【摘要】 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。更多还原

【Abstract】 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.更多还原