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The pathophysiological mechanisms of arterial hypertension during hemodialysis (HD) in patients with end-stage renal disease (ESRD) are still poorly understood. The aim of this study is to investigate physiological, cardiovascular and neuroendocrine changes in patients with ESRD and its correlation with changes in blood pressure (BP) during the HD session. The present study included 21 patients with ESRD undergoing chronic HD treatment. Group A (study) consisted of patients who had BP increase and group B (control) consisted of those who had BP reduction during HD session. Echocardiograms were performed during the HD session to evaluate cardiac output (CO) and systemic vascular resistance (SVR). Before and after the HD session, blood samples were collected to measure brain natriuretic peptide (BNP), catecholamines, endothelin-1 (ET-1), nitric oxide (NO), electrolytes, hematocrit, albumin and nitrogen substances. The mean age of the studied patients was 43±4.9 years, and 54.6% were males. SVR significantly increased in group A (P<0.001). There were no differences in the values of BNP, NO, adrenalin, dopamin and noradrenalin, before and after dialysis, between the two groups. The mean value of ET-1, post HD, was 25.9 pg ml−1 in group A and 13.3 pg ml−1 in group B (P=<0.001). Patients with ESRD showed different hemodynamic patterns during the HD session, with significant BP increase in group A, caused by an increase in SVR possibly due to endothelial dysfunction, evidenced by an increase in serum ET-1 levels.

This paper presents an algorithm that applies metrics derived from automatic QRS detection and segmentation in electrocardiogram signals for analyzing Heart Rate Variability to study the evolution of metrics in the frequency domain of a clinical procedure. The analysis was performed on three sets of elderly people, who are categorized according to frailty phenotype. The first set was comprised of frail elderly, the second pre-frail elderly, and the third robust elderly. Investigators from many disciplines have been encouraged to contribute to the understanding of molecular and physiological changes in multiple systems that may increase the vulnerability of frail elderly. In this work, the frailty phenotype can be characterized by unintentional weight loss, as self-reported, fatigue assessed by self-report, grip strength (measured directly), physical activity level assessed by self-report and gait speed (measured). The results obtained demonstrate the existence of significant differences between Heart Rate Variability metrics for the three groups, especially considering a higher preponderance for sympathetic nervous system for the group of robust patients in response to postural maneuver.

Association Rule Mining by Aprior method has been one of the popular data mining techniques for decades, where knowledge in the form of item-association rules is harvested from a dataset. The quality of item-association rules nevertheless depends on the concentration of frequent items from the input dataset. When the dataset becomes large, the items are scattered far apart. It is known from previous literature that clustering helps produce some data groups which are concentrated with frequent items. Among all the data clusters generated by a clustering algorithm, there must be one or more clusters which contain suitable and frequent items. In turn, the association rules that are mined from such clusters would be assured of better qualities in terms of high confidence than those mined from the whole dataset. However, it is not known in advance which cluster is the suitable one until all the clusters are tried by association rule mining. It is time consuming if they were to be tested by brute-force. In this paper, a statistical property called prior probability is investigated with respect to selecting the best out of many clusters by a clustering algorithm as a pre-processing step before association rule mining. Experiment results indicate that there is correlation between prior probability of the best cluster and the relatively high quality of association rules generated from that cluster. The results are significant as it is possible to know which cluster should be best used for association rule mining instead of testing them all out exhaustively.

The visual analysis of cardiotocographic examinations is a very subjective process. The accurate detection and segmentation of the fetal heart rate (FHR) features and their correlation with the uterine contractions in time allow a better diagnostic and the possibility of anticipation of many problems related to fetal distress. This paper presents a computerized diagnostic aid system based on digital signal processing techniques to detect and segment changes in the FHR and the uterine tone signals automatically. After a pre-processing phase, the FHR baseline detection is calculated. An auxiliary signal called detection line is proposed to support the detection and segmentation processes. Then, the Hilbert transform is used with an adaptive threshold for identifying fiducial points on the fetal and maternal signals. For an antepartum (before labor) database, the positive predictivity value (PPV) is 96.80% for the FHR decelerations, and 96.18% for the FHR accelerations. For an intrapartum (during labor) database, the PPV found was 91.31% for the uterine contractions, 94.01% for the FHR decelerations, and 100% for the FHR accelerations. For the whole set of exams, PPV and SE were both 100% for the identification of FHR DIP II and prolonged decelerations.

It is known that the probability is not a conserved quantity in the stock market, given the fact that it corresponds to an open system. In this paper we analyze the flow of probability in this system by expressing the ideal Black-Scholes equation in the Hamiltonian form. We then analyze how the non-conservation of probability affects the stability of the prices of the Stocks. Finally, we find the conditions under which the probability might be conserved in the market, challenging in this way the non-Hermitian nature of the Black-Scholes Hamiltonian.

It is plausible to assume that the component waves in ECG signals constitute a unique human characteristic because morphology and amplitudes of recorded beats are governed by multiple individual factors. According to the best of our knowledge, the issue of automatically classifying different ’identities’ of QRS morphology has not been explored within the literature. This work proposes five alternative mathematical models for representing different QRS morphologies providing the extraction of a set of features related to QRS shape. The technique incorporates mechanisms of combining the mathematical functions Gaussian, Mexican-Hat and Rayleigh probability density function and also a mechanism for clipping the waveform of those functions. The searching for the optimal parameters which minimize the normalized RMS error between each mathematical model and a given QRS search window enables to find an optimal model. Such modeling behaves as a robust alternative for delineating heartbeats, classifying beat morphologies, detecting subtle and anomalous changes, compression of QRS complex windows among others. The validation process evaluates the ability of each model to represent different QRS morphology classes within 159 full ECG signal records from QT database and 584 QRS search windows from MIT-BIH Arrhythmia database. From the experimental results, we rank the winning rates for which each mathematical model best models and also discriminates the most predominant QRS morphologies Rs, rS, RS, qR, qRs, R, rR’s and QS. Furthermore, the average time errors computed for QRS onset and offset locations when using the corresponding winner mathematical models for delineation purposes were, respectively, 12.87±8.5 ms and 1.47±10.06 ms.

The use of learning analytics (LA) in real-world educational applications is growing very fast as academic institutions realize the positive potential that is possible if LA is integrated in decision making. Education in schools on public health need to evolve in response to the new knowledge and th...

Crowdsensing exploits the sensing abilities offered by smart phones and users' mobility. Users can mutually help each other as a community with the aid of crowdsensing. The potential of crowdsensing has yet to be fully realized for improving public health. A protocol based on gamification to encoura...