探索睡眠动力学的内在几何学，并通过无监督学习技术预测睡眠阶段
Explore intrinsic geometry of sleep dynamics and predict sleep stage by unsupervised learning techniques
摘要：
我们提出了一种新的无监督的方法，结合现代谐波分析工具的睡眠动力学探索和自动注释。具体而言，我们应用基于扩散的算法，扩散图（DM）和交替扩散（AD）算法，通过重组的频谱信息的脑电图（EEG）提取的非线性型时频分析工具，同步压缩变换（SST），重建睡眠动力学的内在几何结构。利用DM和AD的非线性降维特性实现可视化。此外，重建的睡眠动力学的非线性几何结构，使我们能够实现自动注释的目的。训练隐马尔可夫模型来预测睡眠阶段。预测性能在公开可用的基准数据库Physionet Sleep-EDF [扩展] SC* 和ST* 上进行验证，并采用留一主题交叉验证。Sleep-EDF SC* 的总体准确度和宏F1分别达到82：57%和76%，Sleep-EDF ST* 达到77.01%和71：53%，这与基于监督学习算法的最新结果兼容。结果表明，所提出的算法的临床应用的潜力。
英文摘要：
We propose a novel unsupervised approach for sleep dynamics exploration and automatic annotation by combining modern harmonic analysis tools. Specifically, we apply diffusion-based algorithms, diffusion map (DM) and alternating diffusion (AD) algorithms, to reconstruct the intrinsic geometry of sleep dynamics by reorganizing the spectral information of an electroencephalogram (EEG) extracted from a nonlinear-type time frequency analysis tool, the synchrosqueezing transform (SST). The visualization is achieved by the nonlinear dimension reduction properties of DM and AD. Moreover, the reconstructed nonlinear geometric structure of the sleep dynamics allows us to achieve the automatic annotation purpose. The hidden Markov model is trained to predict the sleep stage. The prediction performance is validated on a publicly available benchmark database, Physionet Sleep-EDF [extended] SC* and ST*, with the leave-one-subject-out cross validation. The overall accuracy and macro F1 achieve 82:57% and 76% in Sleep-EDF SC* and 77.01% and 71:53% in Sleep-EDF ST*, which is compatible with the state-of-the-art results by supervised learning-based algorithms. The results suggest the potential of the proposed algorithm for clinical applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
