Heart rate variability (HRV) is a reliable reflection of the many physiological factors modulating the normal rhythm of the heart. In fact, they provide a powerful means of observing the interplay between the sympathetic and parasympathetic nervous systems. It shows that the structure generating the signal is not only simply linear, but also involves nonlinear contributions. Heart rate (HR) is a nonstationary signal; its variation may contain indicators of current disease, or warnings about impending cardiac diseases. The indicators may be present at all times or may occur at random-during certain intervals of the day. It is strenuous and time consuming to study and pinpoint abnormalities in voluminous data collected over several hours. Hence, HR variation analysis (instantaneous HR against time axis) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system. Computer based analytical tools for in-depth study of data over daylong intervals can be very useful in diagnostics. Therefore, the HRV signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. In this paper, we have discussed the various applications of HRV and different linear, frequency domain, wavelet domain, nonlinear techniques used for the analysis of the HRV.

Heart rate variability: a review

Excess intra-abdominal adipose tissue accumulation, often termed visceral obesity, is part of a phenotype including dysfunctional subcutaneous adipose tissue expansion and ectopic triglyceride storage closely related to clustering cardiometabolic risk factors. Hypertriglyceridemia; increased free fatty acid availability; adipose tissue release of proinflammatory cytokines; liver insulin resistance and inflammation; increased liver VLDL synthesis and secretion; reduced clearance of triglyceride-rich lipoproteins; presence of small, dense LDL particles; and reduced HDL cholesterol levels are among the many metabolic alterations closely related to this condition. Age, gender, genetics, and ethnicity are broad etiological factors contributing to variation in visceral adipose tissue accumulation. Specific mechanisms responsible for proportionally increased visceral fat storage when facing positive energy balance and weight gain may involve sex hormones, local cortisol production in abdominal adipose tissues, endocannabinoids, growth hormone, and dietary fructose. Physiological characteristics of abdominal adipose tissues such as adipocyte size and number, lipolytic responsiveness, lipid storage capacity, and inflammatory cytokine production are significant correlates and even possible determinants of the increased cardiometabolic risk associated with visceral obesity. Thiazolidinediones, estrogen replacement in postmenopausal women, and testosterone replacement in androgen-deficient men have been shown to favorably modulate body fat distribution and cardiometabolic risk to various degrees. However, some of these therapies must now be considered in the context of their serious side effects. Lifestyle interventions leading to weight loss generally induce preferential mobilization of visceral fat. In clinical practice, measuring waist circumference in addition to the body mass index could be helpful for the identification and management of a subgroup of overweight or obese patients at high cardiometabolic risk.

Pathophysiology of Human Visceral Obesity: An Update

EVOLUTION: Of Mice . . .

Over 7,000 peer reviewed articles have been published on electrochemical glucose assays and sensors over recent years. Their number makes a full review of the literature, or even of the most recent advances, impossible. Nevertheless, this chapter should acquaint the reader with the fundamentals of the electrochemistry of glucose and provide a perspective of the evolution of the electrochemical glucose assays and monitors helping diabetic people, who constitute about 5 % of the world’s population. Because of the large number of diabetic people, no assay is performed more frequently than that of glucose. Most of these assays are electrochemical. The reader interested also in nonelectrochemical assays used in, or proposed for, the management of diabetes is referred to a 2007 excellent review of Kondepati and Heise [1].

Electrochemical glucose sensors and their applications in diabetes management.

Defining and reporting hypoglycemia in diabetes: a report from the American Diabetes Association Workgroup on Hypoglycemia.

OBJECTIVE—To compare the clinical accuracy of two different continuous glucose sensors (CGS) during euglycemia and hypoglycemia using continuous glucose–error grid analysis (CG-EGA). RESEARCH DESIGN AND METHODS—FreeStyle Navigator (Abbott Laboratories, Alameda, CA) and MiniMed CGMS (Medtronic, Northridge, CA) CGSs were applied to the abdomens of 16 type 1 diabetic subjects (age 42 ± 3 years) 12 h before the initiation of the study. Each system was calibrated according to the manufacturer’s recommendations. Each subject underwent a hyperinsulinemic-euglycemic clamp (blood glucose goal 110 mg/dl) for 70–210 min followed by a 1–mg · dl−1 · min−1 controlled reduction in blood glucose toward a nadir of 40 mg/dl. Arterialized blood glucose was determined every 5 min using a Beckman Glucose Analyzer (Fullerton, CA). CGS glucose recordings were matched to the reference blood glucose with 30-s precision, and rates of glucose change were calculated for 5-min intervals. CG-EGA was used to quantify the clinical accuracy of both systems by estimating combined point and rate accuracy of each system in the euglycemic (70−180 mg/dl) and hypoglycemic ( RESULTS—A total of 1,104 data pairs were recorded in the euglycemic range and 250 data pairs in the hypoglycemic range. Overall correlation between CGS and reference glucose was similar for both systems (Navigator, r = 0.84; CGMS, r = 0.79, NS). During euglycemia, both CGS systems had similar clinical accuracy (Navigator zones A + B, 88.8%; CGMS zones A + B, 89.3%, NS). However, during hypoglycemia, the Navigator was significantly more clinically accurate than the CGMS (zones A + B = 82.4 vs. 61.6%, Navigator and CGMS, respectively, P CONCLUSIONS—CG-EGA is a helpful tool for evaluating and comparing the clinical accuracy of CGS systems in different blood glucose ranges. CG-EGA provides accuracy details beyond other methods of evaluation, including correlational analysis and the original EGA.

https://care.diabetesjournals.org/content/diacare/28/10/2412.full.pdf

Evaluating the clinical accuracy of two continuous glucose sensors using continuous glucose-error grid analysis.

Patients with Insulin-Dependent Diabetes are continuously involved in a clinical optimization process: to maintain strict glycemic control without increasing their risk for hypoglycemia. This study offers quantitative tools for on-line assessment of the quality of this optimization, based on self-monitoring of blood glucose (SMBG). Ninety-six adults with Insulin Dependent Diabetes Mellitus (IDDM), age 35 ± 8 yrs., duration of diabetes 16 ± 10 yrs., HbAlc 8.6 ± 1.8%, 43 of whom had a recent history of severe hypoglycemia (SH), while 53 did not, used Lifescan One Touch II meters for 135 ± 53 SMBG readings over a month. For the following six months the subjects recorded occurrence of SH. The two patient groups, with and without a history of SH, did not differ in age, duration of diabetes, HbAlc, insulin units/day, average BG or BG variability. We suggest a computational procedure based on a symmetrization of the BG measurement scale and on a superimposed BG risk function, that allows for computation of two glycemic control markers: the Low BG Index (LBGI) and the High BG Index (HBGI). The LBGI is associated with SH: the LBGI and the rate of change of the BG risk, classified correctly 77% of the subjects with vs. without a history of SH and accounted for 46% of the variance of future SH. The HBGI, in combination with age, duration of diabetes and daily insulin dose, accounted for 57% of the variance of patients' glycosylated hemoglobin. We conclude that the LBGI and the HBGI are accurate on-line SMBG measures for patients' glycemic control.

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Risk Analysis of Blood Glucose Data: A Quantitative Approach to Optimizing the Control of Insulin Dependent Diabetes

We introduce the sample asymmetry analysis (SAA) and illustrate its utility for assessment of heart rate characteristics occurring early in the course of neonatal sepsis and systemic inflammatory response syndrome (SIRS) Conceptually, SAA describes changes in the shape of the histogram of RR intervals that are caused by reduced accelerations and/or transient decelerations of heart rate Unlike other measures of heart rate variability, SAA allows separate quantification of the contribution of accelerations and decelerations The application of SAA is exemplified by a study comparing 50 infants, who experienced a total of 75 episodes of sepsis and SIRS, with 50 control infants The two groups were matched by birth weight and gestational age RR intervals were recorded for all infants throughout their course in the Neonatal Intensive Care Unit The sample asymmetry of the RR intervals increased in the 3-4 d preceding sepsis and SIRS, with the steepest increase in the last 24 h, from a baseline value of 33 (SD = 16) to 42 (SD = 23), p = 002 After treatment and recovery, sample asymmetry returned to its baseline value of 33 (SD = 13) The difference between sample asymmetry in health and before sepsis and SIRS was mainly due to fewer accelerations than to decelerations Compared with healthy infants, infants who experienced sepsis had similar sample asymmetry in health, and elevated values before sepsis and SIRS (p = 0002) We conclude that SAA is a useful new mathematical technique for detecting the abnormal heart rate characteristics that precede neonatal sepsis and SIRS

/pdf/sample-asymmetry-analysis-of-heart-rate-characteristics-with-3wdntf4fuw.pdf

Sample Asymmetry Analysis of Heart Rate Characteristics with Application to Neonatal Sepsis and Systemic Inflammatory Response Syndrome

Publisher Summary This chapter focuses on the mathematical approximation of endocrine oscillations in the framework of a modeling process structured in three formal phases. The mathematical methods presented are tailored to quantitatively interpret formal endocrine networks with (delayed) feedbacks. The goal is to illustrate different conditions, under which oscillations can emerge. The formal network itself consists of nodes and conduits, and is based on a qualitative analysis of available experimental data. In the presentation, the nodes are hormone concentrations in abstract pools, in which hormones are released or synthesized, under the control of other hormones. The conduits specify how the nodes interact within the network. The quantitative analysis of the formal network is based on approximation of the rate of change of a single system node. This essentially means that the dynamics of the hormone concentration is described with a single (delayed) ODE. A single half-life elimination model is used and the control of the synthesis is expressed as a combination of sigmoid Hill functions, depending on the related nodes. The derivation of the ODE is demonstrated, along with a brief analysis of the properties of its solution to facilitate the actual determination of all system parameters.

Modeling of oscillations in endocrine networks with feedback.

This study quantifies blood glucose (BG) disturbances occurring before and after episodes of severe hypoglycemia (SH). For 6-8 months, 85 individuals with type 1 diabetes and a history of SH (age, 44+/-10 yr; 41 women and 44 men; duration of diabetes, 26+/-11 yr; hemoglobin A1c, 7.7+/-1.1%) used Lifescan One Touch BG meters for self-monitoring three to five times daily and recorded the date and time of SH episodes in diaries. For each subject, the timing of SH episodes was located in the temporal stream of SMBG readings recorded by the meter, and characteristics, including the Low BG index (LBGI), were computed in 24-h increments. In the 24-h period before the SH episode LBGI rose (P < 0.001), average BG was lower (P = 0.001), and BG variance increased (P = 0.001). In the 24 h after SH, LBGI and BGvariance remained elevated (P < 0.001), but average BG returned to baseline. These disturbances disappeared in 48 h. On the basis of LBGI we identified subjects at low, moderate, and high risk of SH, who reported, on the average, 1.7, 3.4, and 7.4 SH episodes (P < 0.005) during the study. In addition, we designed an algorithm that predicted 50% of all SH episodes that occurred in this subject group. We conclude that episodes of SH are preceded and followed by quantifiable BG disturbances, which could be used to devise warnings of imminent SH.

Leon S. Farhy

Papers

Evaluating the clinical accuracy of two continuous glucose sensors using continuous glucose-error grid analysis.

Risk Analysis of Blood Glucose Data: A Quantitative Approach to Optimizing the Control of Insulin Dependent Diabetes

Sample Asymmetry Analysis of Heart Rate Characteristics with Application to Neonatal Sepsis and Systemic Inflammatory Response Syndrome

Modeling of oscillations in endocrine networks with feedback.

Episodes of severe hypoglycemia in type 1 diabetes are preceded and followed within 48 hours by measurable disturbances in blood glucose.