Query: "heart rate variability" and glucose

title: Noninvasive Glucose Measurement Using Machine Learning and Neural Network Methods and Correlation with Heart Rate Variability
abstract: Diabetes is one of today's greatest global problems, and it is only becoming bigger. Constant measuring of blood glucose level is a prerequisite for monitoring glucose blood level and establishing diabetes treatment procedures. The usual way of glucose level measuring is by an invasive procedure that requires finger pricking with the lancet and might become painful and obeying, especially if this becomes a daily routine. In this study, we analyze noninvasive glucose measurement approaches and present several classification dimensions according to different criteria: size, invasiveness, analyzed media, sensing properties, applied method, activation type, response delay, measurement duration, and access to results. We set the focus on using machine learning and neural network methods and correlation with heart rate variability and electrocardiogram, as a new research and development trend.

title: Heart Rate Variability of 30 minute ECG Measurements and Correlation to Glucose Levels
abstract: This paper shows the correlation between Heart Rate Variability parameters and glucose levels, which might potentially lead to a novel non invasive glucose monitoring methodology. The study is conducted using a dataset with 143 patients from the Gluco project. The dataset consists of plain ECG data. Heart rate variability (HRV) parameters are then extracted from the electrocardiogram (ECG) data. Outlier handling, statistical measures and HRV distribution charts are covered in this paper. Our previous results show that a 24h ECG measurement will produce a more relevant result for the HRV parameters. In this paper, we investigate the hypothesis is: "if 30 minutes ECG measurements are relevant as an estimation of a body regulation ability instead of 24h measurements"

title: Blood glucose fluctuations in patients with coronary heart disease and diabetes mellitus correlates with heart rate variability: A retrospective analysis of 210 cases
abstract: This retrospective analysis aims to evaluate the correlation between blood glucose fluctuation (BGF) and heart rate variability (HRV) in patients with coronary heart disease (CHD) and type 2 diabetes mellitus (T2DM) Subjects  In total, 210 patients with CHD and T2DM from January 2014 to January 2019 admitted to Wenling Hospital of Traditional Chinese Medicine were enrolled in this study. Based on whether BGF existed, patients were allocated to BG control group and BG fluctuation group. The HRV parameters, frequency of adverse events, and Gensini score between groups were recorded and Pearson analysis was performed.  Results displayed that no significant differences in age, gender, body mass index (BMI) systolic blood pressure (SBP) diastolic blood pressure (DBP) alcohol consumption history, drinking history, or serum lipid were found between groups (P 0.05 for all items) However, the BGF parameters were significantly higher while the HRV parameters were significantly lower in BG fluctuation group, compared with BG control group (P 0.05 for all items) Pearson analysis showed that despite mean blood glucose (MBG) and mean amplitude of glycemic excursions (MAGE) both correlated with a standard deviation of NN intervals (SDNN) level, the correlation coefficient of MAGE SDNN was much higher 0.705 vs 0.185) Additionally, the frequencies of adverse events and Gensini scores were also significantly higher in the BG fluctuation group than the BG control group.  It suggests that BGF strongly correlated with HRV in patients with CHD and T2DM. It also provides experimental instructions for clinical practice.

title: Sexual Dimorphism of Heart Rate Variability in Adolescence: A Case Control Study on Depression, Anxiety, Stress Levels, Body Composition, and Heart Rate Variability in Adolescents with Impaired Fasting Glucose
abstract: Prediabetes in the form of impaired fasting glucose, impaired glucose tolerance, or both is considered as a preliminary stage for the onset of diabetes and diabetic complications. Hormonal fluctuations in adolescence are accompanied by body composition modifications, which are associated with insulin resistance and subclinical inflammation. Bioimpedance (BIA) accurately evaluates body composition, and heart rate variability (HRV) assesses cardiac autonomic function, which are frequently afflicted by insulin resistance. We aimed at evaluating the effect of glycemic status on mental stress, anxiety, and depression status in adolescents with impaired fasting glucose, body composition, and HRV parameters. This is a case control study to evaluate the effect of the hyperglycemia on depression, anxiety, and stress levels (DASS21 questionnaire) body composition (BIA ACC BIOTEKNA(c) and HRV (PPG Stress Flow BIOTEKNA(c) between euglycemic adolescents (euglycemic group) and adolescents with impaired fasting glucose (prediabetic group) aged 12 20 years. No differences were found between the prediabetic (n 13) and the euglycemic (n 16) groups in the outcome measures, possibly due to the number of participants. Interestingly, females, irrespective of their glycemic status, exhibited altered sympathovagal function as revealed by impaired HRV. In the euglycemic group, HRV parameters were significantly correlated and in line with the DASS21 scores, but in the prediabetic group, similarities to those of adults were observed. Impaired fasting glucose had no impact on mental health, body composition, or HRV parameters in adolescents. HRV parameters were impaired in females, irrespective of their glycemic status. This finding implies that females seem to be more prone to stress disorders, even from a young age. Future studies are needed to confirm these findings.

title: Correlating Heart Rate Variability to Glucose Levels
abstract: A study has been undertaken under the umbrella of a project focusing on noninvasive electrocardiogram (ECG) monitoring. The main goal of the study is to figure out which heart rate variability parameter has the ability to give the most accurate information for the Glucose level of a patient. The results show that for short term continuous measurements (minimum five minutes) and without extrasystoles or noise in the ECG signal the Root Mean Square Successive Differences parameter has demonstrated better correlation capabilities with a glucose level compared to other parameters. On the other hand, for long term measurements (hourly lengths) the better indicator came to be Standard Deviation of Normal to Normal intervals however, not the combined Standard Deviation of Normal to Normal intervals but the averages of Standard Deviations of Normal to Normal intervals calculated on separate sequences.

title: Rapid glucose rise reduces heart rate variability in adults with type 1 diabetes: A prospective secondary outcome analysis
abstract: To investigate differences in heart rate variability (HRV) during oral glucose tolerance tests (OGTTs) in response to the rate of change in glucose and to different glycaemic ranges in individuals with type 1 diabetes. This was a single centre, prospective, secondary outcome analysis in 17 individuals with type 1 diabetes (glycated haemoglobin 53 6.3 mmol/L) who underwent two OGTTs (after 12 and 36 hours of fasting) investigating differences in HRV in response to rapid glucose increases/decreases and different glycaemic ranges during OGTT. Based on the rate of change in glucose level, the variables heart rate (P 0.001) square root of the mean standard difference of successive R R intervals (P 0.002) percentage of pairs of R R intervals with >50 ms difference (P 0.001) and corrected QT interval (P 0.04) were significantly altered, with HRV particularly reduced during episodes of rapid glucose rises. Glycaemic ranges during OGTT had no impact on HRV (P 0.05) Individuals with type 1 diabetes showed no changes in HRV in response to different glycaemic ranges. HRV was dependent on the rate of change in glucose, especially rapid increases in glucose level.

title: Deep Learning Method to Estimate Glucose Level from Heart Rate Variability
abstract: Recently, there have been efforts by different researchers in implementing some Machine Learning (ML) and Deep Learning (DL) techniques in designing a model that will predict Glucose based solely on Heart Rate Variability parameters. However, each study uses an in house dataset and thus the results differ from the rest. The aim of this paper is to explore the predictive capabilities of DL techniques in designing a model to predict glucose regulation from Heart Rate Variability. The clinical study was conducted on a dataset of 155 patients with long term Electrocardiogram measurements. The best results are achieved with an architecture of three hidden layers (32, 256, and 64 neurons, respectively) with an Adam optimizer alongside a learning rate of 0.001 coupled by a Binary Cross entropy loss function. Furthermore, the Z score outlier removal method proved to lead to a higher accuracy value, whilst the IQR outlier removal method proved to lead to a higher Fl score value.

title: Correlating Short Term Heart Rate Variability and Instantaneous Blood Glucose Measurements
abstract: In this research, we aim to analyze the correlations between instantaneous blood glucose measurements and short term (30 seconds to 5 minutes) heart rate variability (HRV) parameters and check the research hypothesis that these HRV parameters indicate the ability of a patient to control the blood glucose level.  Pearson and Spearman's Rank correlations are used as methods within this paper. Although 155 patients with heart problems and arrhythmia were included in the study, only intervals between normal heartbeats were evaluated.  The HRV parameters correlated to the blood glucose levels are divided into time domain (TD) HRV parameters: SDNN, SDNN Prima (SDNN 1) ASDNN, ASDNN Prima (ASDNN 1) ASDNN Secunda (ASDNN 2) ASDNN Tertia (ASDNN 3) SDANN Secunda (SDANN 2) SDANN Tertia (SDANN 3) NN50, NN50 Prima (NN50 1) pNN50, pNN50 Prima (pNN50 1) rMSSD and rMSSD Prima (rMSSD 1) and non linear HRV parameters: SD1, SD2, SD1/SD2. Several methods were utilized to process the ECG signal strip and eliminate the ectopic beats, artifacts, lost signals, noise, and other segments that influence HRV metrics.  We found that SD1/SD2 showed the strongest positive correlations for 5 minute ECG recordings, 45 40 minutes before $r 0.36$ and 20 25 minutes after $r 0.31$ the fasting glucose measurement satisfying the condition $p$ 0.05. We conclude that short term HRV parameters can't indicate a strong linear or monotonic correlation to the blood glucose regulation ability, but can motivate further research including deep learning.

title: Association of glucose homeostasis measures with heart rate variability (HRV) among hyperlipidemic rat model: protective role of Emblica officinalis
abstract: Altered glucose homeostasis due to hyperlipidemia mainly revolves around pathophysiological effects of abnormal lipid levels and insulin resistance. Reduced HRV an indicator of cardiac autonomic function is connected with greater risk of cardiovascular diseases. Glucose homeostasis markers were related with reduced cardiac autonomic functions in diabetic rats but results reported were discordant with those without diabetes.  Study aimed to assess relation between glucose homeostasis with HRV among hyperlipidemic rats and to assess protective glucose regulatory actions of Emblica officinalis on hyperlipidemic rats.  Ethanolic extract of Emblica Officinalis (EEO) was prepared. Rats were divided into four groups (n=6) group 1 Control (20% fat) group 2 (20% fat+ EEO 100 mg/kg/b w) group 3 (30% fat) and group 4 (30% fat EEO 100 mg/kg/b w) for 21 days.Gravimetry and electrophysiological analysis (HRV and sympathovagal balance) were done. In vivo, tests on glucose regulatory system were conducted like fasting and postprandial glucose, fasting insulin levels and Oral Glucose Tolerance Test. Homeostasis Model Assessment of Insulin Resistance (HOMA IR) was calculated. ANOVA and Pearson's correlation was done to analyze the data.  Subchronic supplementation of EEO to hyperlipidemic rats showed a significant beneficial effect on altered glucose homeostasis. An electrophysiological evaluation revealed altered sympathovagal balance in hyperlipidemic rats which indicate cardiac autonomic malfunctions and found to be improved in EEO supplemented rats. A significant correlation was observed between HRV and glucose homeostasis.  Impairment in glucose homeostasis due to High fat diet is associated with altered heart rate variability. EEO supplementation found to be protective against high fat diet induced cardiac autonomic dysfunction and glucose metabolism.

title: Effect Of Energy Drink Consumption On Heart Rate Variability And Blood Glucose In Relation To Exercise
abstract: Consecutive 14 girl runners (16.6 0.7 y/o, 161.0 6.3 cm, 45.2 5.4 kg) in the same competitive high school team were evaluated without control on food and exercise. Each runner was asked to report dietary records with photos and training logs for 7 days. Energy intake (EI) was assessed by registered nutritionists. The runners were tested on treadmill with indirect calorimeter to yield individual prediction equations for VO2 by running velocity. Exercise energy expenditure (EEE) was calculated by the equations based on the training log. EA was calculated by subtracting EEE from EI for each day. The daily means of those variables were calculated. BMR was measured by whole room calorimeter in the early morning after overnight sleep at the night of the last day of the assessment inside the calorimeter. Body composition was measured by DXA. Bivariate correlation analyses and t test were used to examine the relationships and the difference between variables and groups, respectively.  Percent body fat %BF) fat free mass (FFM) and bone mineral density of TBLH (BMD) were 13.3 4.5 39.0 3.5 kg, and 1.013 0.04 g/cm, respectively. BMR, EI, EEE, and EA were 26.5 2.4 kcal/kg*FFM/day, 2330 479 kcal/day, 892 245 kcal/day, and 37.4 10.7 kcal/kg*FFM/day, respectively. EA was significantly correlated with BMR (r=0.60, p=0.02) However, EA was not correlated with either %BF (p=0.25) or BMD (p=0.16) and did not differ between runners with oligo/amenorrhea (n=7; 40.8 11.9) and eumenorrhea (n=6; 34.0 9.8) (p=0.29)  The strong linear correlation between EA and BMR supports the theory that decrease in EA leads to compensatory adaptation in metabolism which may suppress reproduction or bone mineral accrual. However, arbitrarily evaluated EA in short term was not related to the triad associated conditions. Those results were consistent with the evidences from adult Caucasian population.