Unsupervised domain transfer: Overcoming signal degradation in sleep monitoring by increasing scoring realism.

Abstract

Investigate whether hypnogram 'realism' can be used to guide an unsupervised
method for handling arbitrary types of signal degradation in mobile sleep monitoring.
Approach: Combining a pretrained, state-of-the-art 'u-sleep' model with a 'discriminator'
network, we align features from a target domain with a feature space learned during
pretraining. To test the approach, we distort the source domain with realistic signal
degradations, to see how well the method can adapt to different types of degradation. We
compare the performance of the resulting model with best-case models designed in a
supervised manner for each type of transfer.
Main Results: Depending on the type of distortion, we find that the unsupervised
approach can increase Cohen's kappa with as little as 0.03 and up to 0.29, and that for all
transfers, the method does not decrease performance. However, the approach never quite
reaches the estimated theoretical optimal performance, and when tested on a real-life
domain mismatch between two sleep studies, the benefit was insignificant.
Significance: 'Discriminator-guided fine tuning' is an interesting approach to handling
signal degradation for 'in the wild' sleep monitoring, with some promise. In particular, what
it says about sleep data in general is interesting. However, more development will be
necessary before using it 'in production'.