  Objective: With the rapid rise of wearable sleep monitoring devices with
non-conventional electrode configurations, there is a need for automated
algorithms that can perform sleep staging on configurations with small amounts
of labeled data. Transfer learning has the ability to adapt neural network
weights from a source modality (e.g. standard electrode configuration) to a new
target modality (e.g. non-conventional electrode configuration). Methods: We
propose feature matching, a new transfer learning strategy as an alternative to
the commonly used finetuning approach. This method consists of training a model
with larger amounts of data from the source modality and few paired samples of
source and target modality. For those paired samples, the model extracts
features of the target modality, matching these to the features from the
corresponding samples of the source modality. Results: We compare feature
matching to finetuning for three different target domains, with two different
neural network architectures, and with varying amounts of training data.
Particularly on small cohorts (i.e. 2 - 5 labeled recordings in the
non-conventional recording setting), feature matching systematically
outperforms finetuning with mean relative differences in accuracy ranging from
0.4% to 4.7% for the different scenarios and datasets. Conclusion: Our findings
suggest that feature matching outperforms finetuning as a transfer learning
approach, especially in very low data regimes. Significance: As such, we
conclude that feature matching is a promising new method for wearable sleep
staging with novel devices.
