Publications by authors named "Polyxeni T Gkivogkli"
Arterial Stiffness Alterations in Simulated Microgravity and Reactive Sledge as a Countermeasure.
High Blood Press Cardiovasc Prev
January 2022
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.
Article Synopsis
- Sleep staging is essential for analyzing polysomnographic data, which helps in understanding sleep patterns.* -
- Numerous automatic methods have been developed to interpret bio-signal recordings, such as EEG, ECG, and others, to aid in this process.* -
- The review discusses the effectiveness of these automatic methods and highlights the challenges that still need to be addressed for better sleep staging evaluation.*
Advanced network neuroscience approaches in sleep neurobiology on extreme environments.
Annu Int Conf IEEE Eng Med Biol Soc
July 2019
Article Synopsis
- The paper presents a new method to study abnormal sleep patterns using network neuroscience techniques, focusing on changes in brain connectivity.
- It involves analyzing these connectivity patterns with graph theory to identify key brain regions and how they relate to sleep-regulating biomarkers like adenosine.
- The methodology is tested on sleep data from a microgravity simulation, revealing that microgravity adversely affects sleep, especially in participants who did not engage in specific countermeasures like reactive sledge jumps.
SmartHypnos: Developing a Toolbox for Polysomnographic Data Visualization and Analysis.
Annu Int Conf IEEE Eng Med Biol Soc
July 2019
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.
View Article and Find Full Text PDFArticle Synopsis
- Understanding sleep mechanisms is crucial for mental and physical health, and accurate sleep staging is essential for studying these effects.
- Traditional manual staging is compared with newer computer-based techniques, which have improved accuracy but face acceptance issues in the medical community.
- A novel deep learning framework using convolutional neural networks demonstrates high accuracy (99.85%) in classifying sleep stages by analyzing electroencephalographic rhythms and addressing data imbalance in sleep onset stages.
Article Synopsis
- Sleep staging involves labeling different sleep phases, but this process is often complicated due to noise in the data, necessitating effective noise reduction for accurate analysis.
- This paper introduces a detailed pipeline for pre-processing electroencephalographic (EEG) signals and explores two new methods (Synchronization Likelihood and Relative Wavelet Entropy) for automatic sleep staging.
- Using data from a controlled sleep study by the European Space Agency, the proposed methods achieved over 90% accuracy in classifying sleep epochs, indicating their potential for semi-automatic sleep staging.