Assessing the importance of long-range correlations for deep-learning-based sleep staging

arXiv:2402.17779v1 Announce Type: cross
Abstract: This study aims to elucidate the significance of long-range correlations for deep-learning-based sleep staging. It is centered around S4Sleep(TS), a recently proposed model for automated sleep staging. This model utilizes electroencephalography (EEG) as raw time series input and relies on structured state space sequence (S4) models as essential model component. Although the model already surpasses state-of-the-art methods for a moderate number of 15 input epochs, recent literature results suggest potential benefits from incorporating very long …

abstract arxiv automated correlations cs.lg eeg eess.sp importance raw series significance sleep space staging state study time series type