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In this paper we present the first steps in developing SmartHypnos, an easy to use and user friendly graphical user interface, which aims to provide polysomngographic data visualization and the detection and classification of sleep related events. Currently SmartHypnos supports the visualization of EEG, ECG, EOG and EMG signals, and respiratory signals such as nasal pressure, thermistor, oxygen saturation, thoracic and abdominal belt recordings. All these are incorporated into an interface that provides quick and effortless access to the signals mentioned above. The interface displays automatic sleep staging capabilities as well as the detection of apnea events with accuracy rates surpassing 80%. It is expected that SmartHypnos will reduce the time required to analyze sleep data and also reduce possible human errors.

In this paper we propose a novel methodology for investigating pathological sleep patterns through network neuroscience approaches. It consists of initial identification of statistically significant alterations in cortical functional connectivity patterns. The resulting sub-network is then analyzed by employing graph theory for estimating both global performance metrics (integration and specialization) as well as the significance of specific network nodes and their hierarchical organization. So, nodes with important role in network structure are recognized and their functionality is correlated with adenosine biomarker which is important in sleep regulation and promotion. The aforementioned pipeline is applied in a dataset of sleep data gathered during a microgravity simulation experiment. The analysis was performed on cortical resting-state networks involved in sleep physiology. It demonstrated the detrimental effects of microgravity which were more prominent for the group which did not perform reactive sledge jumps as a countermeasure.