Data-Efficient Sleep Staging with Synthetic Time Series Pretraining

Algorithms, vol. 18, pp. 580

Abstract

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.

Authors 3

1. Niklas Grieger corresponding Aachen Department of Medical Engineering and Technomathematics Institute for Data-Driven Technologies

   FH Aachen · Utrecht University

   Affiliation as printed

   Department of Information and Computing Sciences, Utrecht University, 3584 CS Utrecht, The Netherlands

   Department of Medical Engineering and Technomathematics, FH Aachen University of Applied Sciences, 52428 Jülich, Germany

   Institute for Data-Driven Technologies, FH Aachen University of Applied Sciences, 52428 Jülich, Germany

2. Siamak Mehrkanoon

   Utrecht University

   Affiliation as printed

   Department of Information and Computing Sciences, Utrecht University, 3584 CS Utrecht, The Netherlands

3. Stephan Bialonski corresponding Aachen Department of Medical Engineering and Technomathematics Institute for Data-Driven Technologies

   FH Aachen

   Affiliation as printed

   Department of Medical Engineering and Technomathematics, FH Aachen University of Applied Sciences, 52428 Jülich, Germany

   Institute for Data-Driven Technologies, FH Aachen University of Applied Sciences, 52428 Jülich, Germany