Advances in Modeling and Interpretability of Deep Neural Sleep Staging: A Systematic Review

Abstract

Sleep staging has a very important role in diagnosing patients with sleep disorders. In general, this task is very time-consuming for physicians to perform. Deep learning shows great potential to automate this process and remove physician bias from decision making. In this study, we aim to identify recent trends on performance improvement and the causes for these trends. Recent papers on sleep stage classification and interpretability are investigated to explore different modeling and data manipulation techniques, their efficiency, and recent advances. We identify an improvement in performance up to 12% on standard datasets over the last 5 years. The improvements in performance do not appear to be necessarily correlated to the size of the models, but instead seem to be caused by incorporating new architectural components, such as the use of transformers and contrastive learning.

Automatic sleep scoring for real-time monitoring and stimulation in individuals with and without sleep apnea

“…In clinics, sleep doctors could integrate automatic sleep scoring tools to streamline the diagnostic process and facilitate timely interventions. By automating the labor-intensive task of sleep staging, clinicians could allocate more time to patient care and decision-making, ultimately improving the quality and efficiency of healthcare delivery (Gaiduk et al 2023; Soleimani et al 2023). Moreover, the utility of automatic sleep scoring extends beyond the clinic and home-based monitoring may facilitate earlier detection of sleep disorders.…”