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Báo cáo hóa học: " Research Article A Framework for Automatic Time-Domain Characteristic Parameters Extraction of Human Pulse Signals"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article A Framework for Automatic Time-Domain Characteristic Parameters Extraction of Human Pulse Signals | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 468390 9 pages doi 10.1155 2008 468390 Research Article A Framework for Automatic Time-Domain Characteristic Parameters Extraction of Human Pulse Signals Pei-Yong Zhang1 and Hui-Yan Wang2 1 Institute of VLSI Design Zhejiang University Hangzhou 310027 China 2 College of Computer Science and Information Engineering Zhejiang Gongshang University Hangzhou 310018 China Correspondence should be addressed to Hui-Yan Wang cederic@mail.zjgsu.edu.cn Received 21 May 2007 Revised 17 September 2007 Accepted 19 November 2007 Recommended by Tan Lee A methodology for the automated time-domain characteristic parameter extraction of human pulse signals is presented. Due to the subjectivity and fuzziness of pulse diagnosis the quantitative methods are needed. Up to now the characteristic parameters are mostly obtained by labeling manually and reading directly from the pulse signal which is an obstacle to realize the automated pulse recognition. To extract the parameters of pulse signals automatically the idea is to start with the detection of characteristic points of pulse signals based on wavelet transform and then determine the number of pulse waves based on chain code to label the characteristics. The time-domain parameters which are endowed with important physiological significance by specialists of traditional Chinese medicine TCM are computed based on the labeling result. The proposed methodology is testified by applying it to compute the parameters of five hundred pulse signal samples collected from clinic. The results are mostly in accord with the expertise which indicate that the method we proposed is feasible and effective and can extract the features of pulse signals accurately which can be expected to facilitate the modernization of pulse diagnosis. Copyright 2008 P.-Y. Zhang and H.-Y. Wang. This is an open access article distributed under the Creative Commons .