New deep learning model can accurately identify sleep stages

IMAGE: Modern sleep diagnostics is based on wearable, non-intrusive methods. A new deep learning model developed by researchers at the University of Eastern Finland can identify sleep stages as accurately as an experienced physician. This opens up new avenues for the diagnostics and treatment of sleep disorders, including obstructive sleep apnoea. Obstructive sleep apnoea (OSA) is a nocturnal breathing disorder that causes a major burden on public health care systems and national economies. It is estimated that up to one billion people worldwide suffer from obstructive sleep apnoea, and the number is expected to grow due to population ageing and increased prevalence of obesity.