Personalization of automatic sleep scoring: How best to adapt models to
personal domains in wearable EEG
Abstract
Abstractâ\euro” Wearable EEG enables us to capture large amounts of
high-quality sleep data for diagnostic purposes. To make full use of
this capacity we need high-performance automatic sleep scoring models.
To this end, it has been noted that domain mismatch between recording
equipment can be considerable, e.g. PSG to wearable EEG, but a
previously observed benefit from personalizing models to individual
subjects further indicates a personal domain in sleep EEG. In this work,
we have investigated the extent of such a personal domain in wearable
EEG, and review supervised and unsupervised approaches to
personalization as found in the literature.Â
We investigated the personalization effect of the unsupervised
Adversarial Domain Adaptation and implemented an unsupervised method
based on statistics alignment. No beneficial personalization effect was
observed using these unsupervised methods. We find that supervised
personalization leads to a substantial performance improvement on the
target subject ranging from 15% Cohenâ\euro™s Kappa for subjects with
poor performance (κ < 0.70) to roughly 2% on subjects with
high performance (κ > 0.80). This improvement was present
for models trained on both small and large datasets, indicating that
even high-performance models benefit from supervised personalization. We
found that this  personalization can be beneficially regularized using
Kullback-Leibler regularization, leading to lower variance with
negligible cost to improvement.
Based on the experiments, we recommend model personalization using
Kullback-Leibler regularization.