Sleep stages play an important role in identifying sleep patterns and diagnosing sleep disorders. In this study, we present an automated sleep stage classifier called the Attentive Dilated Convolutional Neural Network (AttDiCNN), which uses deep learning methodologies to address challenges related to data heterogeneity, computational complexity, and reliable and automatic sleep staging. We employed a force-directed layout based on the visibility graph to capture the most significant information from the EEG signals, thereby representing the spatial-temporal features. The proposed network consists of three modules: the Localized Spatial Feature Extraction Network (LSFE), Spatio-Temporal-Temporal Long Retention Network (S2TLR), and Global Averaging Attention Network (G2A). The LSFE captures spatial information from sleep data, the S2TLR is designed to extract the most pertinent information in long-term contexts, and the G2A reduces computational overhead by aggregating information from the LSFE and S2TLR. We evaluated the performance of our model on three comprehensive and publicly accessible datasets, achieving state-of-the-art accuracies of 98.56%, 99.66%, and 99.08% for the EDFX, HMC, and NCH datasets, respectively, while maintaining a low computational complexity with 1.4 M parameters. Our proposed architecture surpasses existing methodologies in several performance metrics, thereby proving its potential as an automated tool for clinical settings.

翻译：睡眠阶段在识别睡眠模式和诊断睡眠障碍中具有重要作用。本研究提出了一种名为注意力扩张卷积神经网络（AttDiCNN）的自动化睡眠分期分类器，利用深度学习方法应对数据异质性、计算复杂度及可靠自动化睡眠分期等挑战。我们采用基于可见性图的力导向布局来捕捉脑电信号中最显著的信息，从而表征时空特征。该网络由三个模块组成：局部化空间特征提取网络（LSFE）、时空-时间长期保留网络（S2TLR）和全局平均注意力网络（G2A）。LSFE从睡眠数据中捕获空间信息，S2TLR旨在提取长期背景中最相关的信息，而G2A通过聚合LSFE与S2TLR的信息来降低计算开销。我们在三个全面且公开的数据集上评估了模型性能，分别在EDFX、HMC和NCH数据集上实现了98.56%、99.66%和99.08%的先进准确率，同时以1.4M参数维持了低计算复杂度。所提出的架构在多项性能指标上超越了现有方法，证明了其作为临床自动化工具的潜力。