An algorithmic approach to identification of gray areas: Analysis of sleep scoring expert ensemble non agreement areas using a multinomial mixture model

An optimized framework for processing multicentric polysomnographic data incorporating expert human oversight

“…However, this study does not go into detail about the source of the uncertainty in sleep staging between sleep technologists. For instance, it is well known that one primary uncertainty source is the transition between the sleep stages N2 and N3 (Bakker et al, 2023 ; Jouan et al, 2023 ).…”

“…The second part concerns the gray areas. Using the predicted hypnodensity from the aSAGA model as input, a trained clustering algorithm tags each epoch that belongs to the gray areas (Jouan et al, 2023 ). The clustering algorithm is a multi-objective method based on multinomial mixture models clustering the different levels of sleep technologist agreement and summarizing the results into two sets of high-agreement and gray area clusters.…”

“…We refer to this dataset as 50 × 10PSG. This dataset comes from a cohort of 50 participants that have previously been scored by ten independent sleep technologists to create a consensus scoring (Jouan et al, 2023;Rusanen et al, 2023). After testing the clustering algorithm on predicted hypnodensities from aSAGA, the threshold separating the gray All three components were hosted on a Linux virtual machine run on a Cisco Hyperflex high-performance compute system located at Reykjavik University.…”

“…One strategy to achieve that goal is to direct the focus of the sleep technologists to the areas of sleep that automatic algorithms have less “certainty” of. By displaying these areas of high uncertainty, referred to as gray areas from now on, we can specifically target the sleep technologists toward these areas, instead of unilaterally trusting or mistrusting the automatic scoring algorithms (Jouan et al, 2023 ).…”