In human sleep staging models, augmenting the temporal context of the input
to the range of tens of minutes has recently demonstrated performance
improvement. In contrast, the temporal context of mouse sleep staging models is
typically in the order of tens of seconds. While long-term time patterns are
less clear in mouse sleep, increasing the temporal context further than that of
the current mouse sleep staging models might still result in a performance
increase, given that the current methods only model very short term patterns.
In this study, we examine the influence of increasing the temporal context in
mouse sleep staging up to 15 minutes in three mouse cohorts using two recent
and high-performing human sleep staging models that account for long-term
dependencies. These are compared to two prominent mouse sleep staging models
that use a local context of 12 s and 20 s, respectively. An increase in context
up to 28 s is observed to have a positive impact on sleep stage classification
performance, especially in REM sleep. However, the impact is limited for longer
context windows. One of the human sleep scoring models, L-SeqSleepNet,
outperforms both mouse models in all cohorts. This suggests that mouse sleep
staging can benefit from more temporal context than currently used.

本研究通过使用两个能够考虑长期依赖关系的高效人类睡眠分级模型，对三组老鼠的睡眠分级进行了长时间段上下文的扩充研究，发现增加睡眠分级模型的时间窗口对睡眠阶段的分类有积极影响，特别是快速动眼期睡眠，但在更长的时间窗口上影响有限，因此认为当前的老鼠睡眠分级可以受益于更多的时间上下文。