A complex model of mechanically ventilated ARDS lungs is proposed in the paper. This analogue is based on a combination of four components that describe breathing mechanics: morphology, mechanical properties of surfactant, tissue and chest wall characteristics. Physical-mathematical formulas attained from experimental data have been translated into their electrical equivalents and implemented in MultiSim software. To examine the adequacy of the forward model to the properties and behaviour of mechanically ventilated lungs in patients with ARDS symptoms, several computer simulations have been performed and reported in the paper. Inhomogeneous characteristics observed in the physical properties of ARDS lungs were mapped in a multi-lobe model and the measured outputs were compared with the data from physiological reports. In this way clinicians and scientists can obtain the knowledge on the moment of airway zone reopening/closure expressed as a function of pressure, volume or even time. In the paper, these trends were assessed for inhomogeneous distributions (proper for ARDS) of surfactant properties and airway geometry in consecutive lung lobes. The proposed model enables monitoring of temporal alveolar dynamics in successive lobes as well as those occurring at a higher level of lung structure organization, i.e. in a point P0 which can be used for collection of respiratory data during indirect management of recruitment/de-recruitment processes in ARDS lungs. The complex model and synthetic data generated for various parametrization scenarios make possible prospective studies on designing an indirect mode of alveolar zone management, i.e. with

Sleep apnea syndrome is a common sleep disorder. Detection of apnea and differentiation of its type: obstructive (OSA), central (CSA) or mixed is important in the context of treatment methods, however, it typically requires a great deal of technical and human resources. The aim of this research was to propose a quasi-optimal procedure for processing single-channel electroencephalograms (EEG) from overnight recordings, maximizing the accuracy of automatic apnea or hypopnea detection, as well as distinguishing between the OSA and CSA types. The proposed methodology consisted in processing the EEG signals divided into epochs, with the selection of the best methods at the stages of preprocessing, extraction and selection of features, and classification. Normal breathing was unmistakably distinguished from apnea by the k-nearest neighbors (kNN) and an artificial neural network (ANN), and with 99.98% accuracy by the support vector machine (SVM). The average accuracy of multinomial classification was: 82.29%, 83.26%, and 82.25% for the kNN, SVM and ANN, respectively. The sensitivity and precision of OSA and CSA detection ranged from 55 to 66%, and the misclassification cases concerned only the apnea type.