a fixed-point blind source extraction algorithm and its application to ecg data analysis

ISCAS OpenIR

	a fixed-point blind source extraction algorithm and its application to ecg data analysis
	Zhang Hongjuan; Wu Zikai; Ding Shuxue; Chen Luonan
	2012
会议名称	2012 IEEE 6th International Conference on Systems Biology, ISB 2012
会议录名称	2012 IEEE 6th International Conference on Systems Biology, ISB 2012
页码	73-78
会议日期	August 18, 2012 - August 20, 2012
会议地点	Xi'an, China
收录类别	EI
ISBN	9781467343985
部门归属	(1) Department of Mathematics Shanghai University Shanghai 200444 China; (2) Business School University of Shanghai for Science and Technology Shanghai 200093 China; (3) Department of Computer Software University of Aizu Tsuruga Ikki-Machi Aizu-Wakamatsu City Fukushima 965-8580 Japan; (4) Key Laboratory of Systems Biology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai 200031 China
摘要	Generalized autocorrelations and complexity pursuit are two recently developed methods for extracting interesting component from time series. They are the extensions of projection pursuit to time series data. In this paper, a fixed-point blind source extraction (BSE) algorithm for generalized autocorrelations and complexity pursuit of the desired signals is presented. The fixed-point algorithm inherits the advantages of the well-known FastICA algorithm of ICA, which is very simple, converges fast, and does not need to choose any learning step sizes. Numerical experiments on electrocardiogram (ECG) data indicate its better performance. © 2012 IEEE.; Generalized autocorrelations and complexity pursuit are two recently developed methods for extracting interesting component from time series. They are the extensions of projection pursuit to time series data. In this paper, a fixed-point blind source extraction (BSE) algorithm for generalized autocorrelations and complexity pursuit of the desired signals is presented. The fixed-point algorithm inherits the advantages of the well-known FastICA algorithm of ICA, which is very simple, converges fast, and does not need to choose any learning step sizes. Numerical experiments on electrocardiogram (ECG) data indicate its better performance. © 2012 IEEE.
关键词	Algorithms Autocorrelation Electrocardiography Time Series
主办者	National Natural Science Foundation of China (NSFC); Academy of Mathematics and Systems Sciences of CAS (AMSS); Shanghai Institutes for Biological Sciences of CAS (SIBS); Xidian University; K. C. Wong Education Foundation
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/15952
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Zhang Hongjuan,Wu Zikai,Ding Shuxue,et al. a fixed-point blind source extraction algorithm and its application to ecg data analysis[C],2012:73-78.