Abstract
The measurement and analysis of the arterial pulse waveform (APW) are the means for cardiovascular risk assessment. Optical sensors represent an attractive instrumental solution to APW assessment due to their truly non-contact nature that makes the measurement of the skin surface displacement possible, especially at the carotid artery site. In this work, an automatic method to extract and classify the acquired data of APW signals and noise segments was proposed. Two classifiers were implemented: k-nearest neighbours and support vector machine (SVM), and a comparative study was made, considering widely used performance metrics. This work represents a wide study in feature creation for APW. A pool of 37 features was extracted and split in different subsets: amplitude features, time domain statistics, wavelet features, cross-correlation features and frequency domain statistics. The support vector machine recursive feature elimination was implemented for feature selection in order to identify the most relevant feature. The best result (0.952 accuracy) in discrimination between signals and noise was obtained for the SVM classifier with an optimal feature subset .

Abstract
Arterial stiffness, recognized as an independent predictor of cardiovascular events, can be assessed non-invasively by regional and local methods. The present work proposes and describes a novel and low-cost device, based on a double-headed acoustic probe (AP), to assess local arterial stiffness, by means of pulse wave velocity (PWV) measurements. Local PWV is measured over the carotid artery and relies on the determination of the time delay between the signals acquired simultaneously by both acoustic sensors, placed at a fixed distance. The AP was characterized with dedicated test setups, in order to evaluate its performance concerning waveform analysis, repeatability, crosstalk effect and time resolution. Results show that AP signals are repeatable and crosstalk effect do not interfere with its time resolution, when the cross-correlation algorithm for time delay estimation is used. The AP's effectiveness in measuring higher PWV (14 m/s), with a relative error less than 5 %, when using two uncoupled APs, was also demonstrated. Finally, its clinical feasibility was investigated, in a set of 17 healthy subjects, in which local PWV and other hemodynamic parameters were measured. Carotid PWV yielded a mean value of 2.96 +/- 1.08 m/s that is in agreement with the values obtained in other reference studies.

Abstract
The main motivation of this work was to provide a valid contribution for the assessment of the cardiovascular condition by the analysis of several Arterial Pressure Waveform (APW) parameters collected by a new non-invasive device. Three sets of recordings for the carotid pressure waveform at left and right carotid arteries were performed, under standardized conditions, in 20 volunteers by three trained operators. The mean of the inter-operator differences were higher for the right artery, comparatively to the left artery. In this case, an Augmentation Index (AIx) value of -2.31 ± 7.29 % and a Systolic Wave Transit Time (SWTT) value of -12.94 ± 31.46 ms were observed, which are higher than the left measurements, 0.94 ± 7.52 % and -2.96 ± 22.67 ms, respectively. Intra-operator differences were calculated for each of the three sets of measurements and showed good reproducibility. The pulse-by-pulse variability analysis gives very good markers for the Left Ventricular Ejection Time (LVET), Dicrotic Wave Amplitude (DWA), Reflection Wave Amplitude (RWA), Coefficient of Variation (CV) < 10 %, and satisfactory values for the AIx (CV< 30 %). The SWTT and Reflected Wave Transit Time (RWTT) also presented satisfactory results (10 %

Abstract
Presently the interest in non-invasive devices for monitoring the cardiovascular system has increased in importance, especially in the diagnosis of some pathologies. The proposed optical device reveals an attractive instrumental solution for local pulse wave velocity (PWV) assessment and other hemodynamic parameters analysis, such as Augmentation Index (AIx), Subendocardial Viability Ratio (SEVR), Maximum Rate of Pressure Change (dP/dtmax) and Ejection Time Index (ETI). These parameters allow a better knowledge on the cardiovascular condition and management of many disease states. Two studies were performed in order to validate this technology. Firstly, a comparative test between the optical system and a gold-standard in PWV assessment was carried out. Afterwards, a large study was performed in 131 young subjects to establish carotid PWV reference values as well as other hemodynamic parameters and to find correlations between these and the population characteristics. The results allowed the use of this new technique as a reliable method to determine these parameters. For the total of subjects values for carotid PWV vary between 3-7.69 m s-1 a clear correlation with age and smoking status was found out. The Aix varies between -6.15% and 11.46% and exhibit a negative correlation with heart, and dP/dtmax parameter shows a significant decrease with age.

Abstract
EGFR and KRAS are the most frequently mutated genes in lung cancer, being active research topics in targeted therapy. The biopsy is the traditional method to genetically characterise a tumour. However, it is a risky procedure, painful for the patient, and, occasionally, the tumour might be inaccessible. This work aims to study and debate the nature of the relationships between imaging phenotypes and lung cancer-related mutation status. Until now, the literature has failed to point to new research directions, mainly consisting of results-oriented works in a field where there is still not enough available data to train clinically viable models. We intend to open a discussion about critical points and to present new possibilities for future radiogenomics studies. We conducted high-dimensional data visualisation and developed classifiers, which allowed us to analyse the results for EGFR and KRAS biological markers according to different combinations of input features. We show that EGFR mutation status might be correlated to CT scans imaging phenotypes; however, the same does not seem to hold for KRAS mutation status. Also, the experiments suggest that the best way to approach this problem is by combining nodule-related features with features from other lung structures.

Abstract
AbstractAtrial fibrillation (AF) is a cardiac rhythm disorder associated with increased morbidity and mortality. It is the leading risk factor for cardioembolic stroke and its early detection is crucial in both primary and secondary stroke prevention. Continuous monitoring of cardiac rhythm is today possible thanks to consumer-grade wearable devices, enabling transformative diagnostic and patient management tools. Such monitoring is possible using low-cost easy-to-implement optical sensors that today equip the majority of wearables. These sensors record blood volume variations—a technology known as photoplethysmography (PPG)—from which the heart rate and other physiological parameters can be extracted to inform about user activity, fitness, sleep, and health. Recently, new wearable devices were introduced as being capable of AF detection, evidenced by large prospective trials in some cases. Such devices would allow for early screening of AF and initiation of therapy to prevent stroke. This review is a summary of a body of work on AF detection using PPG. A thorough account of the signal processing, machine learning, and deep learning approaches used in these studies is presented, followed by a discussion of their limitations and challenges towards clinical applications.