In diesem Wintersemester wird die im WiSe 2022/23 erfolgreich gestartete Vortragsreihe Medical Information Sciences fortgesetzt. Renommierte Wissenschaftlerinnen und Wissenschaftler unterschiedlicher Fachdisziplinen und Forschungsstandorte geben jeden Donnerstag ab 17:30 Uhr Einblicke in aktuelle Fragestellungen und Anwendungsgebiete des breiten Forschungsfeldes Medical Information Sciences.

Sleep disorders represent a critical global health challenge, significantly affecting physical and mental health while imposing considerable economic burdens. Polysomnography (PSG) remains the diagnostic gold standard, but it is highly resource-intensive, leading to long waiting times and widespread underdiagnosis. Innovative technologies focusing on wearable and contactless monitoring have emerged to address these shortcomings, offering noninvasive, cost-effective alternatives for sleep analysis. This presentation investigates advancements in contactless sleep monitoring. This technology leverages techniques like movement analysis to measure cardiorespiratory parameters such as heart rate and respiration. It aims to provide accurate diagnostics without physical contact. The study highlights ongoing research into AI-driven solutions that integrate noninvasive sensors, machine learning algorithms, and user-friendly interfaces to identify sleep stages and detect disorders like apnea and insomnia. These approaches demonstrate promising accuracy in monitoring vital signs and diagnosing conditions, showcasing the potential to bridge the gap between clinical-grade diagnostics and home-based monitoring. Efforts to refine these technologies, such as improving data scalability and reducing development costs, aim to make sleep monitoring more accessible and effective. AI-powered systems also enable automated sleep stage scoring, combining traditional signal processing techniques with advanced machine learning to deliver efficient, objective analyses of physiological data. The findings underscore the transformative potential of contactless sleep monitoring technologies in advancing sleep medicine. By enhancing accessibility and diagnostic efficiency, these innovations promise to improve individuals' health outcomes while addressing the systemic limitations of conventional diagnostic methods.