Increasing sensitivity in the measurement of heart rate variability: the method of non-stationary RR time-frequency analysis

A method overview in smart aquaculture

Aquaculture is an important part of agricultural economy. In the past, major farming accidents often occurred due to subjective experience. There are many factors affecting the water quality of aquaculture. Maintaining an ecological environment with good water quality is the most critical link to ensure the production efficiency and quality of aquaculture. With the continuous development of science and technology, intelligence and informatization in aquaculture has become a new trend. Smart aquaculture cannot only realize real-time monitoring, prediction, warning, and risk control of the physical and chemical factors of the aquaculture environment but can also conduct real-time monitoring of the characteristics and behaviors of the fish, which infers the changes of the aquaculture ecological environment. In this paper, the research achievements over past two decades both are summarized from four aspects: water quality factor acquisition and pre-processing, water quality factor prediction, morphological characteristics, and behavioral characteristic recognition of fish and the mechanism between fish behavior and water quality factors. The advantages and disadvantages of existing research routes, algorithm models, and research methods in smart aquaculture are summarized. The work in this paper can provide a well-organized and summative knowledge reference for further study on the dynamic mechanism between the changes of water quality factors and the fish body characteristics and behavior. Meanwhile, the work can also provide valuable reference for promoting the smart, ecological, and efficient development of aquaculture.

Physiological and Behavioral Responses of Horses to Wither Scratching and Patting the Neck When Under Saddle

Riding is considered to be an arousing activity for horses. It has been suggested that wither scratching may be a more useful tool for relaxation compared with the common practice of neck patting. In the current study, 18 horses were exposed to 3 treatments, including control or no interaction, neck patting, and wither scratching, for 1 min each following a short obstacle course. Heart rate, heart rate variability, and a variety of behaviors were measured in the horses. Wither scratching produced a significantly longer duration of relaxed-type behaviors. Wither scratching could be a useful tool to help a horse relax while under saddle. Additionally, the study identified 2 ear positions that may be useful for future research in horse behavior.

Poor glycemic control impacts linear and non-linear dynamics of heart rate in DM type 2

INTRODUCTION: It is well known that type 2 diabetes mellitus (T2DM) produces cardiovascular autonomic neuropathy (CAN), which may affect the cardiac autonomic modulation. However, it is unclear whether the lack of glycemic control in T2DM without CAN could impact negatively on cardiac autonomic modulation. Objective: To evaluate the relationship between glycemic control and cardiac autonomic modulation in individuals with T2DM without CAN. Descriptive, prospective and cross sectional study.METHODS: Forty-nine patients with T2DM (51±7 years) were divided into two groups according to glycosylated hemoglobin (HbA1c): G1≤7% and G2>7.0%. Resting heart rate (HR) and RR interval (RRi) were obtained and calculated by linear (Mean iRR; Mean HR; rMSSD; STD RR; LF; HF; LF/HF, TINN and RR Tri,) and non-linear (SD1; SD2; DFα1; DFα2, Shannon entropy; ApEn; SampEn and CD) methods of heart rate variability (HRV). Insulin, HOMA-IR, fasting glucose and HbA1c were obtained by blood tests.RESULTS: G2 (HbA1c≤7%) showed lower values for the mean of iRR; STD RR; RR Tri, TINN, SD2, CD and higher mean HR when compared with G1 (HbA1c > 7%). Additionally, HbA1c correlated negatively with mean RRi (r=0.28, p=0.044); STD RR (r=0.33, p=0.017); RR Tri (r=-0.35, p=0.013), SD2 (r=-0.39, p=0.004) and positively with mean HR (r=0.28, p=0.045). Finally, fasting glucose correlated negatively with STD RR (r=-0.36, p=0.010); RR Tri (r=-0.36, p=0.010); TINN (r=-0.33, p=0.019) and SD2 (r=-0.42, p=0.002).CONCLUSION: We concluded that poor glycemic control is related to cardiac autonomic modulation indices in individuals with T2DM even if they do not present cardiovascular autonomic neuropathy.

gHRV: Heart rate variability analysis made easy

In this paper, the gHRV software tool is presented. It is a simple, free and portable tool developed in python for analysing heart rate variability. It includes a graphical user interface and it can import files in multiple formats, analyse time intervals in the signal, test statistical significance and export the results. This paper also contains, as an example of use, a clinical analysis performed with the gHRV tool, namely to determine whether the heart rate variability indexes change across different stages of sleep. Results from tests completed by researchers who have tried gHRV are also explained: in general the application was positively valued and results reflect a high level of satisfaction. gHRV is in continuous development and new versions will include suggestions made by testers.