Figures & data
Notes: A set of 13 open-access datasets (in blue) was used for the baseline training of the U-Sleep. The middle and right parts of the schema relate to the evaluations on BSDB. Its ID part refers to PSGs each scored by one of more than 50 assistants and 10 senior physicians. The ID training and validation splits (in yellow) were used to fine-tune U-Sleep and, subsequently, to train the confidence network. Baseline evaluation of both algorithmic approaches was performed on the ID-test data (in orange). Their robustness was further evaluated on two OOD test sets (in red), each containing PSGs scored by a unique SP.
Abbreviations: ID, in-domain; OOD, out-of-domain; SP, senior physician; AP, assistant physician; PSG, polysomnography.
Notes: Combined output for a 44-year-old female diagnosed with hypersomnolence. On-subject (Acc, F1w, K) of (79.2, 72.2, 61.5)%, respectively. On-subject average TCP of 0.74. For correctly and incorrectly classified epochs, the average on-subject TCP was 0.87 and 0.41, respectively.
Abbreviation: TCP, true class probability.
Notes: An EEG-EOG channel-pair is used as an input for the U-Sleep classifier. Using the trained U-Sleep, several representations are extracted (softmax; binary code indexing the predicted class; hidden representations - hiddens - from the layer preceding softmax) and used as an input for the confidence network evaluating the True Class Probability (TCP) confidence score. The hypnogram predicted by U-Sleep (y) is provided jointly with the assessment of predictive uncertainty (1-TCP) to guide an efficient review by physician.
Abbreviations: N, number of epochs; TCP, true class probability; y, U-Sleep predicted sleep-stages.