文献类型：期刊文献

中文题名：基于改进K均值聚类及其距离修正的睡眠分期方法

英文题名：Automatic sleep staging based on improved K-means clustering and distance correction

作者：于莹[1];王蓓[1];马家睿[1];王行愚[1]

机构：[1]化工过程先进控制和优化技术教育部重点实验室(华东理工大学),上海200237

年份：2020

卷号：40

期号：S01

起止页码：269

中文期刊名：计算机应用

外文期刊名：journal of Computer Applications

收录：CSTPCD;;北大核心:【北大核心2017】；CSCD:【CSCD_E2019_2020】；

基金：国家自然科学基金资助项目(61773164);上海市自然科学基金资助项目(16ZR1407500)。

语种：中文

中文关键词：K均值聚类;睡眠分期;距离修正;脑电信号;分步聚类处理

外文关键词：K-means clustering;sleep staging;distance correction;ElectroEncephaloGraphy(EEG);stepped clustering processing

摘要：针对原始K均值聚类算法的局限性,设计了一种改进的K均值聚类算法,结合不同睡眠阶段的特性,实现睡眠阶段的自动分期。首先,针对原始K均值选取初始聚类中心的随机性,基于密度和距离两项指标来选取初始聚类中心,使得初始聚类中心的选择更加合理,从而提高算法的稳定性;其次,选用高斯核函数作为聚类中心更新时的权值,减少离群点对中心的影响;然后,根据不同睡眠状态的特征,设计了分步聚类处理方式;最后,定义了距离修正系数对K均值聚类的结果加以修正,使其聚类结果更符合实际睡眠状态变化规律。将改进算法分别在来自不同数据集的睡眠数据上进行了测试,比原始K均值聚类有显著提升,且更贴近人工判读,能够为睡眠状态分析提供可行的辅助判读方式。
Due to the limitations of the original K-means clustering algorithm,an improved K-means clustering algorithm was developed for automatic sleep staging by considering the characteristics of sleep stages. Unlike the randomly selected initial clustering centers,two indexes based on density and distance were designed to make the determination of initial cluster centers more reasonable and improve the stability of the algorithm. The Gaussian kernel function was adopted as the weight to update the cluster centers,which can reduce the influence of the outliers. Based on the characteristics of sleep states,a stepped clustering processing was implemented. The distance correction coefficients were defined according to the actual sleep states regulation to modify the clustering results. The improved K-means clustering algorithm was tested on the sleep data from different datasets respectively. Comparing with the original K-means clustering algorithm,the classification performance was improved. The obtained results were more close to the visual scoring by clinicians. The presented method can provide a feasible assistant way for sleep state analysis.