Towards a cyber physical system for personalised and automatic OSA treatment

Obstructive sleep apnea (OSA) is a breathing disorder that takes place in the course of the sleep and is produced by a complete or a partial obstruction of the upper airway that manifests itself as frequent breathing stops and starts during the sleep. The real-time evaluation of whether or not a patient is undergoing OSA episode is a very important task in medicine in many scenarios, as for example for making instantaneous pressure adjustments that should take place when Automatic Positive Airway Pressure (APAP) devices are used during the treatment of OSA. In this paper the design of a possible Cyber Physical System (CPS) suited to real-time monitoring of OSA is described, and its software architecture and possible hardware sensing components are detailed. It should be emphasized here that this paper does not deal with a full CPS, rather with a software part of it under a set of assumptions on the environment. The paper also reports some preliminary experiments about the cognitive and learning capabilities of the designed CPS involving its use on a publicly available sleep apnea database

Sea Level Rise and Coastal Impacts. Innovation and Improvement of the Local Urban Plan for a Climate-Proof Adaptation Strategy

In recent years, the territorial impacts connected to sea level rise have prompted a reflection on the responsibilities of policy makers in transposing these issues into urban agendas. The need also emerged to both broaden and update the skills of urban planners and to improve territorial governance tools, with the aim of developing feasible regeneration and resilience strategies to face climate change. In this paper, a methodology for the production of Flood Risk Maps is presented, as applied to the Municipality of Ravenna, Italy, by only considering the static component of inundation hazard, i.e., the projected Mean Sea Level Rise, as a first step towards increased preparedness. The resulting Flood Risk Maps represent, in fact, an innovation with respect to the current cognitive framework that supports local urban planning, by providing information on a potential risk that has so far been overlooked. The method combines sea level rise projections under the pessimistic RCP8.5 scenario with georeferenced territorial data, aiming to identify the physical consistency of the urban-structure components which are potentially at risk. For successive time horizons (2030, 2050 and 2100), our results show the progressive impairment and potential degradation of extensive urban areas that are disregarded in the urban planning regulations currently in force. This preliminary evaluation phase is aimed at prompting and supporting the necessary updating of the planning tools and regulations adopted by the public bodies responsible for territorial governance, by identifying priority areas for intervention, and helping define mitigation and adaptation actions

Artificial Intelligence for Mobile Health Data Analysis and Processing

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Blood pressure drop prediction by using HRV measurements in orthostatic hypotension

Orthostatic Hypotension is defined as a reduction of systolic and diastolic blood pressure within 3 minutes of standing, and may cause dizziness and loss of balance. Orthostatic Hypotension has been considered an important risk factor for falls since 1960. This paper presents a model to predict the systolic blood pressure drop due to orthostatic hypotension, relying on heart rate variability measurements extracted from 5 minute ECGs recorded before standing. This model was developed and validated with the leave-one-out cross-validation technique involving 10 healthy subjects, and finally tested with an additional 5 healthy subjects, whose data were not used during the training and cross-validation process. The results show that the model predicts correctly the systolic blood pressure drop in 80 % of all experiments, with an error rate below the measurement error of a sphygmomanometer digital device

Marine energy exploitation in the mediterranean region: steps forward and challenges

This works aims to describe current perspectives for marine energy exploitation in the Mediterranean basin, highlighting challenges and opportunities as well as the factors that still limit its market deployment. Technologies for the conversion of Marine Energy (ME) into electricity are now ready for full-scale deployment in farms of devices, making the final step from demonstration to operability and commercial exploitation. Although marine energy is more abundant along the Atlantic and Nordic European coasts, significant resources are also available in the Mediterranean Sea, opening up new perspectives for sustainable energy production in sensitive coastal areas and for the economic development of Southern Europe. The implementation of ME converters in the Mediterranean is in fact liable to induce significant technological advancements leading to product innovation, due to the local low energy levels which impose more restrictive constraints on device efficiency and environmental compatibility. In addition, the milder climate allows the testing of concepts and prototypes in the natural environment at more affordable costs, lowering capital risks for new and innovative small and medium enterprises. Research institutions and industrial players in Mediterranean countries have already taken up the challenge, despite the numerous limiting factors that still need to be removed. In particular, the ME sector adds up to the many different traditional maritime activities and to the new ocean-related industries that are developing, potentially exacerbating the competition for the use of marine space in the Mediterranean region and threatening its environmental status. The ME sector needs therefore to design suitable instruments to involve all the relevant stakeholders in a participative public debate as to how to best manage the maritime space. As the prospective sea use patterns are rapidly changing, an adequate international legal and policy framework needs to be designed for the coherent management of sea space, and Marine Spatial Planning needs to be finally implemented by EU Member States also in the Mediterranean area. To this end, the creation of transnational clusters of stakeholders is expected be an effective catalyzer, especially as they can foster the exchange of knowledge and best practices both across European countries and between the North and the South shore of the Mediterranean basin

A new database of healthy and pathological voices☆ Ugo Cesari a, Giuseppe De Pietro b, Elio Marciano c, Ciro Niri d, Giovanna Sannino,b, Laura Verde e a Department of Otorhinolaryngology, University Hospital (Policlinico) Federico II of Naples, Via S.Pansini, 5 Naples, Italy b Institute of High Performance Computing and Networking (ICAR-CNR), Via Pietro Castellino, 111, Naples, Italy c Area of Audiology, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Via S.Pansini, 5, Naples, Italy d Independent Doctor Surgeon Specialized in Audiology and Phoniatrics, Naples, Italy e Department of Engineering, University of Naples Parthenope, Centro Direzionale di Napoli, Isola C4, Naples, Italy

In the era of Edge-of-Things computing for the accomplishment of smart healthcare systems, the availability of accurate and reliable databases is important to provide the right tools for researchers and business companies to design, develop and test new techniques, methodologies and/or algorithms to monitor or detect the patient’s healthcare status. In this paper, the study and building of the VOice ICar fEDerico II (VOICED) database are presented, useful for anybody who needs voice signals in her/his research activities. It consists of 208 healthy and pathological voices collected during a clinical study performed following the guidelines of the medical SIFEL (Società Italiana di Foniatria e Logopedia) protocol and the SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) 2013 Statement. For each subject, the database contains a recording of the vowel /a/ of five seconds in length, lifestyle information, the medical diagnosis, and the results of two specific medical questionnaires

voice disorder identification by using machine learning techniques

Nowadays, the use of mobile devices in the healthcare sector is increasing significantly. Mobile technologies offer not only forms of communication for multimedia content (e.g. clinical audio-visual notes and medical records) but also promising solutions for people who desire the detection, monitoring, and treatment of their health conditions anywhere and at any time. Mobile health systems can contribute to make patient care faster, better, and cheaper. Several pathological conditions can benefit from the use of mobile technologies. In this paper we focus on dysphonia, an alteration of the voice quality that affects about one person in three at least once in his/her lifetime. Voice disorders are rapidly spreading, although they are often underestimated. Mobile health systems can be an easy and fast support to voice pathology detection. The identification of an algorithm that discriminates between pathological and healthy voices with more accuracy is necessary to realize a valid and precise mobile health system. The key contribution of this paper is to investigate and compare the performance of several machine learning techniques useful for voice pathology detection. All analyses are performed on a dataset of voices selected from the Saarbruecken voice database. The results obtained are evaluated in terms of accuracy, sensitivity, specificity, and receiver operating characteristic area. They show that the best accuracy in voice diseases detection is achieved by the support vector machine algorithm or the decision tree one, depending on the features evaluated by using opportune feature selection methods

A Control Cycle for the Automatic Assisted Positioning of Auscultation Sensors

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Efficacy Evaluation of Cu- and Ag-Based Antibacterial Treatments on Polypropylene Fabric and Comparison with Commercial Products

Filter masks are disposable devices intended to be worn in order to reduce exposure to potentially harmful foreign agents of 0.1–10.0 microns. However, to perform their function correctly, these devices should be replaced after a few hours of use. Because of this, billions of non-biodegradable face masks are globally discarded every month (3 million/minute). The frequent renewal of masks, together with the strong environmental impact of non-biodegradable plastic-based mask materials, highlights the need to find a solution to this emerging ecological problem. One way to reduce the environmental impact of masks, decrease their turnover, and, at the same time, increase their safety level is to make them able to inhibit pathogen proliferation and vitality by adding antibacterial materials such as silver, copper, zinc, and graphene. Among these, silver and copper are the most widely used. In this study, with the aim of improving commercial devices’ efficacy and eco-sustainability, Ag-based and Cu-based antibacterial treatments were performed and characterized from morphological, compositional, chemical–physical, and microbiological points of view over time and compared with the antibacterial treatments of selected commercial products. The results demonstrated the good distribution of silver and copper particles onto the surface of the masks, along with almost 100% antibacterial capabilities of the coatings against both Gram-positive and Gram-negative bacteria, which were still confirmed even after several washing cycles, thus indicating the good potential of the developed prototypes for mask application