Erika Severeyn

A New On-Line Electrocardiographic Records Database and Computer Routines for Data Analysis

Gathering experimental data to test computer methods developed during a research is a hard work. Nowadays, some databases have been stored online that can be freely downloaded, however there is not a wide range of databases yet and not all pathologies are covered. Researchers with low resources are in need of more data they can consult for free. To cope with this we present an on-line portal containing a compilation of ECG databases recorded over the last two decades for research purposes. The first version of this portal contains four databases of ECG records: ischemic cardiopathy (72 patients, 3-lead ECG each), ischemic preconditioning (20 patients, 3-lead ECG each), diabetes (51 patients, 8-lead ECG each) and metabolic syndrome (25 subjects, 12-lead ECG each). In addition, one computer program and three routines are provided in order to correctly read the signals, and two digital filters along with two ECG waves detectors are provided for further processing. This portal will be constantly growing, other ECG databases and signal processing software will be uploaded. With this project, we give the scientific community a resource to avoid hours of data collection and to develop free software.

Adaptation of five indirect insulin sensitivity evaluation methods to three populations: metabolic syndrome, athletic and normal subjects.

Insulin sensitivity is determined using direct or indirect methods. Indirect methods are less invasive than direct methods, but have lower accuracy. The accuracy is set through the Spearmans rank correlation coefficient between the indirect method and a direct method. Since the set of parameters of each indirect method has been set empirically, different values of insulin sensitivity have been reported when they are applied on different populations. In this paper, five indirect methods (Avignon, HOMA-IR, QUICKI, Raynaud, and Matsuda) used to determine insulin sensitivity were adapted to three different populations: athletics, metabolic syndrome and normal subjects. The parameters of each method were varied in a range of values until the optimal value that gives the best correlation coefficient with a gold standard was obtained. Results show that the adaptation procedure led to an improved correlation coefficient. Additionally, the method of Matsuda was the most accurate, followed by the method of Avignon. We have confirmed that each indirect method needs a different set of parameters when it is applied to a specific population in order to obtain an accurate value of insulin sensitivity.

Anthropometric measurements for assessing insulin sensitivity on patients with metabolic syndrome, sedentaries and marathoners.

The diagnosis of low insulin sensitivity is commonly done through the HOMA-IR index, in which fasting insulin and glucose blood levels are evaluated. Insulin and blood glucose levels are used for insulin sensitivity assessment by surrogate methods (HOMA-IR, Matsuda, etc), but anthropometric measurements like body weight, height and waist circumference are not considered, even if these variables also are related to low insulin sensitivity and metabolic syndrome. In this study we evaluate the impact of anthropometric measurements on the HOMA-IR, Matsuda and Caumo indexes to estimate insulin sensitivity. Specifically, we compare insulin sensitivity indexes with and without the anthropometric measurements in their equations on three different groups: patients with metabolic syndrome, sedentaries and marathoners. Results show relationships between anthropometric variables and insulin sensitivity indexes. On the other hand, subjects are mapped differently for insulin sensitivity assessment when anthropometric variables are taken into account. In addition, subjects diagnosed with normal insulin sensitivity could be considered as having low insulin sensitivity when anthropometric variables are considered.

Unsupervised subjects classification using insulin and glucose data for insulin resistance assessment

In this paper, the K-means clustering algorithm is employed to perform an unsupervised classification of subjects based on unidimensional observations (HOMA-IR and the Matsuda indexes separately) and multidimensional observations (insulin and glucose samples obtained from the oral glucose tolerance test). The goal is to explore if the clusters obtained could be used to predict or diagnose insulin resistance or are related to the profiles of the population under study: metabolic syndrome, marathoners and sedentaries. Using two and three clusters, three classification experiments were carried out: i) using the HOMA-IR index as unidimensional observations, ii) using the Matsuda index as unidimensional observations, and iii) using five insulin and five glucose samples as multidimensional observations. The results show that using the HOMA-IR index the clusters are related to insulin resistance but when multidimensional observations are used in the classification process the clusters could be used to predict the insulin resistance or other related diseases.

Data fusion for QRS complex detection in multi-lead electrocardiogram recordings

Heart diseases are the main cause of death worldwide. The first step in the diagnose of these diseases is the analysis of the electrocardiographic (ECG) signal. In turn, the ECG analysis begins with the detection of the QRS complex, which is the one with the most energy in the cardiac cycle. Numerous methods have been proposed in the bibliography for QRS complex detection, but few authors have analyzed the possibility of taking advantage of the information redundancy present in multiple ECG leads (simultaneously acquired) to produce accurate QRS detection. In our previous work we presented such an approach, proposing various data fusion techniques to combine the detections made by an algorithm on multiple ECG leads. In this paper we present further studies that show the advantages of this multi-lead detection approach, analyzing how many leads are necessary in order to observe an improvement in the detection performance. A well known QRS detection algorithm was used to test the fusion techniques on the St. Petersburg Institute of Cardiological Technics database. Results show improvement in the detection performance with as little as three leads, but the reliability of these results becomes interesting only after using seven or more leads. Results were evaluated using the detection error rate (DER). The multi-lead detection approach allows an improvement from DER = 3:04% to DER = 1:88%. Further works are to be made in order to improve the detection performance by implementing further fusion steps.

Random Sub-sampling Cross Validation for Empirical Correlation Between Heart Rate Variability, Biochemical and Anthropometrics Parameters

According to National Cholesterol Education Program-Adult Treatment Panel III, metabolic syndrome (MS) is a condition characterized by: Dyslipidemia, abdominal obesity, high levels in fasting glucose and arterial hypertension. Studies have explored indexes using dimensional analysis (DA) formed by anthropometric, biochemical and heart rate variability parameters for the diagnosis of MS. The dimensionless numbers made from DA have the capability to manage them as a mathematical functionality; therefore it is possible to relate them, even when the parameters used are not connected. The aim of this work is to find a polynomial equation using as variables two dimensionless numbers designed from anthropometrical and biochemical (πIS) parameters and from heart rate variability (πHRV) parameters. A fitting using a parametrical random sub-sampling cross validation (RSV) was performed using as an objective function the least squares method. A database of 40 subjects (25 control subjects and 15 subjects with MS) was employed. The polynomial parameters that best fit the database used correspond to a polynomial of order eight. The RSV substantially improves the adjustment of the polynomial compared to the application of the least squares method only (0.6678 vs. 0.3255). The polynomial relationship between πIS and πHRV allows the possibility to determine biochemical and anthropometric variables from heart rate variability parameters. Due to the limited number of subjects in the database used, it is necessary to repeat this methodology in a more extensive database to determine a more general polynomial that can be used with any type of population.

Cardiac autonomic modulation in response to a glucose stimulus

AbstractThis paper focuses on the effect of a sudden increase of plasma glucose concentration in the cardiac autonomic modulation using time-domain and frequency-domain heart rate variability (HRV) measures. Plasma glucose and insulin levels, measured each 30 min during an oral glucose tolerance test, and RR¯

Comparing glucose and insulin data from the two-hour oral glucose tolerance test in metabolic syndrome subjects and marathon runners

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Classification of metabolic syndrome subjects and marathon runners with the k-means algorithm using heart rate variability features

(mean of the RR interval), SDNN (standard deviation of normal-to-normal heartbeats), rMSSD (root-mean-square of successive differences between normal heartbeats), TP (total spectral power), LF and HF (power of the low- and high-frequency bands), LF norm and HF norm (LF and HF in normalized units), and LF/HF ratio of the HRV signal, obtained from 5-min-long ECG recordings during each phase of the test, were analyzed for subjects with the metabolic syndrome, marathon runners, and a control group. Results show that, after the glucose load, subjects with the metabolic syndrome experienced an increased sympathetic and decreased parasympathetic tone, which suggests an imbalance in cardiac autonomic modulation as a consequence of hyperglycemia and hyperinsulinemia. The significance of this study lies in the use of the ECG to assess the effects of a sudden increase in plasma glucose concentration on the cardiac autonomic modulation in subjects with different cardiovascular and metabolic conditions. Graphical AbstractTime-domain and frequency-domain heart rate variability measures are altered in subjects with different cardiovascular and metabolic conditions during an oral glucose tolerance test

Nonlinear parameters of heart rate variability during oral glucose tolerance test

Glucose is the main energy source of the bodys cells and is essential for normal metabolism. Two pancreatic hormones, insulin and glucagon, are involved in glucose home-ostasis. Alteration in the plasma glucose and insulin concentrations could lead to distinct symptoms and diseases, ranging from mental function impairment to coma and even death. Type 2 diabetes, insulin resistance and metabolic syndrome are typical examples of abnormal glucose metabolism that increase the risk for cardiovascular disease and mortality. The oral glucose tolerance test (OGTT) is a medical test used to screen for prediabetes, type 2 diabetes and insulin resistance. In the 5-sample 2-hour OGTT, plasma glucose and insulin concentrations are measured after a fast and then after oral intake of glucose, at intervals of 30 minutes. In this work, a statistical analysis is carried out to find significant differences between the five stages of the OGTT for plasma glucose and insulin data. In addition, the behavior of the glucose and insulin data is compared between subjects with the metabolic syndrome and marathon runners. Results show that marathon runners have plasma glucose and insulin levels significantly lower (p <; 0.05) than people with the metabolic syndrome in all the stages of the OGTT. Insulin secretion decreases in marathon runners due to a significant reduction in plasma glucose concentration, but insulin secretion does not decrease in metabolic syndrome subjects due to insulin resistance, consequently plasma glucose concentration does not achieve normal levels.