Data-Efficient Sleep Staging with Synthetic Time Series Pretraining

arXiv:2403.08592v1 Announce Type: new
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 …

abstract arxiv challenges cs.lg data datasets eeg human networks neural networks pretraining q-bio.qm self-supervised learning series sleep small staging strategies supervised learning synthetic time series type