基于支持向量机的睡眠结构分期研究

为了提高睡眠结构分期的准确度，克服分类时样本不足对分类的影响，使用MIT-BIH数据库整晚睡眠脑电数据作为研究样本，提取了时域、频域和非线性共16个参数作为分类特征，用支持向量机的一对一多类分类方法，采用顺序最小优化算法，以径向基函数作为核函数对样本分类。分类结果与专家的分类标注对比，分类准确率达到92%以上。支持向量机可作为睡眠分期的一种实用算法。

Study of sleep architecture stage based on Support Vector Machines

In order to increase the accuracy of sleep architecture stage and overcome the influence in classification brought by samples shortage，all night sleep EEG data from MIT-BIH database are accepted as the research sample and totally 16 parameters including time-domain，frequency-domain and nonlinear picked up for sleep classification using 1-against-1 multi-classification method of Support Vector Machines using Sequential Minimal Optimization algorithm and selecting radial basis function as the kernel function.In contrast to expert’s manually-scored classification label，the classification result is more than 92%.It shows that Support Vector Machines can be a kind of effective algorithm in sleep stage.