Mohammadi Sara Mahvash, Enshaeifar Shirin, Ghavami Mohammad, Sanei Saeid
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:4769-72. doi: 10.1109/EMBC.2015.7319460.
In this study, a single-channel electroencephalography (EEG) analysis method has been proposed for automated 3-state-sleep classification to discriminate Awake, NREM (non-rapid eye movement) and REM (rapid eye movement). For this purpose, singular spectrum analysis (SSA) is applied to automatically extract four brain rhythms: delta, theta, alpha, and beta. These subbands are then used to generate the appropriate features for sleep classification using a multi class support vector machine (M-SVM). The proposed method provided 0.79 agreement between the manual and automatic scores.
在本研究中，提出了一种单通道脑电图（EEG）分析方法，用于自动进行三状态睡眠分类，以区分清醒、非快速眼动（NREM）和快速眼动（REM）状态。为此，应用奇异谱分析（SSA）自动提取四种脑电波节律：δ波、θ波、α波和β波。然后，使用多类支持向量机（M-SVM）将这些子带用于生成睡眠分类的适当特征。所提出的方法在人工评分和自动评分之间的一致性为0.79。
