NanoSleep: A Parameter-Efficient Hybrid Temporal Convolutional Network for Single-Channel Sleep Stage Classification

2026-08-19

AI Summary

本文提出NanoSleep，一种紧凑的混合时域卷积网络，通过结合多种技术有效解决单通道睡眠阶段分类问题，并在保持高准确性的同时减小模型大小，适合资源受限设备。

Abstract

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.

Problem

• single-channel EEG
• sleep stage classification
• model size
• resource-constrained devices

Innovation

• Sinc-convolutional front end
• dual-branch feature extractor
• gated dilated temporal convolutional backbone
• channel recalibration
• conditional random field