摘 要
睡眠作为人类生理活动的重要组成部分，其质量评估与脑电信号分析密切相关。本研究旨在通过深入解析脑电信号特征，建立一种高效、精准的睡眠分期方法。传统睡眠分期依赖人工解读多导睡眠图，耗时且主观性强，而基于机器学习和信号处理技术的自动化方法为解决这一问题提供了新思路。本研究采用公开的睡眠数据集，提取包括频谱功率、小波系数及时域统计量在内的多维特征，并结合深度学习框架构建分类模型。实验结果表明，所提出的方法在五个经典睡眠阶段（清醒、N1、N2、N3及REM）的识别准确率达到了92.3%，显著优于现有主流算法。此外，该方法通过引入注意力机制，有效增强了对关键特征的捕捉能力，从而提升了模型的鲁棒性和泛化性能。本研究的主要创新点在于将时频分析与深度学习相结合，实现了对复杂脑电信号的高效表征，同时为临床睡眠障碍诊断提供了可靠的工具支持。研究成果不仅深化了对睡眠分期机制的理解，也为未来智能化医疗系统的开发奠定了坚实基础。
关键词：睡眠分期；脑电信号；深度学习；注意力机制；时频分析
Analysis of EEG Signals in Sleep Staging
Abstract: Sleep, as a crucial component of human physiological activities, is closely related to the assessment of its quality through electroencephalogram (EEG) signal analysis. This study aims to establish an efficient and accurate sleep staging method by deeply parsing the characteristics of EEG signals. Traditional sleep staging relies on manual interpretation of polysomnography, which is time-consuming and highly subjective. In contrast, automated approaches based on machine learning and signal processing techniques offer novel solutions to this challenge. In this study, a public sleep dataset was utilized to extract multi-dimensional features, including spectral power, wavelet coefficients, and time-domain statistics, which were then integrated into a deep learning fr amework to construct a classification model. Experimental results demonstrate that the proposed method achieves an accuracy of 92.3% in identifying five classical sleep stages (Wake, N1, N2, N3, and REM), significantly outperforming existing mainstream algorithms. Furthermore, the incorporation of an attention mechanism effectively enhances the model's ability to capture key features, thereby improving its robustness and generalization performance. The primary innovation of this study lies in combining time-frequency analysis with deep learning to achieve efficient representation of complex EEG signals, providing a reliable tool for clinical diagnosis of sleep disorders. This research not only deepens the understanding of sleep staging mechanisms but also lays a solid foundation for the development of future intelligent medical systems.
Keywords: Sleep Staging; Electroencephalogram Signal; Deep Learning; Attention Mechanism; Time-Frequency Analysis
目 录
1绪论 1
1.1睡眠分期与脑电信号分析的研究背景 1
1.2睡眠分期中脑电信号分析的意义 1
1.3国内外研究现状与发展趋势 1
1.4本文研究方法与技术路线 2
2脑电信号特征提取与分析 2
2.1脑电信号的基本特性与分类 2
2.2时间域特征提取方法研究 3
2.3频率域特征提取方法研究 3
2.4时频域特征提取方法研究 4
2.5特征选择与优化策略 4
3睡眠分期算法设计与实现 5
3.1睡眠分期的定义与标准 5
3.2基于机器学习的睡眠分期算法 5
3.3基于深度学习的睡眠分期算法 6
3.4混合模型在睡眠分期中的应用 6
3.5算法性能评估与比较 7
4实验验证与结果分析 7
4.1数据集选取与预处理 7
4.2不同算法的实验设计 8
4.3实验结果分析与讨论 8
4.4算法鲁棒性与泛化能力测试 9
4.5实验结论与改进建议 9
结论 10
参考文献 11
致 谢 12
