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 correlations spanning hundreds of input epochs. In this
submission, we explore the possibility of achieving further enhancements by
systematically scaling up the model's input size, anticipating potential
improvements in prediction accuracy. In contrast to findings in literature, our
results demonstrate that augmenting the input size does not yield a significant
enhancement in the performance of S4Sleep(TS). These findings, coupled with the
distinctive ability of S4 models to capture long-range dependencies in time
series data, cast doubt on the diagnostic relevance of very long-range
interactions for sleep staging.

通过研究深度学习睡眠分期模型 S4Sleep (TS)，本文探讨了长程相关性对自动睡眠分期的影响，结果发现增加模型输入大小并不能显著提高性能，这对睡眠分期中的非常长程的相关性的诊断意义产生了怀疑。