NanoSleep：一种用于单通道睡眠分期分类的参数高效混合时间卷积网络

中文摘要 AI 辅助

从单通道脑电图（EEG）进行睡眠分期分类对于可穿戴设备和家庭式睡眠监测至关重要。然而，许多深度学习模型以大模型体积为代价实现高准确率，这限制了它们在资源受限设备上的部署。本研究提出NanoSleep，一种用于自动睡眠分期分类的紧凑混合时间卷积网络。NanoSleep结合了可学习的Sinc卷积前端、融合多尺度时间与频谱表示的双分支特征提取器、带通道重校准的门控膨胀时间卷积骨干网络，以及用于序列级解码的条件随机场。我们还采用加权校准焦点损失来解决类别不平衡问题。我们在Sleep-EDF和Sleep-EDF-Expanded数据集上使用受试者层面交叉验证评估NanoSleep，该模型始终优于六种代表性基线方法， ablation研究证实了各主要组件的贡献。这些结果表明，NanoSleep在准确率与效率间实现了有效平衡，非常适用于可穿戴设备、家庭式睡眠监测及资源受限的临床应用。

英文摘要

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.

发表机构

• School of Computing, Wichita State University（威奇托州立大学计算学院）

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