Argentina Leite

Abstract
In this paper, a numerical study is conducted to investigate the influence of imposed inlet velocity profile on the hemodynamics in the region of the abdominal aorta bifurcation, for a patient specific. The influences of two different inflow velocity profiles on the hemodynamics of the abdominal aorta bifurcation have been investigated. The simulations were carried out under the same conditions changing only the shape of the inlet velocity profile in abdominal aorta. The simulations were performed with a parabolic profile (PP) and a uniform profile (UP) to quantify the hemodynamic differences between them, in the arterial regions, that is, in the upstream bifurcation, in the bifurcation and downstream bifurcation in each of the common iliac arteries. The results reported provide fundamental knowledge to a better understand of the inflow velocity profiles influence in the hemodynamics of the abdominal aorta bifurcation, such as the distribution of the velocity, pressure and wall shear stress (WSS), as well as the distribution of the stress hemodynamic descriptors on the artery wall. The results highlighted that the influence of the inlet velocity profiles in the time-averaged wall shear stress (AWSS) and relative residence time (RRT) is not significant after the abdominal aorta bifurcation. In general, for the hemodynamic descriptors studied, the correlation between the results obtained with the two velocity profiles reaches values close to 1 in the iliac arteries, in contrast to the abdominal region, where the correlation is less than 0.6.

Abstract
Falls, especially in the elderly, are one of the main factors of hospitalization. Time-consuming intervention can be fatal or cause irreversible damages to the victims. On the other hand, there is currently a significant amount of smart clothing equipped with various sensors, particularly gyroscopes and accelerometers, which can be used to detect accidents. The creation of a tool that automatically detects eventual falls allows helping the victims as soon as possible. This works focuses in the automatic fall detection from sensors signals using long short-term memory networks. To train and test this approach, the Sisfall dataset is used, which considers the simulation of 23 adults and 15 older people. These simulations are based on everyday activities and the falls that may result from their execution. The results indicate that the procedure provides an accuracy score of 97.1% on the test set. © 2021, Springer Nature Switzerland AG.

Abstract
The knowledge of hemodynamic behaviour in the abdominal aorta artery bifurcation is of great importance for the early diagnosis of several cardiovascular diseases common in this bifurcation. The work developed focuses on a case study of hemodynamic in the abdominal aorta artery bifurcation, based on a realistic 3D geometric model reconstructed from 2D medical images of a real patient. Hemodynamic quantities based on the wall shear stress (WSS) of the abdominal aorta bifurcation are analysed and is presented an alternative analysis of the well-established stress hemodynamic descriptors to identify specific zones of the artery with a higher probability of developing cardiovascular diseases. The individual analysis of different zones of the artery allowed to obtain information that can remain masked when whole artery is considered as a single zone. The reported results provide a correlation between the analysed stress hemodynamic descriptors and the area of the wall artery. Then, the aim of this work is the identification of regions at the luminal surface subject to atherosusceptible WSS phenotypes. For the patient studied, the analysis presented allowed the identification of the patient's propensity to develop atherosclerosis, according to the hemodynamic descriptors time-averaged WSS (TAWSS), oscillatory shear index (OSI), and relative residence time (RRT). Thus, this work offers a new way of looking to the stress hemodynamic descriptors.

Abstract
This work focus on detection of diseases from Heart Rate Variability (HRV) series using Long Short-Term Memory (LSTM) networks. First, non-linear models are used to extract sequences of features that characterize the HRV series. These time sequences are then used as input for the LSTM. HRV recordings from the Noltisalis database are used for training and testing this approach. The results indicate that the procedure provides accuracy scores in the range of 86.7% to 90.0 % on the test set. © 2020 IEEE.

Abstract
The concept of health indicates physical, mental and social well-being. Psychological stress is commonly present among freshmen due to social and environmental changes. An approach to study the impact of stress on students relied on biological data assessment. In this work, electrocardiogram signals from first year students, from the Biomedical Engineering course, were collected during an oral presentation, acquiring the RR time series. Linear and nonlinear methodologies are used to extract features that best characterize the RR time series in young subjects under stress conditions.