Gilberto Perpinan

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.

Extracting Stationary Segments from Non-Stationary Synthetic and Cardiac Signals

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.

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.

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.

Heart rate variability analysis during a dehydration protocol on athletes

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 α1 and α2, 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%.

Wrist and arm body surface bipolar ECG leads signal and sensor study for long-term rhythm monitoring

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.

Nonlinear heart rate variability measures during the oral glucose tolerance test

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: α1 and α2). The findings in this paper imply that parameters: RR, RMSSD, SDRR, LF, HF, α2, 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.