Abstract

Abstract
Aims: To determine myocardial stiffness by means of measuring the velocity of naturally occurring myocardial shear waves (SWs) at mitral valve closure (MVC) and investigate their changes with myocardial remodelling in patients with hypertensive heart disease. Methods and results: Thirty-three treated arterial hypertension (HT) patients with hypertrophic left ventricular (LV) remodelling (59 ± 14 years, 55% male) and 26 aged matched healthy controls (55±15 years, 77% male) were included. HT patients were further divided into a concentric remodelling (HT1) group (13 patients) and a concentric hypertrophy (HT2) group (20 patients). LV parasternal long-axis views were acquired with an experimental ultrasound scanner at 1266 ± 317 frames per seconds. The SW velocity induced by MVC was measured from myocardial acceleration maps. SW velocities differed significantly between HT patients and controls (5.83 ± 1.20 m/s vs. 4.04 ± 0.96 m/s; P < 0.001). In addition, the HT2 group had the highest SW velocities (P < 0.001), whereas values between controls and the HT1 group were comparable (P = 0.075). Significant positive correlations were found between SW velocity and LV remodelling (interventricular septum thickness: r = 0.786, P < 0.001; LV mass index: r = 0.761, P < 0.001). SW velocity normalized for wall stress indicated that myocardial stiffness in the HT2 group was twice as high as in controls (P < 0.001), whereas values of the HT1 group overlapped with the controls (P = 1.00). Conclusions: SW velocity as measure of myocardial stiffness is higher in HT patients compared with healthy controls, particularly in advanced hypertensive heart disease. Patients with concentric remodelling have still normal myocardial properties whereas patients with concentric hypertrophy show significant stiffening.

Abstract
This paper addresses the problem of the left inversion of switched linear systems from a diagnostics perspective. The problem of left inversion is to reconstruct the input of a system with the knowledge of its output, whose differentiation is usually required. In the case of thiswork, the objective is to reconstruct the system's unknown inputs, based on the knowledge of its outputs, switching sequence and known inputs. With the inverse model of the switched linear system, a real-time Fault Detection and Isolation (FDI) algorithm with an integrated Fuzzy Logic System (FLS) that is capable of detecting and isolating abrupt faults occurring in the system is developed. In order to attenuate the effects of unknown disturbances and noise at the output of the inverse model, a smoothing strategy is also used. The results are illustrated with an example. The performance of the method is validated experimentally in a DC-DC boost converter, using a low-cost microcontroller, without any additional components.

Abstract

Abstract
Diabetes Mellitus (DM) is one of the most predominant diseases in the world, causing a high number of deaths. Diabetic foot is one of the main complications observed in diabetic patients, which can lead to the development of ulcers. As the risk of ulceration is directly linked to an increase of the temperature in the plantar region, several studies use thermography as a method for automatic identification of problems in diabetic foot. As the distribution of plantar temperature of diabetic patients do not follow a specific pattern, it is difficult to measure temperature changes and, therefore, there is an interest in the development of methods that allow the detection of these abnormal changes. The objective of this work is to develop a methodology that uses thermograms of the feet of diabetic and healthy individuals and analyzes the thermal changes diversity in the plantar region, classifying each foot as belonging to a DM or a healthy individual. Based on the concept of clustering, a binary classifier to predict diabetic foot is presented; both a quantitative indicator and a classification thresholder (evaluated and validated by several performance metrics) are presented. To measure the binary classifier performance, experiments were conducted on a public dataset (with 122 images of DM individuals and 45 of healthy ones), being obtained the following metrics: Sensitivity = 0.73, Fmeasure = 0.81 and AUC = 0.84.

Abstract