Overview of the hypnodensity approach to scoring sleep for polysomnography and home sleep testing

Abstract

Human experts scoring sleep according to the American Academy of Sleep Medicine (AASM) rules are forced to select, for every 30-second epoch, one out of five stages, even if the characteristics of the neurological signals are ambiguous, a very common occurrence in clinical studies. Moreover, experts cannot score sleep in studies where these signals have not been recorded, such as in home sleep apnea testing (HSAT). In this topic review we describe how artificial intelligence can provide consistent and reliable scoring of sleep stages based on neurological signals recorded in polysomnography (PSG) and on cardiorespiratory signals recorded in HSAT. We also show how estimates of sleep stage probabilities, usually displayed as hypnodensity graph, can be used to quantify sleep stage ambiguity and stability. As an example of the application of hypnodensity in the characterization of sleep disordered breathing (SDB), we compared 49 patients with sleep apnea to healthy controls and revealed a severity-depending increase in ambiguity and decrease in stability during non-rapid eye movement (NREM) sleep. Moreover, using autoscoring of cardiorespiratory signals, we show how HSAT-derived apnea-hypopnea index and hypoxic burden are well correlated with the PSG indices in 80 patients, showing how using this technology can truly enable HSATs as alternatives to PSG to diagnose SDB.

Single-channel EOG sleep staging on a heterogeneous cohort of subjects with sleep disorders

“…One can interpret the hypnodensity graph as the sleep stage uncertainty or ambiguity of the method (van Gorp et al 2022). The hypnodensity concept has gained much attention recently, as it has been shown to match well with the label distribution of a human panel and potentially carries more clinically relevant information than the hypnogram (Stephansen et al 2018, Bakker et al 2022, Anderer et al 2023, Huijben et al 2023.…”