Evaluation of a combined wavelet and a combined principal component analysis classification system for BCG diagnostic problem

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	Evaluation of a combined wavelet and a combined principal component analysis classification system for BCG diagnostic problem
	Yu, XS ; Gong, DJ ; Li, SR ; Xu, YP ; Palade, V ; Howlett, RJ ; Jain, L
刊名	KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 1, PROCEEDINGS
	2003
卷号	2773 页码:646-652
关键词	Heart-disease Ballistocardiogram
ISSN号	0302-9743
文献子类	Article
英文摘要	Heart disease is one of the main factor causing death in the developed countries. Over several decades, variety of electronic and computer technology have been developed to assist clinical practices for cardiac performance monitoring and heart disease diagnosis. Among these methods, Ballistocardiography (BCG) has an interesting feature that no electrodes are needed to be attached to the body during the measurement. Thus, it is provides a potential application to asses the patients heart condition in the home. In this paper, a comparison is made for two neural networks based BCG signal classification models. One system uses a principal component analysis (PCA) method, and the other a discrete wavelet transform, to reduce the input dimensionality. It is indicated that the combined wavelet transform and neural network has a more reliable performance than the combined PCA and neural network system. Moreover, the wavelet transform requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced.; Heart disease is one of the main factor causing death in the developed countries. Over several decades, variety of electronic and computer technology have been developed to assist clinical practices for cardiac performance monitoring and heart disease diagnosis. Among these methods, Ballistocardiography (BCG) has an interesting feature that no electrodes are needed to be attached to the body during the measurement. Thus, it is provides a potential application to asses the patients heart condition in the home. In this paper, a comparison is made for two neural networks based BCG signal classification models. One system uses a principal component analysis (PCA) method, and the other a discrete wavelet transform, to reduce the input dimensionality. It is indicated that the combined wavelet transform and neural network has a more reliable performance than the combined PCA and neural network system. Moreover, the wavelet transform requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced.
学科主题	Computer Science, Artificial Intelligence
语种	英语
WOS记录号	WOS:000186518000088
公开日期	2010-12-22
内容类型	期刊论文
源URL	[http://ir.qdio.ac.cn/handle/337002/2365]
专题	海洋研究所_海洋环境工程技术研究发展中心
作者单位	1.Ocean Univ China, Marine Geol Coll, Qingdao 266003, Peoples R China 2.Chinese Acad Sci, Inst Oceanol, Qingdao 266071, Peoples R China
推荐引用方式 GB/T 7714	Yu, XS,Gong, DJ,Li, SR,et al. Evaluation of a combined wavelet and a combined principal component analysis classification system for BCG diagnostic problem[J]. KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 1, PROCEEDINGS,2003,2773:646-652.
APA	Yu, XS.,Gong, DJ.,Li, SR.,Xu, YP.,Palade, V.,...&Jain, L.(2003).Evaluation of a combined wavelet and a combined principal component analysis classification system for BCG diagnostic problem.KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 1, PROCEEDINGS,2773,646-652.
MLA	Yu, XS,et al."Evaluation of a combined wavelet and a combined principal component analysis classification system for BCG diagnostic problem".KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 1, PROCEEDINGS 2773(2003):646-652.