high body mass index (BMI) with greater risk for cardiovascular disease with disturbance in autonomic neuronal activity. Analysis of Heart rate variability (HRV)during acute mental stress assesses the autonomic status of the individual. Hence, we aimed to study the effect of acute mental stress on time domain measures in obese adults.   Materials and Methods:Sixty male volunteers of 30 each in study group (obese individuals) and control group (non-obese individuals) were recruited for the study. A basal recording of ECG in lead II was done on all the individuals. Then they underwent mental arithmetic stress task for 5 minutes during which again ECG was recorded. The change in time domain measures of HRV during rest and stress task was analyzed and compared between both the groups.   Results: Analysis of time domain measures of HRV revealed a statistically significant increase (p ? 0.001) in mean heart rate in both obese and non-obese individuals, while rMSSD(root mean square differences of successive RR interval) and SDNN (standard deviation of all NN intervals) showed a statistically significant (p? 0.001) decrease in obese individuals and non-obese individuals did not show any statistically significant change during the mental stress task.   Conclusion: In response to acute mental stress there was increased heart rate in both the groups. But the autonomic neuronal activity differed by way of sympathetic dominance in non-obese individuals and parasympathetic withdrawal in obese individuals.  

Trends in Heart Rate and Heart Rate Variability during Pregnancy and Three Months Postpartum: Continuous Monitoring in a Free-Living Context (Preprint)

2021 ◽  
Author(s):  
Fatemeh Sarhaddi ◽  
Iman Azimi ◽  
Anna Axelin ◽  
Hannakaisa Niela-Vilen ◽  
Pasi Liljeberg ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Pregnant Women ◽  
Time Domain ◽  
Second Trimester ◽  
Third Trimester ◽  
Non Invasive ◽  
The Third ◽  
Invasive Method ◽  
Frequency Power

BACKGROUND Heart rate variability (HRV) is a non-invasive method reflecting autonomic nervous system (ANS) regulations. Altered HRV is associated with adverse mental or physical health complications. ANS also has a central role in physiological adaption during pregnancy causing normal changes in HRV. OBJECTIVE Assessing trends in heart rate (HR) and HRV parameters as a non-invasive method for remote maternal health monitoring during pregnancy and three months postpartum. METHODS Fifty-eight pregnant women were monitored using an Internet-of-Things (IoT)-based remote monitoring system during pregnancy and 3-months postpartum. Pregnant women were asked to continuously wear Gear sport smartwatch to monitor their HR and HRV. In addition, a cross-platform mobile application was utilized for collecting pregnancy-related information. The trends of HR and HRV parameters were extracted using reliable data. We also analyzed the trends of normalized HRV parameters based on HR to remove the effect of HR changes on HRV trends. Finally, we exploited hierarchical linear mixed models to analyze the trends of HR, HRV, and normalized HRV parameters. RESULTS HR increased significantly during the second trimester (P<.001) and decreased significantly during the third trimester (P<.01). Time-domain HRV parameters, average normal interbeat intervals (AVNN), standard deviation of normal interbeat intervals (SDNN), root mean square of the successive difference of normal interbeat intervals (RMSSD), normalized SDNN (nSDNN), and normalized RMSSD (nRMSSD) decreased significantly during the second trimester (P<.001) then increased significantly during the third trimester (P<.01). Some of the frequency domain parameters, low-frequency power (LF), high-frequency power (HF), and normalized HF (nHF) decreased significantly during the second trimester (P<.01), and HF increased significantly during the third trimester (P<.01). In the postpartum period, nRMSSD decreased (P<.05), and the LF to HF ratio (LF/HF) increased significantly (P<.01). CONCLUSIONS Our study showed that HR increased and HRV parameters decreased as the pregnancy proceeded, and the values returned to normal after the delivery. Moreover, our results show that HR started to decrease while time-domain HRV parameters and HF started to increase during the third trimester. Our results also demonstrate the possibility of continuous HRV monitoring in everyday life settings.

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