Patients with chronic obstructive pulmonary disease (COPD) are characterized by increased work of breathing (WOB) and ventilatory muscle dysfunction. Mechanical ventilation is applied to unload the WOB; rest respiratory muscles decrease arterial partial pressure of carbon dioxide (PaCO2) and treat hypoxemia. Since patients' needs are not static, ventilator settings have to be adjusted regularly. The aim of the present study was the development and evaluation of a neuro-fuzzy controller, that utilizes non-invasively acquired parameters for the determination of the appropriate tidal volume (VT) and respiration frequency (RR) ventilator settings for COPD patients. Forty three (43) hours of non-invasively monitored physiology parameters and ventilator settings, from four (4) different COPD patients ventilated in control mode, were collected in two (2) General Hospitals in Greece. Recorded data were randomly allocated into two sets, namely training set (60%) and evaluation set (40%). A neuro-fuzzy controller was developed and trained, by employing the training set. The controller utilizes non-invasively measured parameters, namely oxygen saturation (SpO2), lung compliance (C) and resistance (R), Peak Inspiratory pressure (PIP) and Plateau pressure (Pplateau), for predicting appropriate VT and RR settings. The developed neuro-fuzzy controller was tested against evaluation set. The Mean Square Error of the tidal volume and the respiration rate was 0.222 ml/Kgr and 1.21 breaths per minute (bpm) respectively.

The purpose of this study is the presentation of a system appropriate to be used upon the transition of a patient, from hospital to homecare. The developed system is based upon the creation of a structured subset of data, complying with the ASTM E2369-0 Standard Specification for Continuity of Care Record, concerning the most relevant facts about a patient's healthcare, organized and transportable, in order to be employed during the post-discharge homecare period. The system allows for the extension of the use of DRGs to estimate mean Home-Care cost, taking advantage of the planning and the optimal documentation of the provided homecare.

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This paper presents the reforming of the curriculum of the Department of Medical Instrumentation Technology at the Technological Educational Institution of Athens (TEI-A), as inspired by current trends in higher education. The reforming is taking place in the framework of the "Upgrading of Undergraduate Curricula of TEI-A" project The project-funded upgrading focuses on a core of eight laboratory sectors, with particular emphasis placed on student-centered learning, taking advantage of computer-enhanced educational environment. The existing and proposed curricula are compared. The student workload in the proposed curriculum is reduced, while maintaining an extensive set of basic and applied knowledge related to biomedical engineering. The overall aim is to provide a curriculum that will help in developing multi-skilled individuals that can relate to the demands of this field within a dynamic social and economical environment.

The Internet of Things (IoT) is not new, but it is attracting increasing attention recently. The concept of the Internet of Things entails the use of electronic devices that capture, monitor and transmit data, are connected to a cloud, enabling them to automatically trigger meaningful "events". The modern Hospital is the most complex and representative system created by the human society and in its present stage of development, the Internet of Things is able to lend itself, to serve the radical transformation of Health-care and of its "temple", the 21 st Century Hospital. It is the purpose of this paper, to attempt a first elementary approach, to categorize the numerous and multifarious equipment and materials employed and to sort the services needed and offered, in the diverse Departments of the modern Hospital, under the light of the disrupting influence of the emerging IoT. The most important Hospital Departments and Services (Emergency, Surgery, Radiotherapy, ICU, Medical Imaging, Bio-Signals, IVD-Laboratories, Cell-therapies etc.), are presented in this preliminary "road-map approach", in combination with innovative, already existing and emerging, Health-care supporting IoT applications.