To access the session recording, navigate to the content tab and click the view video button. 

Session Schedule

Find a specific presentation in the course by navigating to the timestamp indicated below.

0:00:00
Introduction

0:01:30
Development and verification of a neck-wearable Piezoelectric sensor for detecting snoring and sleep apnea from snoring and carotid pulse signals
Li-Ang Lee (Taiwan)

0:20:54
Analysis of sleep and speech patterns for the diagnosis of impulse control disorders in adolescents
Natividad Martínez Madrid (Germany)

0:41:45
All-night EEG-fNIRS as a novel tool for investigating sleep physiology
Christophe Grova (Canada)

1:06:00
Quantification of REM sleep without atonia in natural sleep environment
Shani Oz (Israel)

Summary

Sleep is essential for maintaining physical and cognitive health, yet disruptions in sleep patterns pose significant public health challenges. These disruptions are linked to increased workplace accidents, cognitive decline, and chronic illnesses such as cardiovascular diseases and dementia. While polysomnography (PSG) is the gold standard for sleep assessment, its reliance on costly, controlled laboratory settings often fails to capture natural sleep behaviors, creating barriers to accessibility. This symposium addresses the pressing need for innovative, cost-effective, and accessible solutions to monitor sleep, diagnose sleep disorders, and assess cognitive health. The event focuses on advancements in sleep research facilitated by wearable technologies, multimodal approaches and artificial intelligence (AI). Novel developments in sensor technology form a cornerstone of these innovations, enabling real-time, non-invasive, and scalable methods for sleep diagnostics.

One highlighted innovation is a neck-wearable piezoelectric sensor capable of detecting snoring vibrations and carotid pulse signals. This device combines statistical analysis and deep learning to distinguish between habitual snoring and severe sleep apnea, providing an automated, real-time diagnosis. By offering portability and practicality, it addresses limitations associated with traditional diagnostic tools and demonstrates the potential for clinical and at-home applications.

Another key area of focus is the integration of electrodermal activity sensors and speech analysis for real-time sleepiness monitoring. By leveraging biomarkers from both the sympathetic nervous system and speech patterns, this system provides critical tools to prevent accidents and identify individuals at risk of cognitive decline. This approach underscores the role of wearable technology in addressing public health concerns and enabling continuous sleep monitoring in everyday environments.

Additionally, sleep and speech patterns are analyzed to explore the influence of sleep disorders on diagnosing impulse control disorders (ICD) in adolescents. This approach, supported by wearable devices, highlights the complex interplay between stress, sleep, and behavioral health. Its findings translate into practical treatments, improving outcomes for vulnerable populations.

Multimodal systems that combine EEG and functional Near-Infrared Spectroscopy (fNIRS) are presented. These systems investigate sleep-state-specific patterns of oxygenated and deoxygenated hemoglobin oscillations, offering more profound insights into sleep physiology. Personalized, whole-night monitoring systems utilizing these technologies pave the way for understanding the spatio-temporal organization of sleep-related hemodynamic processes.

Lastly, advances in algorithms for analyzing REM sleep without atonia (RSWA) and sleep spindles are presented. Validated in laboratory and home settings, these semi-automated algorithms provide an accessible alternative to PSG for diagnosing REM sleep behavior disorder (RBD). By ensuring usability outside of controlled environments, these algorithms demonstrate the potential for widespread application in sleep diagnostics.

This symposium highlights the transformative potential of sensor technologies and AI-driven systems, showcasing scalable, non-invasive solutions to enhance sleep monitoring and diagnostics while addressing critical public health needs.