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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jun Jing YuHua Hu Xian Li Zheng Huang |
| Copyright Year | 2007 |
| Description | Author affiliation: Inst. of Biomed. Eng., Yan Shan Univ., Qinhuangdao (Jun Jing; YuHua Hu; Xian Li; Zheng Huang) |
| Abstract | A pulse diagnosis approach based on Hilbert-Huang transformation method and Singular Value Decomposition (SVD) technique is proposed. The Empirical Mode Decomposition (EMD) method is used to decompose the signal into a number of IMF components, then applying the Hilbert transformation to creating analytic signal and obtaining instantaneous frequency and instantaneous amplitude, from which the initial feature vector matrices are formed. By applying the singular value decomposition technique to the initial feature vector matrices, the singular values are obtained, which are regarded as the state feature vectors of the human pulse signals. Finally the first 20 singular values of SVD are showed in a parallel coordinate's graphic form. Practical examples show that the proposed approach can be applied to pulse diagnosis effectively. A method of mining pulse signal is presented for extracting the time-frequency distribution feature of the data based on the technique of the singular value decomposition. By the time-frequency analysis, the important pulse characteristic information is extracted, the research provide the basis for further classification. This provides one new method for the pulse diagnosis thorough research. It will be helpful to make the objectification of pulse study. |
| Starting Page | 1007 |
| Ending Page | 1010 |
| File Size | 347263 |
| Page Count | 4 |
| File Format | |
| ISBN | 1424411203 |
| DOI | 10.1109/ICBBE.2007.261 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-07-06 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Pathology Humans Time domain analysis Feature extraction Time frequency analysis Data mining Matrix decomposition Signal analysis Frequency domain analysis Singular value decomposition |
| Content Type | Text |
| Resource Type | Article |
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