用小波变换分析脑电波，自动识别睡眠阶段更准
EEG Sleep Stage Classification with Continuous Wavelet Transform and Deep Learning
- 用连续小波变换提取脑电信号的时间-频率特征
- 整体准确率88.37%，宏平均F1达73.15%
- 结果优于传统方法，适合临床睡眠诊断应用
准确的睡眠阶段分类对睡眠障碍的诊断与管理至关重要。传统睡眠评分依赖人工标注或从脑电图（EEG）信号中提取时域/频域特征。本研究提出一种基于小波变换的时间-频率分析框架，实现自动化睡眠阶段评分。使用Sleep-EDF Expanded Database（sleep-cassette recordings）进行评估。连续小波变换（CWT）生成时间-频率图，捕捉各睡眠相关频段中的瞬态与振荡模式。实验结果表明，该小波特征结合集成学习，整体准确率达88.37%，宏平均F1得分为73.15%，优于传统机器学习方法，并达到或超过近期深度学习方法性能。研究证实小波分析在稳健、可解释且临床可用的睡眠阶段分类中具有潜力。
原文摘要 · Abstract (English)
Accurate classification of sleep stages is crucial for the diagnosis and management of sleep disorders. Conventional approaches for sleep scoring rely on manual annotation or features extracted from EEG signals in the time or frequency domain. This study proposes a novel framework for automated sleep stage scoring using time-frequency analysis based on the wavelet transform. The Sleep-EDF Expanded Database (sleep-cassette recordings) was used for evaluation. The continuous wavelet transform (CWT) generated time-frequency maps that capture both transient and oscillatory patterns across frequency bands relevant to sleep staging. Experimental results demonstrate that the proposed wavelet-based representation, combined with ensemble learning, achieves an overall accuracy of 88.37 percent and a macro-averaged F1 score of 73.15, outperforming conventional machine learning methods and exhibiting comparable or superior performance to recent deep learning approaches. These findings highlight the potential of wavelet analysis for robust, interpretable, and clinically applicable sleep stage classification.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。