Sleep disorders and their associated respiratory complications annually affect about a million adults in Switzerland and about 45 million across Europe. Advanced tools for bedside assessment of the respiratory system in the perioperative setting are lacking. The current state-of-the-art tool for the diagnosis of sleep disorders is overnight attended polysomnography. Polysomnography provides sophisticated insights into sleep physiology and mechanisms of sleep disorders, but has drawbacks, including an obtrusive patient setting dependent on a hospital-based sleep laboratory with high operational costs, first-night effect, and reduced reliability due to night-to-night variability.
Our group is investigating a contact-less sleep monitoring system combined with automatic algorithm-based sleep stage assessment to overcome the shortcomings of polysomnography and other obtrusive techniques, to address the limitations of cumbersome manual pre-processing of raw recordings, and to enable long-term follow-up of sleep disorders and their associated respiratory complications.
Our current work aims to use a translational research approach to validate a Doppler radar-based sleep monitoring and assessment system against the gold standard: overnight polysomnography.