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cs.LG2026
NanoSleep: A Parameter-Efficient Hybrid Temporal Convolutional Network for Single-Channel Sleep Stage Classification
S M Asif Hossain, Shruti Kshirsagar
Sleep stage classification from single-channel electroencephalography (EEG) is essential for wearable and home-based sleep monitoring. However, many deep learning models achieve hi…
cs.LG2026
STDA-Net: Spectrogram-Based Domain Adaptation for cross-dataset Sleep Stage Classification
Unaza Tallal, Shruti Kshirsagar, Ankita Shukla
Accurate sleep stage classification across datasets remains challenging due to variability in EEG channel montages, sampling rates, recording environments, and subject populations.…
cs.LG2026
Demographic-Aware Transfer Learning for Sleep Stage Classification in Clinical Polysomnography
S M Asif Hossain, Shruti Kshirsagar
Automated sleep stage classification typically employs a single population-agnostic model, disregarding established demographic variations in sleep architecture. Sleep patterns, ho…