轻量级模型高效识别脑电睡眠阶段，小样本下表现优异。
Lightweight ML-Based Automatic Sleep Staging Framework with Constrained CNN and Mamba for Small-Sample EEG Datasets
- 用改进的Gabor与可学习滤波器提取特征，结合Mamba架构建模时序
- 仅30.86千参数达87.86%准确率，难分阶段识别显著提升
- 适合便携式设备部署，尤其适用于小样本睡眠数据建模
自动睡眠分期是精准诊断和治疗睡眠障碍及长期家庭睡眠监测的关键技术。便携式脑电图（EEG）设备因数据采集便捷成为研究热点。然而，现有方法仍面临三大挑战：参数量大易在小数据集上过拟合、对N1和REM等难分阶段识别准确率低、最优训练数据集规模不明确、难以部署。本文提出GamSleepNet，一种针对单通道EEG的轻量级低延迟自动睡眠分期框架。该框架包含FEB模块，结合改进Gabor核与可学习滤波器进行特征提取；采用Mamba架构构建时序分类网络；引入新型对比损失与两阶段训练策略；并通过实验验证了单通道EEG睡眠分期模型的最优数据集规模。在Sleepedf数据集上，该模型仅用30.86千参数即达到87.86%的整体准确率，各项指标均达当前最优水平，显著提升困难睡眠阶段的识别精度。
原文摘要 · Abstract (English)
Automatic sleep staging is a key technology for precise diagnosis and treatment of sleep disorders as well as long-term home sleep monitoring. Portable electroencephalogram (EEG) devices have become the focus of research due to their convenience in data collection. However, current methods still face three major challenges: large parameter sizes that easily lead to overfitting on small datasets, low accuracy in classifying difficult stages such as N1 and REM, unclear optimal training dataset size, and difficulty in deployment. This paper proposes GamSleepNet, a lightweight and low-latency automatic sleep staging framework for single-channel EEG. The framework features the FEB module, which combines improved Gabor kernels with learnable filters for feature extraction, uses the Mamba architecture to build a temporal classification network, introduces a novel contrastive loss and a two-stage training strategy, and experimentally validates the optimal dataset size for single-channel EEG sleep staging models. On the Sleepedf dataset, this model achieves an overall accuracy of 87.86 percent with only 30.86 thousand parameters, with all metrics reaching SOTA levels and significantly improving the identification accuracy of challenging sleep stages.
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