标题： NeuroSleepNet：一种基于多头自注意力的自动睡眠评分方案，具有空间和多尺度时序表示学习 显示英文标题

标题： NeuroSleepNet: A Multi-Head Self-Attention Based Automatic Sleep Scoring Scheme with Spatial and Multi-Scale Temporal Representation Learning

摘要： 目的：自动睡眠评分对于诊断睡眠障碍至关重要。 基于多导睡眠图的现有框架通常依赖于长序列的输入信号来预测睡眠阶段，这可能引入复杂性。 此外，在睡眠评分方法中简化表示学习的探索有限。 方法：在这项研究中，我们提出了NeuroSleepNet，这是一种自动睡眠评分方法，旨在仅使用当前输入信号中的微事件来分类当前的睡眠阶段，而无需过去的输入。 我们的模型采用监督空间和多尺度时间上下文学习，并结合了Transformer编码器以增强表示学习。 此外，为了在五个睡眠阶段之间实现平衡性能，NeuroSleepNet通过引入基于对数标度的加权技术作为损失函数进行了优化。 结果：NeuroSleepNet实现了与当前最先进的结果相似且可比的性能。 对于Sleep-EDF扩展数据集，最佳准确率、宏F1分数和Cohen's kappa分别为86.1%、80.8%和0.805；对于MESA数据集，分别为82.0%、76.3%和0.753；对于Physio2018数据集，分别为80.5%、76.8%和0.738；对于SHHS数据库，分别为86.7%、80.9%和0.804。 结论：NeuroSleepNet表明，即使专注于计算效率和纯粹的监督学习方法，也可以实现与最先进的方法相当的性能。 意义：我们的研究通过仅关注当前输入信号中的微事件来简化自动睡眠评分，同时保持卓越的性能。 这为睡眠诊断应用提供了一个简化的替代方案。 显示英文摘要

摘要： Objective: Automatic sleep scoring is crucial for diagnosing sleep disorders. Existing frameworks based on Polysomnography often rely on long sequences of input signals to predict sleep stages, which can introduce complexity. Moreover, there is limited exploration of simplifying representation learning in sleep scoring methods. Methods: In this study, we propose NeuroSleepNet, an automatic sleep scoring method designed to classify the current sleep stage using only the microevents in the current input signal, without the need for past inputs. Our model employs supervised spatial and multi-scale temporal context learning and incorporates a transformer encoder to enhance representation learning. Additionally, NeuroSleepNet is optimized for balanced performance across five sleep stages by introducing a logarithmic scale-based weighting technique as a loss function. Results: NeuroSleepNet achieved similar and comparable performance with current state-of-the-art results. The best accuracy, macro-F1 score, and Cohen's kappa were 86.1 percent, 80.8 percent, and 0.805 for Sleep-EDF expanded; 82.0 percent, 76.3 percent, and 0.753 for MESA; 80.5 percent, 76.8 percent, and 0.738 for Physio2018; and 86.7 percent, 80.9 percent, and 0.804 for the SHHS database. Conclusion: NeuroSleepNet demonstrates that even with a focus on computational efficiency and a purely supervised learning approach, it is possible to achieve performance that is comparable to state-of-the-art methods. Significance: Our study simplifies automatic sleep scoring by focusing solely on microevents in the current input signal while maintaining remarkable performance. This offers a streamlined alternative for sleep diagnosis applications.