LightSleepX：一种轻量级、基于Inception的双模态睡眠分期网络
LightSleepX: A Lightweight, Inception-Based Dual-Modal Network for Sleep Staging
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中文总结 AI 辅助
LightSleepX提出轻量级双模态睡眠分期网络，结合Inception架构、深度可分离卷积、多尺度注意力与Mamba编码器，在低资源下实现高精度，参数仅0.049M。
中文摘要 AI 辅助
自动睡眠分期是个人健康监测的基础，然而许多现有方法并不适合实际应用。传统流程通常依赖手工特征或浅层机器学习模型，这些模型难以泛化；而最先进的深度学习方法虽然准确，但计算量大，在资源受限的环境中不实用。本文介绍了LightSleepX，一个轻量级框架，旨在资源受限环境中提供稳健的睡眠分析。LightSleepX结合了Inception风格架构与深度可分离卷积和多尺度增强注意力，用于高效的多模态EEG/EOG特征提取，并使用Mamba编码器进行无规则的长距离时序建模。在公开基准数据集上，LightSleepX在Sleep-EDF-20上达到85.9%的准确率和0.803的宏F1分数，在跨受试者ISRUC-S3数据集上达到81.8%的准确率和0.796的宏F1分数。该框架仅有0.049M参数和195.9 MFLOPs，旨在面向计算成本和隐私为核心约束的实际本地部署。
英文摘要
Automatic sleep staging is fundamental to personal health monitoring, yet many existing approaches are ill-suited for real-world applications. Traditional pipelines often rely on hand-crafted features or shallow machine learning models that struggle to generalize, while state-of-the-art deep learning methods, though accurate, are computationally heavy and impractical for resource-constrained environments. This paper introduces LightSleepX, a lightweight framework designed to deliver robust sleep analysis in resource-constrained environments. LightSleepX combines an Inception-style architecture with depthwise separable convolutions and Multi-scale Enhanced Attention for efficient multi-modal EEG/EOG feature extraction, and a Mamba encoder for rule-free long-range temporal modeling. On public benchmark datasets, LightSleepX achieves 85.9% accuracy and a 0.803 macro-F1 score on Sleep-EDF-20, and 81.8% accuracy and a 0.796 macro-F1 score on the cross-subject ISRUC-S3 dataset. With 0.049M parameters and 195.9 MFLOPs, the framework targets practical local deployment where computational cost and privacy are central constraints.
