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Automated Sleep stage classification using raw single channel EEG is acritical tool for sleep quality assessment and disorder diagnosis. However,modelling the complexity and variability inherent in this signal is achallenging task, limiting their practicality and effectiveness in clinicalsettings. To mitigate these challenges, this study presents an end-to-end deeplearning (DL) model which integrates squeeze and excitation blocks within theresidual network to extract features and stacked Bi-LSTM to understand complextemporal dependencies. A distinctive aspect of this study is the adaptation ofGradCam for sleep staging, marking the first instance of an explainable DLmodel in this domain with alignment of its decision-making with sleep expert'sinsights. 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 enhancementstrategy was implemented by increasing stride size, leading to 8x fastertraining times with minimal impact on performance. Comparative analysesunderscore our model outperforms all existing baselines, indicating itspotential for clinical usage.

Transparency in Sleep Staging: Deep Learning Method for EEG Sleep Stage Classification with Model Interpretability