Browsing by Author "Wong De Balzan, Sara"

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Anthropometric index for insulin sensitivity assessment in older adults from Ecuadorian highlands
(SPIE, 2016-12-05) Encalada Torres, Lorena Esperanza; Wong De Balzan, Sara
A marked increase in the population aged 60 years and over is evident; the proportion of the older adult population will rise 18.6% in 2025. On the other hand, obesity, metabolic syndrome (MS), diabetes and insulin resistance (or low insulin sensitivity-IS) are diseases related to lifestyle, they have become a social and public health problem. IS is the ability of cells to react due to insulin's presence; when this ability is diminished, low insulin sensitivity or insulin resistance (IR) is considered. Studies show that IS decreases with age, though no one knows exactly if it is directly due to aging or changes in muscle mass. IS can be determined using direct or indirect methods. This paper aims to propose an insulin sensitivity method design from anthropometries and lipid measures. The methodology consist in a simple correspondence analysis for determine the variables, and a parametrical optimization using Avignon method as optimal function. The database used is composed by 120 Ecuadorian older adults with and without MS. The results show that the proposed optimized method got a better correlation with Avignon compared to non-optimized method. The proposed method could discriminate between subjects with and without IR and with and without MS. This is an important contribution since other methods like HOMA-IR, which is the most used in clinical practice, cannot find these differences, this means that HOMA-IR is not sensitive for IS estimation in elderly people. Future works will focus in the determination of cutoffs for insulin resistance diagnosis in the proposed method.
Anthropometric measurements for assessing insulin sensitivity on patients with metabolic syndrome, sedentaries and marathoners
(INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS INC., 2015-08-25) Wong De Balzan, Sara
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.
Characterizing artifacts in RR stress test time series
(INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS INC., 2016-08-16) Astudillo Salinas, Darwin Fabián; Medina Molina, Ruben; Palacio Baus, Kenneth Samuel; Solano Quinde, Lizandro Damián; Wong De Balzan, Sara
Electrocardiographic stress test records have a lot of artifacts. In this paper we explore a simple method to characterize the amount of artifacts present in unprocessed RR stress test time series. Four time series classes were defined: Very good lead, Good lead, Low quality lead and Useless lead. 65 ECG, 8 lead, records of stress test series were analyzed. Firstly, RR-time series were annotated by two experts. The automatic methodology is based on dividing the RR-time series in non-overlapping windows. Each window is marked as noisy whenever it exceeds an established standard deviation threshold (SDT). Series are classified according to the percentage of windows that exceeds a given value, based upon the first manual annotation. Different SDT were explored. Results show that SDT close to 20% (as a percentage of the mean) provides the best results. The coincidence between annotators classification is 70.77% whereas, the coincidence between the second annotator and the automatic method providing the best matches is larger than 63%. Leads classified as Very good leads and Good leads could be combined to improve automatic heartbeat labeling.
CinC Challenge 2013: Comparing three algorithms to extract fetal ECG
(SPIE, 2015-11-17) Loja, J; Astudillo Salinas, Darwin Fabián; Medina Molina, Ruben; Palacio Baus, Kenneth Samuel; Velecela, E; Wong De Balzan, Sara
This paper reports a comparison between three fetal ECG (fECG) detectors developed during the CinC 2013 challenge for fECG detection. Algorithm A1 is based on Independent Component Analysis, A2 is based on fECG detection of RS Slope and A3 is based on Expectation-Weighted Estimation of Fiducial Points. The proposed methodology was validated using the annotated database available for the challenge. Each detector was characterized in terms of its performance by using measures of sensitivity, (Se), positive predictive value (P+) and delay time (td). Additionally, the database was contaminated with white noise for two SNR conditions. Decision fusion was tested considering the most common types of combination of detectors. Results show that the decision fusion of A1 and A2 improves fQRS detection, maintaining high Se and P+ even under low SNR conditions without a significant tdincrease.
Classification of metabolic syndrome subjects and marathon runners with the k-means algorithm using heart rate variability features
(INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS INC., 2016-08-30) Wong De Balzan, Sara
In this paper, we have applied the k-means clustering algorithm to classify three study groups (people with metabolic syndrome, marathon runners, and sedentary subjects) that underwent a 5-sample 2-hour oral glucose tolerance test (OGTT). For this purpose, time-domain, frequency-domain and non-linear parameters of the heart rate variability (HRV), extracted from ECG recordings acquired at five different instants of the OGTT, were used as unidimensional observations to the k-means algorithm. Specifically, standard deviation of RR intervals (SDNN), root-mean-square differences of successive RR intervals (RMSSD), frequency power in the low frequency (LF) and high-frequency (HF) bands, LF/HF ratio, Poincaré descriptors SD1 and SD2, fractal scaling exponents ?1 and ?2, and approximate entropy were used as observations. Experiments were carried out with k = 2 and k = 3 clusters and using the squared Euclidean and Cityblock distances. Results showed that the Cityblock distance outperformed the squared Euclidean distance for this kind of observations. In addition, the parameter SDNN at the end of the OGTT gave the best classification performance (69.2%). Parameters SDNN, RMSSD, SD1 and SD2 at fast and at 30 min of the test differentiated subjects with metabolic syndrome with classification a performance greater than 60%.
Comparing glucose and insulin data from the two-hour oral glucose tolerance test in metabolic syndrome subjects and marathon runners
(INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS INC., 2016-08-16) Wong De Balzan, Sara
Glucose is the main energy source of the body's 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.
Evaluation of two QRS detection algorithm on ECG stress test database
(INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS INC., 2016-10-19) Fajardo, J; Astudillo Salinas, Darwin Fabián; Palacio Baus, Kenneth Samuel; Solano Quinde, Lizandro Damián; Wong De Balzan, Sara
In this paper, we evaluated two well-known QRS algorithms: Pan & Tompkins (PT) and based wavelet transform (WT) on an ECG stress test database. In the absence of an annotated ECG stress test database, the first stage of this work consisted of the database annotation, using RR-time series obtained from an eight leads stress database (DICARDIA). First, the system proposes to users a lead (reference channel) according to its statistical measures. Then the user realizes a visual inspection aimed at validating or denying the channel proposed by the system. As the series contains few artifacts, the annotation is performed using interval of annotations. Preliminary results realized over 31928 beats provide a sensibility of 99.81% and 98.28% respectively for PT and WT. The procedure developed in this work can be seen as a valuable starting point in semiautomatic annotation of large electrocardiographic databases, as well to evaluate and to improve stress ECG delineations.
Extracting stationary segments from non-stationary synthetic and cardiac signals
(SPIE, 2014-10-14) Wong De Balzan, Sara
Physiological signals are commonly the result of complex interactions between systems and organs, these interactions lead to signals that exhibit a non-stationary behaviour. For cardiac signals, non-stationary heart rate variability (HRV) may produce misinterpretations. A previous work proposed to divide a non-stationary signal into stationary segments by looking for changes in the signal's properties related to changes in the mean of the signal. In this paper, we extract stationary segments from non-stationary synthetic and cardiac signals. For synthetic signals with different signal-to-noise ratio levels, we detect the beginning and end of the stationary segments and the result is compared to the known values of the occurrence of these events. For cardiac signals, RR interval (cardiac cycle length) time series, obtained from electrocardiographic records during stress tests for two populations (diabetic patients with cardiovascular autonomic neuropathy and control subjects), were divided into stationary segments. Results on synthetic signals reveal that the non-stationary sequence is divided into more stationary segments than needed. Additionally, due to HRV reduction and exercise intolerance reported on diabetic cardiovascular autonomic neuropathy patients, non-stationary RR interval sequences from these subjects can be divided into longer stationary segments compared to the control group.
Heart rate variability analysis during a dehydration protocol on athletes
(INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS INC., 2016-08-30) Wong De Balzan, Sara
Athletes usually start the training with normal body water content, and then they dehydrate during exercise. The water deficit may contribute to increased heart rate and therefore impaired heart rate variability (HRV) postexercise. This paper presents a protocol to study the dehydration from the electrocardiographic signal in athletes, which comprised three phases: i) Rest (RE): before any physical activity, ii) post-exercise (PE): athletes performed a physical activity by pedaling a stationary bike, iii) post-hydration (PH): the subjects drank water ad libitum. In each phase, an electrocardiographic acquisition and weight measure were performed. In RE phase height was measured and in PE phase subjective effort perception of Borg was performed. The protocol was carried out in the morning. The sample consisted of 17 male athletes. The study of HRV in each of the electrocardiographic signals was performed by obtaining time-domain parameters (RR, RMSSD, SDRR), frequency-domain parameters (LF, HF) and non-linear parameters (SD1, SD2, approximate entropy and scaled exponents: ?1 and ?2). The findings in this paper imply that parameters: RR, RMSSD, SDRR, LF, HF, ?2, SD1 and SD2 from HRV, are able to differentiate between phases of hydration and dehydration in the individual athlete, which could be used in the early detection of dehydration using the ECG signal, that is readily available and also noninvasively measure.
Lipid-anthropometric index optimization for insulin sensitivity estimation
(SPIE, 2015-11-17) Encalada Torres, Lorena Esperanza; Wong De Balzan, Sara
Insulin sensitivity (IS) is the ability of cells to react due to insuli?s presence; when this ability is diminished, low insulin sensitivity or insulin resistance (IR) is considered. IR had been related to other metabolic disorders as metabolic syndrome (MS), obesity, dyslipidemia and diabetes. IS can be determined using direct or indirect methods. The indirect methods are less accurate and invasive than direct and they use glucose and insulin values from oral glucose tolerance test (OGTT). The accuracy is established by comparison using spearman rank correlation coefficient between direct and indirect method. This paper aims to propose a lipid-anthropometric index which offers acceptable correlation to insulin sensitivity index for different populations (DB1=MS subjects, DB2=sedentary without MS subjects and DB3=marathoners subjects) without to use OGTT glucose and insulin values. The proposed method is parametrically optimized through a random cross-validation, using the spearman rank correlation as comparator with CAUMO method. CAUMO is an indirect method designed from a simplification of the minimal model intravenous glucose tolerance test direct method (MINMOD-IGTT) and with acceptable correlation (0.89). The results show that the proposed optimized method got a better correlation with CAUMO in all populations compared to non-optimized. On the other hand, it was observed that the optimized method has better correlation with CAUMO in DB2 and DB3 groups than HOMA-IR method, which is the most widely used for diagnosing insulin resistance. The optimized propose method could detect incipient insulin resistance, when classify as insulin resistant subjects that present impaired postprandial insulin and glucose values.
Nonlinear parameters of heart rate variability during oral glucose tolerance test
(INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS INC., 2016-08-30) Wong De Balzan, Sara
Heart rate variability (HRV) is a simple, non-invasive measure that can be used to quantify autonomic nervous system modulation. This method has been used to detect alterations of autonomic cardiovascular regulation in diabetes and metabolic syndrome (MetS). MetS is characterized by the clustering of glucose intolerance, central obesity, dyslipidemia, and hypertension. This study analyze the HRV using nonlinear methods, in three study groups: 15 subjects with MetS, 10 subjects for control group (C), 15 subjects athletes (D), belonging to a data base with electrocardiographic signals during the test oral glucose tolerance (OGTT). In order to characterize the study groups, two analyzes were performed, one statistical to find significant differences, and a simple correspondence analysis. In the obtained results, significant differences were observed (p < 0.05-Wilcoxon) between MetS and C groups at the baseline phase in the index average descriptor de Poincaré (SD2) (76.368 ± 26.511ms versus 102.546 ± 35.706ms) and entropy approximate (ApEn) (1.220 ± 0.089 versus 1.307 ± 0.116). The simple correspondence took into account two components representing 59.19% of the total variance, and these components suggests that the descriptor de Poincare (SD1) and correlation dimension (D2) parameters can discriminate between groups. The results suggest that nonlinear parameters SD1, SD2, ApEn and D2, show that the heart rate dynamics and the regularity of the HRV are affected in subjects with MetS.
Semiautomatic validation of RR time series in an ECG stress test database
(SPIE, 2015-11-17) Armijos, J; Astudillo Salinas, Darwin Fabián; Garciá, D; Medina Molina, Ruben; Palacio Baus, Kenneth Samuel; Wong De Balzan, Sara
This paper reports an automatic method for characterizing the quality of the RR-time series in the stress test database known as DICARDIA. The proposed methodology is simple and consists in subdividing the RR time series in a set of windows for estimating the quantity of artifacts based on a threshold value that depends on the standard deviation of RR-time series for each recorded lead. In a first stage, a manual annotation was performed considering four quality classes for the RR-time series (Reference lead, Good Lead, Low Quality Lead and Useless Lead). Automatic annotation was then performed varying the number of windows and threshold value for the standard deviation of the RR-time series. The metric used for evaluating the quality of the annotation was the Matching Ratio. The best results were obtained using a higher number of windows and considering only three classes (Good Lead, Low Quality Lead and Useless). The proposed methodology allows the utilization of the online available DICARDIA Stress Test database for different types of research.
Unsupervised subjects classification using insulin and glucose data for insulin resistance assessment
(INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS INC., 2015-09-02) Wong De Balzan, Sara
In this paper, the ?-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.