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: Arrhythmic Pulses Detection Using Lempel-Ziv Complexity Analysis | Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006 Article ID 18268 Pages 1-12 DOI ASP 2006 18268 Arrhythmic Pulses Detection Using Lempel-Ziv Complexity Analysis Lisheng Xu 1 David Zhang 2 Kuanquan Wang 1 and Lu Wang1 1 Department of Computer Science and Engineering School of Computer Sciences and Technology Harbin Institute of Technology HIT Harbin 150001 China 2 Department of Computing The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong China Received 24 January 2005 Revised 9 September 2005 Accepted 12 September 2005 Recommended for Publication by William Sandham Computerized pulse analysis based on traditional Chinese medicine TCM is relatively new in the field of automatic physiological signal analysis and diagnosis. Considerable researches have been done on the automatic classification of pulse patterns according to their features of position and shape but because arrhythmic pulses are difficult to identity until now none has been done to automatically identify pulses by their rhythms. This paper proposes a novel approach to the detection of arrhythmic pulses using the Lempel-Ziv complexity analysis. Four parameters one lemma and two rules which are the results of heuristic approach are presented. This approach is applied on 140 clinic pulses for detecting seven pulse patterns not only achieving a recognition accuracy of as assessed by experts in TCM but also correctly extracting the periodical unit of the intermittent pulse. Copyright 2006 Hindawi Publishing Corporation. All rights reserved. 1. INTRODUCTION The quantification and analysis of physiological signals have become more important recently. The research on traditional Chinese pulse diagnosis TCPD is relatively new in this area. Usually practitioners of TCPD use pulse sensors to acquire patients pulse waveforms of the wrists and then investigate the patients pulse waveforms 1-7 . Presently the long-term monitoring of pulse waveforms is .
