轻量级模型实现多模态睡眠分期，适合可穿戴设备实时监测。
ULW-SleepNet: An Ultra-Lightweight Network for Multimodal Sleep Stage Scoring
- 采用双流分离卷积与参数共享设计，大幅降低计算开销。
- 在两个数据集上分别达到86.9%和81.4%准确率，仅需13.3K参数。
- 参数减少98.6%，适合部署在物联网和可穿戴设备上。
自动睡眠分期对睡眠障碍的诊断与治疗至关重要。尽管深度学习模型已推动该领域发展，但许多现有模型计算开销大，且仅针对单通道脑电图（EEG），限制了其在多模态多导睡眠图（PSG）数据中的实用性。为此，我们提出ULW-SleepNet，一种超轻量级多模态睡眠分期框架，能高效融合多种生理信号信息。该模型引入新型双流分离卷积（DSSC）模块、深度可分离卷积、通道参数共享及全局平均池化，显著降低计算负担的同时保持优异性能。在Sleep-EDF-20和Sleep-EDF-78数据集上，准确率分别为86.9%和81.4%，仅需13.3K参数和7.89M FLOPs。相比先进方法，参数减少最高达98.6%，性能损失微小，展现出在可穿戴与物联网设备上实现实时睡眠监测的强大潜力。代码已开源。
原文摘要 · Abstract (English)
Automatic sleep stage scoring is crucial for the diagnosis and treatment of sleep disorders. Although deep learning models have advanced the field, many existing models are computationally demanding and designed for single-channel electroencephalography (EEG), limiting their practicality for multimodal polysomnography (PSG) data. To overcome this, we propose ULW-SleepNet, an ultra-lightweight multimodal sleep stage scoring framework that efficiently integrates information from multiple physiological signals. ULW-SleepNet incorporates a novel Dual-Stream Separable Convolution (DSSC) Block, depthwise separable convolutions, channel-wise parameter sharing, and global average pooling to reduce computational overhead while maintaining competitive accuracy. Evaluated on the Sleep-EDF-20 and Sleep-EDF-78 datasets, ULW-SleepNet achieves accuracies of 86.9% and 81.4%, respectively, with only 13.3K parameters and 7.89M FLOPs. Compared to state-of-the-art methods, our model reduces parameters by up to 98.6% with only marginal performance loss, demonstrating its strong potential for real-time sleep monitoring on wearable and IoT devices. The source code for this study is publicly available at https://github.com/wzw999/ULW-SLEEPNET.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。