  Deep learning models for scoring sleep stages based on single-channel EEG
have been proposed as a promising method for remote sleep monitoring. However,
applying these models to new datasets, particularly from wearable devices,
raises two questions. First, when annotations on a target dataset are
unavailable, which different data characteristics affect the sleep stage
scoring performance the most and by how much? Second, when annotations are
available, which dataset should be used as the source of transfer learning to
optimize performance? In this paper, we propose a novel method for
computationally quantifying the impact of different data characteristics on the
transferability of deep learning models. Quantification is accomplished by
training and evaluating two models with significant architectural differences,
TinySleepNet and U-Time, under various transfer configurations in which the
source and target datasets have different recording channels, recording
environments, and subject conditions. For the first question, the environment
had the highest impact on sleep stage scoring performance, with performance
degrading by over 14% when sleep annotations were unavailable. For the second
question, the most useful transfer sources for TinySleepNet and the U-Time
models were MASS-SS1 and ISRUC-SG1, containing a high percentage of N1 (the
rarest sleep stage) relative to the others. The frontal and central EEGs were
preferred for TinySleepNet. The proposed approach enables full utilization of
existing sleep datasets for training and planning model transfer to maximize
the sleep stage scoring performance on a target problem when sleep annotations
are limited or unavailable, supporting the realization of remote sleep
monitoring.
