[1]郑锐,刘久富,杨忠,等.马氏田口施密特度量学习算法研究[J].计算机技术与发展,2019,29(04):29-32.[doi:10. 3969 / j. issn. 1673-629X. 2019. 04. 006]
　ZHENG Rui,LIU Jiu-fu,YANG Zhong,et al.Research on Mahalanobis-Taguchi-Gram-Schmidt Metric Learning Algorithm[J].,2019,29(04):29-32.[doi:10. 3969 / j. issn. 1673-629X. 2019. 04. 006]
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马氏田口施密特度量学习算法研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年04期
页码:
29-32
栏目:
智能、算法、系统工程
出版日期:
2019-04-10

文章信息/Info

Title:
Research on Mahalanobis-Taguchi-Gram-Schmidt Metric Learning Algorithm
文章编号:
1673-629X(2019)04-0029-04
作者:
郑锐1; 刘久富1; 杨忠1; 王志胜1; 刘海阳2; 丁晓彬1
1. 南京航空航天大学 自动化学院,江苏 南京 211100;2. 东南大学 电子科学与工程学院,江苏 南京 210096
Author(s):
ZHENG Rui1; LIU Jiu-fu1; YANG Zhong1; WANG Zhi-sheng1; LIU Hai-yang2; DING Xiao-bin1
1. School of Automation,Nanjing University of Aeronautics and Astronautics,Nanjing 211100,China;2. School of Electronic Science and Engineering,Southeast University,Nanjing 210096,China
关键词:
异常检测; 马氏田口模型; 计算生物学; 信噪比
Keywords:
anomaly detection; Mahalanobis-Taguchi system; bio-computing; signal to noise ratio
分类号:
TP206. 2
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 04. 006
摘要:
人工判别人体睡眠质量一直存在着主观性过强,步骤繁琐以及准确性不高等局限性,此外由于人体睡眠异常检测研究的对象主要是人的脑电信号,传统意义上对大数据量的脑电信号仅凭经验观察和实验的方法已很难从中提取有效信息。对此,文中将马氏田口系统判别方法引入到生物计算学领域,提出一种基于马氏田口模型的睡眠质量自动检测算法。该算法通过对人脑双通道脑电信号进行建模分析,整个人睡眠的脑电信号已被人工睡眠专家标记成了六种状态的睡眠周期,算法在不同睡眠周期下,求取各个通道的标准化向量,同时对线性独立向量组进行施密特正交化处理,运用马氏田口施密特正交化方法计算出各个睡眠周期的信噪比均值,以判别出睡眠质量正常者和异常者。实验结果表明,该算法可以有效地对睡眠质量的正常和异常进行检测。
Abstract:
Artificial judgment of human sleep quality is always subjective,cumbersome and inaccurate. In addition,since human sleep anomaly detection is mainly performed on human electroencephalogram (EEG),it is difficult to extract effective information from large amounts of data on electroencephalogram by empirical observation and experimental methods. Therefore,we introduce the MahalanobisTaguchi system method into the field of bio - computing, and propose an automatic sleep quality detection algorithm based on that. Through the modeling and analysis of human brain dual-channel EEG signals,the entire human sleep EEG signal has been labeled as sixstate sleep stages by artificial sleep experts. Under different sleep stages,the normalized vector of each channel is obtained. At the same time,the linear independent vector group is subjected to Gram-Schmidt orthogonalization,and the mean value of the signal-to-noise ratio of each sleep phase is calculated by using the Mahalanobis-Taguchi-Gram-Schmidt method. So the normal quality of sleep and abnormal people can be identified. The experiment shows that the proposed algorithm can effectively detect the normal and abnormal quality of sleep.

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更新日期/Last Update: 2019-04-10