文献类型：期刊文献

中文题名：基于改进的K均值聚类算法的睡眠自动分期研究

英文题名：Automatic Sleep Stage Classification Based on an Improved K-means Clustering Algorithm

作者：肖姝源[1];王蓓[1];张见[1];张群峰[2];邹俊忠[1]

机构：[1]华东理工大学信息科学与工程学院自动化系,上海200237;[2]上海诺城电气有限公司,上海200245

年份：2016

卷号：33

期号：5

起止页码：847

中文期刊名：生物医学工程学杂志

外文期刊名：Journal of Biomedical Engineering

收录：CSTPCD;;EI(收录号：20164302956462);Scopus;北大核心:【北大核心2014】；CSCD:【CSCD2015_2016】；PubMed;

基金：上海市自然科学基金项目资助(16ZR1407500);上海市科委科技创新行动计划资助(12DZ1940903)

语种：中文

中文关键词：K均值聚类;睡眠分期;脑电信号

外文关键词：K-means clustering; sleep stage classification; electroencephalography

摘要：睡眠分期是医学、神经信息领域的研究热点。人工标记睡眠数据是一项费时且费力的工作。自动睡眠分期方法能够减少人工分期的工作负荷,但在复杂多变的临床数据的应用上仍存在局限性。本文提出了一种改进的K均值聚类算法,主要目的是从实际睡眠数据的特点出发,研究睡眠自动分期方法。针对原始K均值聚类算法对初始聚类中心和离群点敏感的问题,本文结合密度的思想,选择周围数据密集的点作为初始中心,并根据"3σ法则"更新中心。改进算法在健康被试和接受持续正压通气(CPAP)治疗的睡眠障碍者的睡眠数据上进行了测试,平均分类精确度达到76%,同时结合实际睡眠数据的形态多样性验证讨论了该方法在临床数据上的可行性和有效性。
Sleep stage scoring is a hotspot in the field of medicine and neuroscience. Visual inspection of sleep is laborious and the results may be subjective to different clinicians. Automatic sleep stage classification algorithm can be used to reduce the manual workload. However, there are still limitations when it encounters complicated and changeable clinical cases. The purpose of this paper is to develop an automatic sleep staging algorithm based on the characteristics of actual sleep data. In the proposed improved K-means clustering algorithm, points were selected as the initial centers by using a concept of density to avoid the randomness of the original K-means algorithm. Meanwhile, the cluster centers were updated according to the ＇Three-Sigma Rule＇ during the iteration to abate the influence of the outliers. The proposed method was tested and analyzed on the overnight sleep data of the healthy persons and patients with sleep disorders after continuous positive airway pressure （CPAP） treatment. The automatic sleep stage classification results were compared with the visual inspection by qualified clinicians and the averaged accuracy reached 76%. With the analysis of morphological diversity of sleep data, it was proved that the proposed improved K-means algorithm was feasible and valid for clinical practice.