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This paper introduces NanoSleep, a compact hybrid temporal convolutional network designed for single-channel EEG sleep stage classification, addressing the challenge of deploying deep learning models on resource-constrained devices. By integrating a learnable Sinc-convolutional front end with a dual-branch feature extractor and a gated dilated temporal convolutional backbone, NanoSleep achieves high accuracy while maintaining a small model size. Evaluation on the Sleep-EDF datasets shows that NanoSleep outperforms six baseline methods, highlighting its potential for practical applications in wearable and home-based sleep monitoring.
NanoSleep achieves superior sleep stage classification accuracy while being small enough for deployment on wearable devices, striking a crucial balance between performance and efficiency.
Sleep stage classification from single-channel electroencephalography (EEG) is essential for wearable and home-based sleep monitoring. However, many deep learning models achieve high accuracy at the cost of large model sizes, which limits their deployment on resource-constrained devices. In this work, we present NanoSleep, a compact hybrid temporal convolutional network for automatic sleep stage classification. NanoSleep combines a learnable Sinc-convolutional front end, a dual-branch feature extractor that fuses multi-scale temporal and spectral representations, a gated dilated temporal convolutional backbone with channel recalibration, and a conditional random field for sequence-level decoding. We further employ a weighted calibrated focal loss to address class imbalance. We evaluate NanoSleep on the Sleep-EDF and Sleep-EDF-Expanded datasets using subject-wise cross-validation. The proposed model consistently outperforms six representative baseline methods, and an ablation study confirms the contribution of each major component. These results demonstrate that NanoSleep provides an effective balance between accuracy and efficiency, making it well suited for wearable devices, home-based sleep monitoring, and resource-constrained clinical applications.
