1. 上海交通大学 机械与动力工程学院， 上海 200240； 2. 上海市儿童医院， 上海 200240
出版日期:2019-03-28发布日期:2019-03-28通讯作者:胡洁，男，教授，博士生导师，电话(Tel.):021-34206552; E-mail: hujie@sjtu.edu.cn.作者简介:叶仙(1991-)，女，安徽省桐城市人，硕士生，目前主要从事生物医学信号处理研究.基金资助:国家重点研发计划专项(2016YFF0101602，2016YFC0104104)，国家重大科学仪器设备开发专项(2013YQ03065105)，上海交通大学“医-工交叉研究基金”(YG2014MS12)资助项目Automatic Sleep Scoring Based on Refined Composite Multi-Scale Entropy and Support Vector Machine
YE Xian,HU Jie,TIAN Pan,QI Jin,CHE Datian,DING Ying1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;2. Shanghai Children’s Hospital, Shanghai 200240, China
Online:2019-03-28Published:2019-03-28摘要/Abstract
摘要： 提出将脑电信号与眼动信号的精细复合多尺度熵作为睡眠分期依据，利用多层次支持向量机的机器学习算法对睡眠进行自动分期.利用精细复合多尺度熵对睡眠信号进行特征提取，选用脑电以及眼电通道的信号，以保证输入特性的可靠性，并通过3层支持向量机实现了睡眠的自动分期.结果表明，分类器的输入参数可由熵值曲线的变化特征来确定.基于精细复合多尺度熵的多层次支持向量机算法的睡眠分期准确率达到85.3%，与已有的分类算法相比，所提出的算法更加均衡，且整体分类效果更佳.
关键词: 睡眠分期, 精细, 复合多尺度熵
Abstract: Sleep scoring is an important research direction in medical research and clinical medicine. Traditional visual scoring method is based on scoring rules, which is a time consuming and subjective procedure. Therefore an automatic sleep staging method based on refined composite multi-scale entropy (CMSE) and multi-level support vector machine is proposed. Firstly, to ensure the reliability of the input characteristics, refined CMSE is extracted as the feature input and two channels of electroencephalogram (EEG) and electrooculogram (EOG) are used. Then a three-layer support vector machine classification scheme is applied to classify sleep stages. Specifically, the inputs of each layer are obtained according to the trend of the entropy curves. The overall accuracy of the proposed method is 85.3%. Compared with traditional methods, the classification accuracy of the proposed method is more balanced and the global performance is much better.
Key words: sleep scoring, refined, composite multi-scale entropy (CMSE)
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