Automatic sleep staging is a critical task in healthcare due to the global prevalence of sleep disorders. This study focuses on single-channel electroencephalography (EEG), a practical and widely available signal for automatic sleep staging. Existing approaches face challenges such as class imbalance, limited receptive-field modeling, and insufficient interpretability. This work proposes a context-aware and interpretable framework for single-channel EEG sleep staging, with particular emphasis on improving detection of the N1 stage. Many prior models operate as black boxes with stacked layers, lacking clearly defined and interpretable feature extraction roles.The proposed model combines compact multi-scale feature extraction with temporal modeling to capture both local and long-range dependencies. To address data imbalance, especially in the N1 stage, classweighted loss functions and data augmentation are applied. EEG signals are segmented into sub-epoch chunks, and final predictions are obtained by averaging softmax probabilities across chunks, enhancing contextual representation and robustness.The proposed framework achieves an overall accuracy of 89.72% and a macro-average F1-score of 85.46%. Notably, it attains an F1- score of 61.7% for the challenging N1 stage, demonstrating a substantial improvement over previous methods on the SleepEDF datasets. These results indicate that the proposed approach effectively improves sleep staging performance while maintaining interpretability and suitability for real-world clinical applications.

翻译：自动睡眠分期因睡眠障碍的全球普遍性而成为医疗健康领域的关键任务。本研究聚焦于单通道脑电信号——一种实用且广泛可用的自动睡眠分期信号。现有方法面临类别不平衡、感受野建模受限及可解释性不足等挑战。本文提出一种面向单通道脑电睡眠分期的上下文感知可解释框架，特别着重提升N1睡眠期的检测性能。许多现有模型采用堆叠层级的黑箱运作方式，缺乏明确定义且可解释的特征提取机制。所提模型将紧凑型多尺度特征提取与时序建模相结合，以捕捉局部与长程依赖关系。针对数据不平衡问题（尤其是N1期），采用类别加权损失函数与数据增强技术。脑电信号被分割为子时段数据块，通过对各数据块softmax概率取平均获得最终预测，从而增强上下文表征与鲁棒性。该框架在SleepEDF数据集上实现了89.72%的整体准确率与85.46%的宏平均F1分数。值得注意的是，其在挑战性较高的N1期获得了61.7%的F1分数，较先前方法取得显著提升。结果表明，所提方法在保持可解释性与临床实用性的同时，有效提升了睡眠分期性能。