Arterial Stiffness Alterations in Simulated Microgravity and Reactive Sledge as a Countermeasure.

INTRODUCTION: Experiments during spaceflight and simulated microgravity as head-down tilt bedrest, demonstrated the role of arterial stiffness among others, in microgravity induced cardiovascular pathologies and emphasized the need for a robust countermeasure. AIM: The purpose of the present study was to evaluate the use of a new countermeasure, consisting of a high intensity Reactive Sledge (RSL) jumps training protocol, to counteract changes in arterial stiffness during long term head down tilt bedrest (LTBR). METHODS: The participants enrolled in the study were 23 male, healthy volunteers, aged between 20 and 45 years, subjected to LTBR for 60 days and randomly assigned either to a control (11) or to a training sledge (12) group using RSL 3-4 times per week, as a countermeasure. Recorded values were systolic and diastolic blood pressure, heart rate and the user's arterial stiffness index. RESULTS: Compared to baseline measurements, there was a deterioration in the values of arterial stiffness, systolic and diastolic blood pressure and heart rate, in both groups until day 35 of LTBR, interpreted as adaptation to the microgravity environment. From this day until the end of the experiment, arterial stiffness of the control group was constantly fluctuating, while constantly improving for the training group. During the recovery period, arterial stiffness values returned to the pre-experimental levels in both groups. CONCLUSIONS: Overall, arterial stiffness increased the longer the time spent in LTBR and the countermeasure was partially effective in preventing the observed phenomenon. German Clinical Trials Register (DRKS), DRKS00012946, September 18, 2017, retrospectively registered.

Varenicline administration for smoking cessation may reduce apnea hypopnea index in sleep apnea patients.

OBJECTIVE/BACKGROUND: Varenicline (VAR) is used for smoking cessation as it inhibits nicotine for binding on its receptors reducing nicotine dependence. VAR administration has been reported to affect sleep. The aim of this study was to evaluate possible changes in polysomnography (PSG) during VAR treatment (SmokeFreeBrain) in healthy smokers and smokers with obstructive sleep apnea (OSA). PATIENTS/METHODS: Thirty smokers (21 men) with 15.3 ± 10.2 PY, aged 32.8 ± 4.5 years, with BMI 28.6 ± 4 kg/m2, 16 without and 14 with OSA (92% males) were studied with PSG (Embletta MPR-Master) before treatment with VAR while smoking and 20-30 days during VAR administration and smoking cessation for at least 5 days. RESULTS: No significant differences were observed in sleep macro architecture (N1, N2, N3, REM, Sleep Efficiency, Total Sleep Time) during VAR treatment apart from prolongation of sleep latency, N2 and N3 latency in both smokers with and without OSA. Apnea hypopnea index (AHI) was reduced in OSA smokers and especially during REM with a borderline increase of arousal index (ArI) and reduction of sleep efficiency (SE). CONCLUSION: VAR treatment worsened sleep quality as a prolongation of sleep latency, N2 and N3 latency was observed. A marginal reduction of AHI was found in OSA patients, more significantly during REM. Due to the small sample size, further studies are needed to distinguish between the adverse reactions of VAR treatment and smoking cessation effects and to evaluate whether VAR may play a role in OSA treatment.

A review on current trends in automatic sleep staging through bio-signal recordings and future challenges.

Sleep staging is a vital process conducted in order to analyze polysomnographic data. To facilitate prompt interpretation of these recordings, many automatic sleep staging methods have been proposed. These methods rely on bio-signal recordings, which include electroencephalography, electrocardiography, electromyography, electrooculography, respiratory, pulse oximetry and others. However, advanced, uncomplicated and swift sleep-staging-evaluation is still needed in order to improve the existing polysomnographic data interpretation. The present review focuses on automatic sleep staging methods through bio-signal recording including current and future challenges.

Automatic Sleep Staging Employing Convolutional Neural Networks and Cortical Connectivity Images.

Understanding of the neuroscientific sleep mechanisms is associated with mental/cognitive and physical well-being and pathological conditions. A prerequisite for further analysis is the identification of the sleep macroarchitecture through manual sleep staging. Several computer-based approaches have been proposed to extract time and/or frequency-domain features with accuracy ranging from 80% to 95% compared with the golden standard of manual staging. However, their acceptability by the medical community is still suboptimal. Recently, utilizing deep learning methodologies increased the research interest in computer-assisted recognition of sleep stages. Aiming to enhance the arsenal of automatic sleep staging, we propose a novel classification framework based on convolutional neural networks. These receive as input synchronizations features derived from cortical interactions within various electroencephalographic rhythms (delta, theta, alpha, and beta) for specific cortical regions which are critical for the sleep deepening. These functional connectivity metrics are then processed as multidimensional images. We also propose to augment the small portion of sleep onset (N1 stage) through the Synthetic Minority Oversampling Technique in order to deal with the great difference in its duration when compared with the remaining sleep stages. Our results (99.85%) indicate the flexibility of deep learning techniques to learn sleep-related neurophysiological patterns.

SmartHypnos: Developing a Toolbox for Polysomnographic Data Visualization and Analysis.

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.

Advanced network neuroscience approaches in sleep neurobiology on extreme environments.

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.

Achieving Accurate Automatic Sleep Staging on Manually Pre-processed EEG Data Through Synchronization Feature Extraction and Graph Metrics.

Sleep staging, the process of assigning labels to epochs of sleep, depending on the stage of sleep they belong, is an arduous, time consuming and error prone process as the initial recordings are quite often polluted by noise from different sources. To properly analyze such data and extract clinical knowledge, noise components must be removed or alleviated. In this paper a pre-processing and subsequent sleep staging pipeline for the sleep analysis of electroencephalographic signals is described. Two novel methods of functional connectivity estimation (Synchronization Likelihood/SL and Relative Wavelet Entropy/RWE) are comparatively investigated for automatic sleep staging through manually pre-processed electroencephalographic recordings. A multi-step process that renders signals suitable for further analysis is initially described. Then, two methods that rely on extracting synchronization features from electroencephalographic recordings to achieve computerized sleep staging are proposed, based on bivariate features which provide a functional overview of the brain network, contrary to most proposed methods that rely on extracting univariate time and frequency features. Annotation of sleep epochs is achieved through the presented feature extraction methods by training classifiers, which are in turn able to accurately classify new epochs. Analysis of data from sleep experiments on a randomized, controlled bed-rest study, which was organized by the European Space Agency and was conducted in the "ENVIHAB" facility of the Institute of Aerospace Medicine at the German Aerospace Center (DLR) in Cologne, Germany attains high accuracy rates, over 90% based on ground truth that resulted from manual sleep staging by two experienced sleep experts. Therefore, it can be concluded that the above feature extraction methods are suitable for semi-automatic sleep staging.