Abstract
Over the last years, great emphasis has been placed on the role of arterial stiffness in the development of cardiovascular diseases. This hemodynamic parameter, generally associated to age and blood pressure increase, can be assessed by the measurement of pulse wave velocity (PWV). Currently available devices that measure PWV are expensive and need to be operated by skilled medical staff, reducing the potential of ambulatory setting. This research project aims at developing and testing the sensoring and algorithmic basis of an alternative and non-invasive device for PWV assessment. The proposed device is based on a double-headed sensor probe and allows the assessment of PWV in one single location, providing important information on local arterial hemodynamics. Although studies to validate the clinical use of this system are still required, it has already demonstrated good performance on a dedicated test bench system, capable of reproducing a range of relevant cardiovascular system's properties. © 2011 IEEE.

Abstract
In a clinical context, physicians usually take into account information from more than one data modality when making decisions regarding cancer diagnosis and treatment planning. Artificial intelligence-based methods should mimic the clinical method and take into consideration different sources of data that allow a more comprehensive analysis of the patient and, as a consequence, a more accurate diagnosis. Lung cancer evaluation, in particular, can benefit from this approach since this pathology presents high mortality rates due to its late diagnosis. However, many related works make use of a single data source, namely imaging data. Therefore, this work aims to study the prediction of lung cancer when using more than one data modality. The National Lung Screening Trial dataset that contains data from different sources, specifically, computed tomography (CT) scans and clinical data, was used for the study, the development and comparison of single-modality and multimodality models, that may explore the predictive capability of these two types of data to their full potential. A ResNet18 network was trained to classify 3D CT nodule regions of interest (ROI), whereas a random forest algorithm was used to classify the clinical data, with the former achieving an area under the ROC curve (AUC) of 0.7897 and the latter 0.5241. Regarding the multimodality approaches, three strategies, based on intermediate and late fusion, were implemented to combine the information from the 3D CT nodule ROIs and the clinical data. From those, the best model-a fully connected layer that receives as input a combination of clinical data and deep imaging features, given by a ResNet18 inference model-presented an AUC of 0.8021. Lung cancer is a complex disease, characterized by a multitude of biological and physiological phenomena and influenced by multiple factors. It is thus imperative that the models are capable of responding to that need. The results obtained showed that the combination of different types may have the potential to produce more comprehensive analyses of the disease by the models.

Abstract
The great incidence of cardiovascular (CV) diseases in the world spurs the search for new solutions to enable an early detection of pathological processes and provides more precise diagnosis based in multi-parameters assessment. The pulse wave velocity (PWV) is considered one of the most important clinical parameters for evaluate the CV risk, vascular adaptation, and therapeutic efficacy. Several studies were dedicated to find the relationship between PWV measurement and pathological status in different diseases, and proved the relevance of this parameter. The commercial devices dedicate to PWV estimation make a regional assessment (measured between two vessels), however a local measurement is more precise evaluation of artery condition, taking into account the differences in the structure of arteries. Moreover, the current devices present some limitations due to the contact nature. Emerging trends in CV monitoring are moving away from more invasive technologies to non-invasive and non-contact solutions. The great challenge is to explore the new instrumental solutions that allow the PWV assessment with fewer approximations for an accurately evaluation and relatively inexpensive techniques in order to be used in the clinical routine. © The Author(s) 2015.

Abstract
Blood pressure (BP) determination is a fundamental parameter in cardiovascular assessment. The gold standard method to measure BP is based on the inflatable arm cuff, however has several disadvantages for continuous monitoring. New techniques were developed to overcome these limitations using correlations between the pulse transit time (PTT) and BP. This work draws attention to the PTT rationale using several methods. In order to determine the PTT, an electrocardiogram (ECG) was used combined with multiple photoplethysmography (PPG) sensors applied to different arm locations, these signals were acquired with a bioPLUX device. The Ultrassound system (SonoSite Edge) was used to measure the artery diameter. As reference, BP was measured using a cuff- based sphygmomanometric device. Measurements were performed in a study population of 36 volunteers. The correlation coefficient for DBP determined and DBP measured was r = 0,689. The results suggest PTT deduced from different locations can be used to measure BP.

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