NDLI: Lifted wavelet transforms with application to human pulse identification

Content Provider	IEEE Xplore Digital Library
Author	Kunbao Cai Long Wan Decheng Luo
Copyright Year	2011
Description	Author affiliation: College of Communication Engineering, Chongqing University, Chongqing, China (Kunbao Cai; Long Wan) \|\| Rehabilitation Center for Drug Dependence, Chongqing Mental Hospital, Chongqing, China (Decheng Luo)
Abstract	By means of a commonly used lifting decomposition algorithm based on D4 wavelet, the first-level lifted wavelet transform is applied to the pulse waves obtained from 20 healthy normal subjects and 20 heroin addicts. Then, 2-dimensional feature vectors are extracted that can be used to effectively identify the healthy normal subject and heroin addict pulse waves. On this basis, the authors of this paper carefully researched the algorithm principle of lifting wavelet transforms. Considering the non-uniqueness of polyphase matrix decompositions, the authors derived out another decomposition form of the polyphase matrix based on D4 wavelet, as well as its corresponding lifting realization algorithm. The derived algorithm is, respectively, used to the first-level lifted wavelet transform of the 40 pulse waves. Then, the 2-dimensional feature vector is extracted for every pulse wave. The result of experimental analysis shows that the algorithm presented by this paper, comparing with the commonly used algorithm, has a better identification rate for identifying the healthy normal subject and heroin addict pulse waves.
Starting Page	798
Ending Page	802
File Size	842335
Page Count	5
File Format	PDF
ISBN	9781457716126
e-ISBN	9781457716133
DOI	10.1109/BIBMW.2011.6112474
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms polyphase matrix feature extraction lifted wavelet transfom Feature extraction Wavelet analysis Finite impulse response filter Lifting scheme Vectors Matrix decomposition human pulse
Content Type	Text
Resource Type	Article

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