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

Artificial Intelligence for Mobile Health Data Analysis and Processing

n/

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

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

Biotechnological synthesis of succinic acid by actinobacillus succinogenes by exploitation of lignocellulosic biomass

Succinic acid is increasingly used in pharmaceutical industries, for the production of additives in food industries, in agriculture and in refinery processes as a precursor of many chemical compounds among which the most important is the succinate salt. It is also used as an ion chelator and surfactant, and for the biochemicals production. Currently, succinic acid is mainly produced through chemical petroleum-based processes, usually from n-butane using maleic anhydride. However, the use of petrochemical feedstocks raises serious environmental problems, due to the higher values of temperature and pressure required. The biotechnological production of succinic acid by microbial conversion of lignocellulosic biomass is attracting growing interest due to the environmental and economic advantages offered. This research is focused on the exploitation of Arundo donax (Giant reed) as a source of lignocellulosic biomass. Arundo donax is a perennial crop particularly suitable for energy production, as it offers high yields per hectare, even in partially fertile or polluted soils, not used for agriculture. Hydrolyzate of Arundo donax will be used as growth media for the Actinobacillus succinogenes 130Z, a bacterium typically found in the bovine rumen, that is recognized as one of the most promising for the biotechnological production of succinic acid, as it is able to produce higher concentrations of succinic acid. The experimental analysis is carried out to optimize the production of succinic acid taking into account the effect of the most critical parameters of the process (microbial biomass, pH, reducing sugars, volatile fatty acids, and succinic acid). Tests have shown that in 48h the sugars are completely biodegraded with a total production of bio-succinic acid of 5.9 g for 9.1 g of reducing sugars, an hourly production 0.12 g h-1 with a yield equal to 65%

Firms’ disclosure compliance with IASB’s Management Commentary framework:an empirical investigation

The continuous demand for enhanced financial reporting has highlighted the decline of the usefulness of traditional financial statements in satisfying the informational needs and requirements of users. Despite there being several points of view, many proposals revolve around the increase of narrative disclosure accompanying financial statements. It is apparent that the regulatory attention regarding narrative reporting has mainly focused on the Management Commentary (MC) report. As a result, the accounting standard setters have promoted different approaches to improve the comparability and usefulness of the MC among the firms. In December 2010, the IASB completed the project on the MC disclosure framework, through the publishing of a non-binding IFRS practice statement (IFRSps). The aim of this study is to analyze the information conveyed by the MC report for a sample of firms listed on the Italian Stock Exchange at the end of 2010. More specifically, we investigate the determinants affecting the extent of firms disclosure compliance with the IASB’s MC voluntary guidelines, reported in the IFRSps, soon after its implementation. To the best of our knowledge, this is the first empirical study which examines the explanatory factors affecting the extent of voluntary disclosure convergence in relation to IFRSps, with reference to Italy. To analyze the informational content of each MC report, we create an index of disclosure compliance using a self-constructed checklist designed on the IASB’s MC guidelines. To assess the relationship between the index of disclosure compliance and the firm characteristics, we use a regression model. Consistent with previous accounting studies, our results suggest that firm size and ownership diffusion are positively related to the extent of disclosure compliance with IASB’s MC guidelines. On the other hand, the leverage and profitability were found to be unrelated to the index of disclosure compliance. The results also show that the level of disclosure compliance to the IASB’s MC guidance is low, ranging from 10% to 76%, averaging 39%. This means that despite the continued demand for better comparability in financial reporting practices, in Italy a large number of firms do not seem to converge towards a single set of standards for both the narrative and numerical-financial disclosure

A Control Cycle for the Automatic Assisted Positioning of Auscultation Sensors

n/

Firms’ disclosure compliance with IASB’s Management Commentary framework:an empirical investigation

The continuous demand for enhanced financial reporting has highlighted the decline of the usefulness of traditional financial statements in satisfying the informational needs and requirements of users. Despite there being several points of view, many proposals revolve around the increase of narrative disclosure accompanying financial statements. It is apparent that the regulatory attention regarding narrative reporting has mainly focused on the Management Commentary (MC) report. As a result, the accounting standard setters have promoted different approaches to improve the comparability and usefulness of the MC among the firms. In December 2010, the IASB completed the project on the MC disclosure framework, through the publishing of a non-binding IFRS practice statement (IFRSps). The aim of this study is to analyze the information conveyed by the MC report for a sample of firms listed on the Italian Stock Exchange at the end of 2010. More specifically, we investigate the determinants affecting the extent of firms disclosure compliance with the IASB’s MC voluntary guidelines, reported in the IFRSps, soon after its implementation. To the best of our knowledge, this is the first empirical study which examines the explanatory factors affecting the extent of voluntary disclosure convergence in relation to IFRSps, with reference to Italy. To analyze the informational content of each MC report, we create an index of disclosure compliance using a self-constructed checklist designed on the IASB’s MC guidelines. To assess the relationship between the index of disclosure compliance and the firm characteristics, we use a regression model. Consistent with previous accounting studies, our results suggest that firm size and ownership diffusion are positively related to the extent of disclosure compliance with IASB’s MC guidelines. On the other hand, the leverage and profitability were found to be unrelated to the index of disclosure compliance. The results also show that the level of disclosure compliance to the IASB’s MC guidance is low, ranging from 10% to 76%, averaging 39%. This means that despite the continued demand for better comparability in financial reporting practices, in Italy a large number of firms do not seem to converge towards a single set of standards for both the narrative and numerical-financial disclosure