Sleep, a fundamental physiological process, occupies a significant portion of
our lives. Accurate classification of sleep stages serves as a crucial tool for
evaluating sleep quality and identifying probable sleep disorders. This work
introduces a novel methodology that utilises a SE-Resnet-Bi-LSTM architecture
to classify sleep into five separate stages. The classification process is
based on the analysis of single-channel electroencephalograms (EEGs). The
framework that has been suggested consists of two fundamental elements: a
feature extractor that utilises SE-ResNet, and a temporal context encoder that
use stacks of Bi-LSTM units.The effectiveness of our approach is substantiated
by thorough assessments conducted on three different datasets, namely
SLeepEDF-20, SleepEDF-78, and SHHS. Significantly, our methodology attains
notable levels of accuracy, specifically 87.5\%, 83.9\%, and 87.8\%, along with
macro-F1 scores of 82.5, 78.9, and 81.9 for the corresponding datasets.
Notably, we introduce the utilization of 1D-GradCAM visualization to shed light
on the decision-making process of our model in the realm of sleep stage
classification. This visualization method not only provides valuable insights
into the model's classification rationale but also aligns its outcomes with the
annotations made by sleep experts. One notable feature of our research is the
integration of an expedited training approach, which effectively preserves the
model's resilience in terms of performance. The experimental evaluations
conducted provide a comprehensive evaluation of the effectiveness of our
proposed model in comparison to existing approaches, highlighting its potential
for practical applications.

使用 SE-Resnet-Bi-LSTM 架构对睡眠进行五个阶段的分类，方法基于对单通道脑电图（EEG）的分析，利用 1D-GradCAM 可视化方法解释模型决策过程，研究结果表明该模型在睡眠阶段分类上具有较高的准确率和 F1 分数。