Authors : Rameshwari Khamkar; Dr. Manisha Bharati

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/6bjn7f5d

Scribd : https://tinyurl.com/2nc9wa4n

DOI : https://doi.org/10.38124/ijisrt/26apr1697

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Sleep stage classification is pivotal in understanding sleep disorders and improving overall sleep quality. Traditional approaches relying on single-modal data and conventional machine learning techniques fail to fully capture intricate patterns across different sleep stages. This paper proposes a novel multi-modal sleep stage classification framework utilizing Multi-Scale Masked Autoencoders (MSMAE) and Convolutional Neural Networks (CNNs) to enhance accuracy and robustness. The system integrates EEG, EOG, and EMG signals to leverage complementary physiological information. A two-stream encoder-decoder architecture processes and fuses these diverse data modalities: the MSMAE encoder captures hierarchical and multi-scale features via masked auto-encoding, while the CNN-based decoder classifies sleep stages including NREM and REM sleep. A Random Forest classifier further provides ensemble-based predictions. The framework is evaluated against standard baselines using accuracy, sensitivity, specificity, and execution time.

Keywords : Sleep Stage Classification; Multi-Modal Learning; MSMAE; Convolutional Neural Network; Polysomnography; EEG; EOG; EMG; Encoder-Decoder Architecture; Random Forest.

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