Sleep EEG Analysis Based on a Scale Mixture Model and its Application to Sleep Spindle Detection
Sleep EEG Analysis Based on a Scale Mixture Model and its Application to Sleep Spindle Detection
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基于尺度混合模型的睡眠脑电分析及其在睡眠纺锤波检测中的应用
DOI：
10.1109/sii52469.2022.9708856
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
and Toshio Tsuji
中科院分区：
文献类型：
--
作者：
Miyari Hatamoto;Akira Furui;Keiko Ogawa;and Toshio Tsuji
本文提出了一种基于尺度混合模型的睡眠脑电分析方法。在尺度混合模型中，假设EEG信号是服从具有相同均值和不同协方差矩阵的高斯分布的无限混合的随机变量，从而允许表示EEG幅度的随机波动。首先，将尺度混合模型与带通滤波和滑动窗口相结合，提出了一种睡眠脑电信号分析方法，从而可以在特定频段内对模型参数进行时间序列估计。然后，在实验中，我们使用所提出的分析方法对快速眼动睡眠(REM)和睡眠II阶段的脑电信号进行了分析。结果表明，该方法捕捉到了不同睡眠阶段脑电幅值分布的特征变化。此外，我们重点研究了睡眠阶段II的睡眠纺锤波，这是睡眠脑电中的一种独特的波，并以所提出的方法定义的特征作为输入，通过机器学习来验证它们的可检测性。
This paper presents analysis of sleep electroen-cephalogram (EEG) based on a scale mixture model. In the scale mixture model, the EEG signal is assumed to be a random variable that follows a infinite mixture of Gaussian distributions with the same mean and different covariance matrices, thereby allowing the representation of the stochastic fluctuation of the EEG amplitude. First, a sleep EEG analysis method was proposed by combining the scale mixture model with band-pass filters and a sliding window, thereby allowing the time-series estimation of the model parameters in a specific frequency band. Then, in experiments, we analyzed the EEG signals during rapid eye movement (REM) sleep and sleep stage II using the proposed analysis method. The results showed that the proposed method captures the characteristic changes in the amplitude distribution of the EEG depending on the sleep stage. Furthermore, we focused on sleep spindles in sleep stage II, which are distinctive waves in sleep EEG, and verified their detectability by machine learning using the features defined by the proposed method as input.