Analysis and visualization of sleep stages based on deep neural networks.
Analysis and visualization of sleep stages based on deep neural networks.
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DOI：
10.1016/j.nbscr.2021.100064
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发表时间：
2021-05
影响因子：
--
通讯作者：
Schilling A
中科院分区：
文献类型：
--
作者：
Krauss P;Metzner C;Joshi N;Schulze H;Traxdorf M;Maier A;Schilling A
基于深度神经网络的自动睡眠阶段评分已经成为睡眠研究人员和医生的关注焦点，因为能够客观分类睡眠阶段的可靠方法将节省人力资源并简化临床常规。由于机器学习的新型开源软件库，再加上硬件开发的巨大进步，睡眠研究领域向自动诊断的范式转变可能迫在眉睫。我们认为，现代机器学习技术不仅是一种自动进行睡眠阶段分类的工具，也是一种发现睡眠生理学隐藏特性的创造性方法。我们已经开发和建立了算法来可视化和聚类EEG数据，促进了对睡眠呼吸暂停方面的睡眠健康的首次评估，从而降低了白天的警惕性。在下面的研究中，我们进一步分析了大脑皮层活动在睡眠过程中，通过确定瞬时睡眠阶段的概率，表示为hypnodensity图，然后计算不同的EEG通道的向量互相关。我们可以证明，这种措施有助于估计睡眠周期的周期长度，从而可以帮助发现由于病理条件的干扰。人工智能导致睡眠研究的范式转变。使用深度神经网络进行睡眠阶段评分是稳健和可靠的。神经网络发现EEG原始数据的嵌入。瞬时睡眠阶段的概率可以被可视化为睡眠密度图。睡眠阶段概率向量的互相关提供睡眠周期时段长度的估计。
Automatic sleep stage scoring based on deep neural networks has come into focus of sleep researchers and physicians, as a reliable method able to objectively classify sleep stages would save human resources and simplify clinical routines. Due to novel open-source software libraries for machine learning, in combination with enormous recent progress in hardware development, a paradigm shift in the field of sleep research towards automatic diagnostics might be imminent. We argue that modern machine learning techniques are not just a tool to perform automatic sleep stage classification, but are also a creative approach to find hidden properties of sleep physiology. We have already developed and established algorithms to visualize and cluster EEG data, facilitating first assessments on sleep health in terms of sleep-apnea and consequently reduced daytime vigilance. In the following study, we further analyze cortical activity during sleep by determining the probabilities of momentary sleep stages, represented as hypnodensity graphs and then computing vectorial cross-correlations of different EEG channels. We can show that this measure serves to estimate the period length of sleep cycles and thus can help to find disturbances due to pathological conditions. Artificial intelligence leads to a paradigm shift in sleep research. Sleep stage scoring with deep neural networks is robust and reliable. Neural networks find embeddings of EEG raw data. Probabilities of momentary sleep stages can be visualized as hypnodensity graphs. Cross-correlations of sleep stage probability vectors provide an estimate of sleep cycle period length.