Evaluating the Influence of Temporal Context on Automatic Mouse Sleep Staging through the Application of Human Models

TL;DR

This study investigates how extending the temporal context in mouse sleep staging models up to 15 minutes affects classification accuracy, finding that longer context improves performance, especially for REM sleep, and that human models outperform mouse models.

Contribution

The paper demonstrates that increasing temporal context beyond current mouse models enhances sleep staging accuracy, leveraging human sleep models with long-term dependency modeling.

Findings

Increasing context up to 28 s improves classification, especially for REM sleep.

Human model L-SeqSleepNet outperforms mouse models across cohorts.

Longer temporal context has limited benefits beyond 28 s.

Abstract

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.