基于改进的K均值聚类算法的睡眠自动分期研究
发布时间：2018-07-31 12:05
【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。
[Abstract]:Sleep staging is a hot topic in the field of medicine and neuroinformation. Manually marking sleep data is a time-consuming and laborious task. Automatic sleep staging method can reduce the workload of artificial staging, but there are still limitations in the application of complex and changeable clinical data. An improved K-means clustering algorithm is proposed in this paper. The main purpose of this algorithm is to study the automatic sleep staging method based on the characteristics of actual sleep data. In order to solve the problem that the original K-means clustering algorithm is sensitive to the initial clustering center and the outlier point, this paper combines the idea of density, selects the data dense points around as the initial center, and updates the center according to the "3 蟽 rule". The improved algorithm was tested on sleep data in healthy subjects and patients with sleep disorders treated with continuous positive pressure ventilation (CPAP). The average classification accuracy is 76 and the feasibility and effectiveness of the method in clinical data are discussed in combination with the morphological diversity of actual sleep data.
【作者单位】： 华东理工大学信息科学与工程学院自动化系;上海诺城电气有限公司;
【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)
【分类号】：R740
，
本文编号：2155535
[Abstract]:Sleep staging is a hot topic in the field of medicine and neuroinformation. Manually marking sleep data is a time-consuming and laborious task. Automatic sleep staging method can reduce the workload of artificial staging, but there are still limitations in the application of complex and changeable clinical data. An improved K-means clustering algorithm is proposed in this paper. The main purpose of this algorithm is to study the automatic sleep staging method based on the characteristics of actual sleep data. In order to solve the problem that the original K-means clustering algorithm is sensitive to the initial clustering center and the outlier point, this paper combines the idea of density, selects the data dense points around as the initial center, and updates the center according to the "3 蟽 rule". The improved algorithm was tested on sleep data in healthy subjects and patients with sleep disorders treated with continuous positive pressure ventilation (CPAP). The average classification accuracy is 76 and the feasibility and effectiveness of the method in clinical data are discussed in combination with the morphological diversity of actual sleep data.
【作者单位】： 华东理工大学信息科学与工程学院自动化系;上海诺城电气有限公司;
【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)
【分类号】：R740
，
本文编号：2155535
本文链接：https://www.wllwen.com/huliyixuelunwen/2155535.html
最近更新
教材专著
