Use RBF as a Sampling Method in Multistart Global Optimization Method

In this paper, a new sampling technique is proposed that can be used in the Multistart global optimization technique as well as techniques based on it. The new method takes a limited number of samples from the objective function and then uses them to train an Radial Basis Function (RBF) neural network. Subsequently, several samples were taken from the artificial neural network this time, and those with the smallest network value in them are used in the global optimization method. The proposed technique was applied to a wide range of objective functions from the relevant literature and the results were extremely promising

Use RBF as a Sampling Method in Multistart Global Optimization Method

In this paper, a new sampling technique is proposed that can be used in the Multistart global optimization technique as well as techniques based on it. The new method takes a limited number of samples from the objective function and then uses them to train an Radial Basis Function (RBF) neural network. Subsequently, several samples were taken from the artificial neural network this time, and those with the smallest network value in them are used in the global optimization method. The proposed technique was applied to a wide range of objective functions from the relevant literature and the results were extremely promising

Prediction of COVID-19 Cases Using Constructed Features by Grammatical Evolution

A widely used method that constructs features with the incorporation of so-called grammatical evolution is proposed here to predict the COVID-19 cases as well as the mortality rate. The method creates new artificial features from the original ones using a genetic algorithm and is guided by BNF grammar. After the artificial features are generated, the original data set is modified based on these features, an artificial neural network is applied to the modified data, and the results are reported. From the comparative experiments done, it is clear that feature construction has an advantage over other machine-learning methods for predicting pandemic elements

NeuralMinimizer: A Novel Method for Global Optimization

The problem of finding the global minimum of multidimensional functions is often applied to a wide range of problems. An innovative method of finding the global minimum of multidimensional functions is presented here. This method first generates an approximation of the objective function using only a few real samples from it. These samples construct the approach using a machine learning model. Next, the required sampling is performed by the approximation function. Furthermore, the approach is improved on each sample by using found local minima as samples for the training set of the machine learning model. In addition, as a termination criterion, the proposed technique uses a widely used criterion from the relevant literature which in fact evaluates it after each execution of the local minimization. The proposed technique was applied to a number of well-known problems from the relevant literature, and the comparative results with respect to modern global minimization techniques are shown to be extremely promising

Hypertension in Dialysis Patients: Diagnostic Approaches and Evaluation of Epidemiology

Whereas hypertension is an established cardiovascular risk factor in the general population, the contribution of increased blood pressure (BP) to the huge burden of cardiovascular morbidity and mortality in patients receiving dialysis continues to be debated. In a large part, this controversy is attributable to particular difficulties in the accurate diagnosis of hypertension. The reverse epidemiology of hypertension in dialysis patients is based on evidence from large cohort studies showing that routine predialysis or postdialysis BP measurements exhibit a U-shaped or J-shaped association with cardiovascular or all-cause mortality. However, substantial evidence supports the notion that home or ambulatory BP measurements are superior to dialysis-unit BP recordings in diagnosing hypertension, in detecting evidence of target-organ damage and in prognosticating the all-cause death risk. In the first part of this article, we explore the accuracy of different methods of BP measurement in diagnosing hypertension among patients on dialysis. In the second part, we describe how the epidemiology of hypertension is modified when the assessment of BP is based on dialysis-unit versus home or ambulatory recordings

Comparative antiarrhythmic efficacy of amiodarone and dronedarone during acute myocardial infarction in rats

The effects of dronedarone, a non-iodinated derivative of amiodarone, on ventricular tachyeardia and ventricular fibrillation post-myocardial infarction are not well established. Fifty-five Wistar rats were randomly allocated to a 2-week oral treatment with either vehicle (n = 18), antiodarone (30 mg/kg, n =20), or dronedarone (30 mg/kg, n = 17). After acute coronary artery ligation, a single-lead electrocardiogram was continuously recorded for 24 h and episodes of ventricular tachycardia/fibrillation as well as mortality rates were analysed. Monophasic action potential recordings were obtained from the left ventricular epicardium at baseline and 24 h post-myocardial infarction. Thyroid hormones and catecholamines were measured using radioimmunoassay. Thyroid function was similar in the 3 groups. Compared to controls, amiodarone and dronedarone equally decreased the number of ventricular tachycardia/fibrillation episodes by approximately 75%. Both agents prevented the increase in monophasic action potential duration and in beat-to-beat variation. Norepinephrine levels were lower only after antiodarone treatment. Despite the observed antiarrhythmic effect, total mortality did not differ between groups (38.8% in controls, 30.0% in the amiodarone group and 58.8% in the dronedarone group), because of excess bradyarrhythmic mortality in both drug groups that reached significance in the dronedarone group. Dronedarone and amiodarone display similar antiarrhythmic efficacy post-myocardial infarction, partly by preventing repolarization inhomogeneity. However, dronedarone increases bradyarrhythmic mortality possibly secondary to its negative inotropic effects. (c) 2007 Elsevier B.V. All rights reserved

Endothelin B-receptors and sympathetic activation: Impact on ventricular arrhythmogenesis during acute myocardial infarction

AbstractAimsWe investigated the role of endothelin-B receptors on sympathetic activation originating from the adrenal gland or from the myocardium and its impact on arrhythmogenesis during acute myocardial infarction.Main methodsWe studied two groups of rats (n=120, 284±2g), namely wild-type and ETB-deficient. Myocardial infarction was induced by permanent ligation of the left coronary artery and ventricular tachyarrhythmias were evaluated from continuous electrocardiographic recordings. Sympathetic activation, measured by indices of heart rate variability, was evaluated after adrenalectomy or catecholamine depletion induced by reserpine. Acute left ventricular failure was assessed by total animal activity.Key findingsAdrenalectomy decreased the total duration of tachyarrhythmias in ETB-deficient rats, but their incidence remained higher, compared to wild-type rats. After reserpine, heart rate variability indices and tachyarrhythmias were similar in the two groups during the initial, ischaemic phase. During evolving infarction, tachyarrhythmia duration was longer in ETB-deficient rats, despite lower sympathetic activation. Heart rate was lower in ETB-deficient rats throughout the 24-hour observation period, whereas activity was comparable in the two groups.SignificanceEndothelin-B receptors modulate sympathetic activation during acute myocardial infarction not only in the ventricular myocardium, but also in the adrenal gland. Sympathetic activation markedly increases early-phase ventricular tachyarrhythmias, but other mechanisms involving the endothelin system underlie delayed arrhythmogenesis

Beat-to-Beat P-Wave Analysis Outperforms Conventional P-Wave Indices in Identifying Patients with a History of Paroxysmal Atrial Fibrillation during Sinus Rhythm

Early identification of patients at risk for paroxysmal atrial fibrillation (PAF) is essential to attain optimal treatment and a favorable prognosis. We compared the performance of a beat-to-beat (B2B) P-wave analysis with that of standard P-wave indices (SPWIs) in identifying patients prone to PAF. To this end, 12-lead ECG and 10 min vectorcardiogram (VCG) recordings were obtained from 33 consecutive, antiarrhythmic therapy naïve patients, with a short history of low burden PAF, and from 56 age- and sex-matched individuals with no AF history. For both groups, SPWIs were calculated, while the VCG recordings were analyzed on a B2B basis, and the P-waves were classified to a primary or secondary morphology. Wavelet transform was used to further analyze P-wave signals of main morphology. Univariate analysis revealed that none of the SPWIs performed acceptably in PAF detection, while five B2B features reached an AUC above 0.7. Moreover, multivariate logistic regression analysis was used to develop two classifiers—one based on B2B analysis derived features and one using only SPWIs. The B2B classifier was found to be superior to SPWIs classifier; B2B AUC: 0.849 (0.754–0.917) vs. SPWIs AUC: 0.721 (0.613–0.813), p value: 0.041. Therefore, in the studied population, the proposed B2B P-wave analysis outperforms SPWIs in detecting patients with PAF while in sinus rhythm. This can be used in further clinical trials regarding the prognosis of such patients