Data-Efficient Sleep Staging with Synthetic Time Series Pretraining
Niklas Grieger Siamak Mehrkanoon Stephan Bialonski
Analyzing electroencephalographic (EEG) time series can be challenging,
especially with deep neural networks, due to the large variability among human
subjects and often small datasets. To address these challenges, various
strategies, such as self-supervised learning, have been suggested, but they
typically rely on extensive empirical datasets. Inspired by recent advances in
computer vision, we propose a pretraining task termed "frequency pretraining"
to pretrain a neural network for sleep staging by predicting the frequency
content of randomly generated synthetic time series. Our experiments
demonstrate that our method surpasses fully supervised learning in scenarios
with limited data and few subjects, and matches its performance in regimes with
many subjects. Furthermore, our results underline the relevance of frequency
information for sleep stage scoring, while also demonstrating that deep neural
networks utilize information beyond frequencies to enhance sleep staging
performance, which is consistent with previous research. We anticipate that our
approach will be advantageous across a broad spectrum of applications where EEG
data is limited or derived from a small number of subjects, including the
domain of brain-computer interfaces.