  Brain waves vary between people. An obvious way to improve automatic sleep
staging for longitudinal sleep monitoring is personalization of algorithms
based on individual characteristics extracted from the first night of data. As
a single night is a very small amount of data to train a sleep staging model,
we propose a Kullback-Leibler (KL) divergence regularized transfer learning
approach to address this problem. We employ the pretrained SeqSleepNet (i.e.
the subject independent model) as a starting point and finetune it with the
single-night personalization data to derive the personalized model. This is
done by adding the KL divergence between the output of the subject independent
model and the output of the personalized model to the loss function during
finetuning. In effect, KL-divergence regularization prevents the personalized
model from overfitting to the single-night data and straying too far away from
the subject independent model. Experimental results on the Sleep-EDF Expanded
database with 75 subjects show that sleep staging personalization with a
single-night data is possible with help of the proposed KL-divergence
regularization. On average, we achieve a personalized sleep staging accuracy of
79.6%, a Cohen's kappa of 0.706, a macro F1-score of 73.0%, a sensitivity of
71.8%, and a specificity of 94.2%. We find both that the approach is robust
against overfitting and that it improves the accuracy by 4.5 percentage points
compared to non-personalization and 2.2 percentage points compared to
personalization without regularization.
