  Automated Sleep stage classification using raw single channel EEG is a
critical tool for sleep quality assessment and disorder diagnosis. However,
modelling the complexity and variability inherent in this signal is a
challenging task, limiting their practicality and effectiveness in clinical
settings. To mitigate these challenges, this study presents an end-to-end deep
learning (DL) model which integrates squeeze and excitation blocks within the
residual network to extract features and stacked Bi-LSTM to understand complex
temporal dependencies. A distinctive aspect of this study is the adaptation of
GradCam for sleep staging, marking the first instance of an explainable DL
model in this domain with alignment of its decision-making with sleep expert's
insights. We evaluated our model on the publically available datasets
(SleepEDF-20, SleepEDF-78, and SHHS), achieving Macro-F1 scores of 82.5, 78.9,
and 81.9, respectively. Additionally, a novel training efficiency enhancement
strategy was implemented by increasing stride size, leading to 8x faster
training times with minimal impact on performance. Comparative analyses
underscore our model outperforms all existing baselines, indicating its
potential for clinical usage.
