  Despite continued advancement in machine learning algorithms and increasing
availability of large data sets, there is still no universally acceptable
solution for automatic sleep staging of human sleep recordings. One reason is
that a skilled neurophysiologist scoring brain recordings of a sleeping person
implicitly adapts his/her staging to the individual characteristics present in
the brain recordings. Trying to incorporate this adaptation step in an
automatic scoring algorithm, we introduce in this paper a method for
personalizing a general sleep scoring model. Starting from a general
convolutional neural network architecture, we allow the model to learn
individual characteristics of the first night of sleep in order to quantify
sleep stages of the second night. While the original neural network allows to
sleep stage on a public database with a state of the art accuracy,
personalizing the model further increases performance (on the order of two
percentage points on average, but more for difficult subjects). This
improvement is particularly present in subjects where the original algorithm
did not perform well (typically subjects with accuracy less than $80\%$).
Looking deeper, we find that optimal classification can be achieved when broad
knowledge of sleep staging in general (at least 20 separate nights) is combined
with subject-specific knowledge. We hypothesize that this method will be very
valuable for improving scoring of lower quality sleep recordings, such as those
from wearable devices.
