ENTICE aimed to use co-creative methodologies in order to build a solid creation pipeline for medical experiential content. The project has developed and evaluated immersive learning resources and tools aiming to support well-defined learning objectives using tangible and intangible resources (AR/VR/MR, 3D printing) that are highly sought in the fields of anatomy and surgery. In this paper the preliminary results from the evaluation of the learning resources and tools in 3 countries as well as the lessons learnt are presented towards to the improvement of the medical education process.
View Article and Find Full Text PDFIntroduction: Obstructive Sleep Apnea (OSA) is a common sleep disorder requiring the time/money consuming polysomnography for diagnosis. Alternative methods for initial evaluation are sought. Our aim was the prediction of Apnea-Hypopnea Index (AHI) in patients potentially suffering from OSA based on nonlinear analysis of respiratory biosignals during sleep, a method that is related to the pathophysiology of the disorder.
View Article and Find Full Text PDFAim: To classify patients with possible diagnosis of Obstructive Sleep Apnea Syndrome (OSAS) into groups according to the severity of the disease using a decision tree producing algorithm based on nonlinear analysis of 3 respiratory signals instead of the use of full polysomnography.
Patients-methods: Eighty-six consecutive patients referred to the Sleep Unit of a Pulmonology Department underwent full polysomnography and their tests were manually scored. Three nonlinear indices (Largest Lyapunov Exponent-LLE, Detrended Fluctuation Analysis-DFA and Approximate Entropy-APEN) were extracted from two respiratory signals (nasal cannula flow-F and thoracic belt-T).