删除或更新信息,请邮件至freekaoyan#163.com(#换成@)

基于精细复合多尺度熵与支持向量机的睡眠分期

本站小编 Free考研考试/2022-02-12

鐎殿喒鍋撻梺顐g濠€鎵博濮楃P闁挎稒鐭粩瀛樼▔閸モ晩娼氶柤鏉垮暟閻栫儤绋夐幘鑼懝閻犲洦宕橀~瀣紣閹达附顓瑰〒姘虫硶濠€鍛存晬鐏炲墽妲ㄩ柡鍫厸缁楀宕氭0浣侯伇闁告帒妫濋幐鍫曟晬娴h櫣鍗滈悗鐢垫嚀閸ㄦ繄绮诲Δ瀣<
547闁圭鍋撻梻鍕╁灪閻楀酣鎳撻崘顏嗗煛闁兼澘鍟畷锟�1130缂佸绉电€垫氨鈧纰嶉弳鈧柡澶嬪姉濞堟垿宕¢崘褏绋囩紒澶婄Ч閸樸倖绺藉Δ鍜佹毌閹煎瓨鎸堕埀顑挎祰椤锛愰幋顖滅婵炴垹鏁稿ú濠囨嚐鏉堫偒鍤旈柕鍡曡兌缁€趁规惔娑掑亾娴g晫妲堥柛鎺撴偠閳ь兛绶氶崳楣冩懚瀹ュ啠鍋撴担鐑樺€炵€规悶鍎埀顑胯兌椤撴悂鎮堕崱鎰ㄥ亾娴f亽浠﹀ù鍏煎搸閳ь兛娴囬崒銊﹀濮樸儮鍋撴担瑙勬畬闁煎弶褰冪缓楣冩偠閸℃劏鍋撴担鐤幀闁哄倸娲㈤埀顑挎祰婢规捇寮甸妯峰亾娴h鐓€闂傚倽顔婄槐鍫曞箻椤撴壕鍋撴担鍦€婇悗娑宠礋閳ь兛绀佺亸鎵偓娑宠礋閳ь兛娴囬鍝ョ不濡や焦绨氶柕鍡曠瀹稿宕g仦鍌楀亾娴e憡鍕鹃柣鐐叉閳ь兛鐒﹂弬鍌氣柦濮瑰洠鍋撴担鍛婃噸閻庢冻璐熼埀顑挎缂嶅鎳栭懠顒冾潶缂佹冻鎷�28缂侇偉顕ч鐔虹矓閹搭垳纾�
叶仙1,胡洁1,田畔1,戚进1,车大钿2,丁颖2
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 Ying
1. 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)


PDF全文下载地址:

点我下载PDF
闁归潧顑嗗┃鈧煫鍥跺亰閳ь剛鍠庨崢銈囨嫻鐟欏嫭鏆堥柛鎰缁变即宕ㄩ敓锟�闁挎稑鐬奸悵娑㈠础鐎圭姴绠柛娆愮墬濠€鎵博濞嗘帞銈柡鍌涚懆琚欓柛妯侯儑缂傚鈧潧妫涢悥婊堟晬閿燂拷
相关话题/信号 上海交通大学 上海 工程学院 博士生导师