Is a Reduced Entropy in Heart Rate Variability an Early Finding of Silent Cardiac Neurovegetative Dysautonomia in Type 2 Diabetes Mellitus? (original) (raw)
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2008
Cardiac autonomic neuropathy (CAN) in diabetes has been called a 'silent killer', because so few patients realize that they suffer from it, and yet its effect can be lethal. Early sub clinical detection of CAN and intervention are of prime importance for risk stratification in preventing sudden death due to silent myocardial infarction. This study presents the usefulness of heart rate variability (HRV) and complexity analyses from short term ECG recordings as a screening tool for CAN. Poincaré plot indexes and sample entropy (SampEn) measure of HRV were used for analyzing variability (short and long term) and complexity respectively. Analyses were performed on the different length of HRV records during supine rest. Reduced Poincaré plot patterns and higher SampEn were found in CAN+ group. Significant changes in HRV parameters of CAN+ group during the course of supine rest were found in contrast to control group (CAN-). Our results demonstrated the potential utility of nonlin...
Renyi entropy in identification of cardiac autonomic neuropathy in diabetes
2012
Heart rate variability (HRV) has been conventionally analyzed with time- and frequency-domain methods. More recent nonlinear analysis has shown an increased sensitivity for identifying risk of future morbidity and mortality in diverse patient groups. Included in the domain of nonlinear analysis are the multiscale entropy measures. The Renyi entropy is such a measure. It is calculated by considering the probability of sequences of values occurring in the HRV data. An exponent α of the probability can be varied to provide a spectrum of measures. In this work we applied the multiscale Renyi entropy for identification of cardiac autonomic neuropathy (CAN) in diabetes patients. Fifteen participants were identified with CAN (dCAN) using the five-test Ewing battery and 26 were control (nCAN). The multiscale Renyi entropy was measured from -5<;α<;+5. The best result was obtained with α=5, where the mean value for patients with CAN was 0.98 with standard deviation of 0.01, compared wit...
Complexity of heart rate variability in type 2 diabetes - effect of hyperglycemia
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2013
Heart rate variability (HRV) is reduced in diabetes mellitus (DM) patients, suggesting dysfunction of cardiac autonomic regulation which has been associated with increased risk for pathological cardiac events. In this paper, we examined changes in HRV complexity in association to blood glucose level (BGL) and duration of diabetes. Resting HRV and BGL measurements of 32 healthy controls and 54 type 2 DM (T2DM) patients were analyzed. HRV complexity was assessed using Shannon entropy, sample entropy (SampEn), multiscale entropy (MSE), and multiscale Renyi entropy. HRV complexity increased with hyperglycemia indicated by increases in Shannon entropy and MSE and decreases in Renyi entropy for negative orders. Diabetes duration was strongly associated with Renyi entropy which increased for positive orders and decreased for negative orders as a function of disease duration. Shannon entropy, SampEn and MSE did not correlate with disease duration.
2008
Diabetes mellitus is a serious and increasing health problem worldwide. An increased risk for all cardiovascular disease compared to non-diabetic patients including dysfunctional neural control of the heart. The clinical manifestations of cardiac autonomic neuropathy (CAN) include heart rate variability. Poor diagnoses of CAN may result in increased incidence of silent myocardial infarction and ischemia, which can lead to sudden death. This study examined the usefulness of HRV analyses of short ECG recordings as a method for detecting CAN utilizing the traditional Ewing battery as a standard for identification of CAN. Several HRV parameters were assessed including time and frequency domain as well as nonlinear parameters. The advantage of the newer nonlinear HRV measures such as approximate entropy (ApEn) is that they are model independent, suitable for nonlinear processes, and measure aspects of HRV different from the traditional methods such as standard deviation or spectral analysis. Eighteen of 38 individuals with diabetes were positive for two or more of the Ewing battery of tests indicating CAN. Approximate Entropy (ApEn), log normalized total power (LnTP) and log normalized high frequency (LnHF) power were different in CAN+ to CAN-individuals (p < 0.05). This indicates that nonlinear scaling parameters are able to identify people with cardiac autonomic neuropathy in short ECG recordings. CSU ID: CSU285503
2007
Diabetes mellitus is a serious and increasing health problem world-wide. An increased risk for all cardiovascular disease compared to non-diabetic patients including dysfunctional neural control of the heart. The clinical manifestations of cardiac autonomic neuropathy (CAN) include heart rate variability. Poor diagnoses of CAN may result in increased incidence of silent myocardial infarction and ischemia, which can lead to sudden death. This study examined the usefulness of HRV analyses of short ECG recordings as a method for detecting CAN utilizing the traditional Ewing battery as a standard for identification of CAN. Several HRV parameters were assessed including time and frequency domain as well as nonlinear parameters. The advantage of the newer nonlinear HRV measures such as approximate entropy (ApEn) is that they are model independent, suitable for nonlinear processes, and measure aspects of HRV different from the traditional methods such as standard deviation or spectral analysis. Eighteen of 38 individuals with diabetes were positive for two or more of the Ewing battery of tests indicating CAN. Approximate Entropy (ApEn), log normalized total power (LnTP) and log normalized high frequency (LnHF) power were different in CAN + to CAN individuals (p < 0.05). This indicates that nonlinear scaling parameters are able to identify people with cardiac autonomic neuropathy in short ECG recordings.
International Journal of Academic Medicine and Pharmacy, 2023
Background: To evaluate association between time domain analysis of heart rate variability with glycated hemoglobin and duration of type II diabetes mellitus. Materials and Methods: Fifty-six adult patients of type II diabetes mellitus of either gender and equal number of healthy controls was selected. As recommended by the task group, HRV values were obtained from a 24hour Holter electrocardiogram. We examined RR intervals, standard deviations of RR intervals (SDNN), the square root of the mean squared difference of succeeding RR intervals (RMSSD), and the proportion of neighbouring NN intervals that differed by more than 50 ms (pNN50) in the time domain. Results: Group I comprised of 36 males and 20 females and group II 29 males and 27 females. The mean HbA1C level in group I was patients was 9.6% and in group II was 5.3%, SBP was 142.6 mm Hg and 118.2 mm Hg, DBP was 76.4 mm Hg and 80.8 mm Hg in group I and II respectively. The mean heart rate was 86.2 beats/min and 74.2 beats/min in group I and II respectively. SDNN was 21.4 ms and 32.5 ms, RMSSD was 18.2 per minute and 22.1 per minute and pNN50 was 1.6% and 3.4% in group I and II respectively. The difference was significant (P< 0.05). There was positive correlation in mean heart rate with both duration of diabetes and HbA1C. Conclusion: The detection and screening of cardiac autonomic neuropathy, which is brought on by persistently elevated blood sugar levels, can be done using heart rate variability.
PLOS ONE, 2017
Microvascular, macrovascular and neurological complications are the key causes of morbidity and mortality among type II diabetes mellitus (T2DM) patients. The aim of this study was to investigate the alterations of cardiac autonomic function of diabetic patients in relation to three types of diabetes-related complications. ECG recordings were collected and analyzed from 169 T2DM patients in supine position who were diagnosed with nephropathy (n = 55), peripheral neuropathy (n = 64) and retinopathy (n = 106) at two hospitals in the UAE. Comparison between combinations of patients with complications and a control diabetic group (CONT) with no complication (n = 34) was performed using time, frequency and multi-lag entropy measures of heart rate variability (HRV). The results show that these measures decreased significantly (p<0.05) depending on the presence and type of diabetic complications. Entropy, (median, 1 st-3 rd interquartile range) for the group combining all complications (1.74,1.37-2.09) was significantly lower than the corresponding values for the CONT group (1.77, 1.39-2.24) with lag-1 for sequential beat-to-beat changes. Odds ratios (OR) from the entropy analysis further demonstrated a significantly higher association with the combination of retinopathy and peripheral neuropathy versus CONT (OR: 1.42 at lag 8) and an even OR for the combination of retinopathy and nephropathy (OR: 2.46 at lag 8) compared to the other groups with complications. Also, the OR of low frequency power to high frequency power ratio (LF/HF) showed a higher association with these diabetic-related complications compared to CONT, especially for the patient group combining all complications (OR: 4.92). This study confirms that the type of microvascular or peripheral neuropathy complication present in T2DM patients have different effects on heart rate entropy, implying disorders of multi-organ connectivity are directly associated with autonomic nervous system dysfunction. Clinical practice may benefit from including multi-lag entropy for cardiac rhythm analysis in conjunction with traditional screening methods in patients with diabetic complications to ensure better preventive and treatment outcomes in the Emirati Arab population.
Influence of type 2 diabetes on symbolic analysis and complexity of heart rate variability in men
Diabetology & Metabolic Syndrome, 2014
Background: Individuals with diabetes may develop cardiac autonomic dysfunction that may be evaluated by heart rate variability (HRV). The aim was evaluated heart rate variability (HRV) of individuals with type 2 diabetes, without cardiovascular autonomic neuropathy (CAN), in response to active postural maneuver by means of nonlinear analysis (symbolic analysis, Shannon and conditional entropy) and correlate HRV parameters between them, glycated hemoglobin and diabetes duration.
Purpose: The present study examined whether HRV is predictive of CAN in patients with DMII. Subjects and methods: The study group consisted of 50 patients, newly diagnosed with type 2 diabetes mellitus. The control group consisted of 50 healthy subjects. HRV was measured using a 24-hour ECG Holter monitoring system. Time domain parameters used are: SDNN, and SDANN. Results: There are significant differences between disease duration. Orthostatic hypotension was found in 14 patients and heart rate increased over 100 at rest was found in 28 patients. HRV parameters are lower in DM group but differences are significant only for SDNN. From the total group, more than half had HRV parameters below the normal range (56%). Of the asymptomatic patients, 14 (28%) had abnormal HRV parameters. Decreased HRV was found in newly diagnosed type two DM. Conclusion: HRV is decreased in newly diagnosed DM II. Heart Rate Variability (HRV) is a Powerful Predictor in the Early Diagnosis of Cardiac Autonomic Neuropathy (CAN) In Patients with Type Two Diabetes Mellitus (DM II). [Abdulhalim Salim Serafi. Heart Rate Variability (HRV) is a Powerful Predictor IN the Early Diagnosis of Cardiac Autonomic Neuropathy (CAN) In Patients with Type Two Diabetes Mellitus (DM II). Life Sci J
Nonlinear analysis of heart rate variability in Type 2 diabetic patients
2016
The article illustrates the results of a scientific research of heart rate variability (HRV) of a group of patients with type2 diabetes. This analysis is based on digital electrocardiograms and characterized as a non-invasive and effective tool to reflect the autonomic nervous system regulation of the heart. Heart rate variability is used to diagnose and estimate the alterations in heart rate by measuring the variation of the time intervals between two consecutive heart beats (RR) intervals. During the study RR time series of both healthy and type 2 diabetic patients are extracted from electrocardiograms through the “Polar Advantage Interface” device. The proposed study employed three methods for nonlinear analysis of the RR time series: Detrended Fluctuation Analysis (α1, p<0.0001; α2, p≈0.06; αall, p<0.05), Poincaré plot (SD1, p<0.05; SD2, p<0.0001; SD1/SD2, p<0.0001) and Rescaled adjusted range Statistics plot (H, p<0.0001). The results from the experiments conc...