Accession number:20091912070094
Title: Fuzzy C-means clustering for myocardial ischemia estimation with pulse waveform analysis
Authors: Liu, Shing-Hong (1); Chang, Kang-Ming (2); Tyan, Chu- Chang (3)
Author affiliation:(1) Department of Computer Science and Information Engineering, Chaoyang University of Technology,
Taichung, Taiwan; (2) Department of Computer and Communication Engineering, Asia University, Taichung, Taiwan; (3) Division of
Chinese Medicine, Buddhist Tzu Chi General Hospital, Sindian City, Taipei, Taiwan; (4) Department of Computer and Communication Engineering, Asia University, 500, Lioufeng Rd., Wufeng, Taichung 41354, Taiwan
Corresponding author:Chang, K.-M.
(changkm@asia.edu.tw)
Source title: Biomedical Engineering - Applications, Basis and Communications
Abbreviated source title:Biomed. Eng. Appl. Basis Commun.
Volume:21 Issue:2
Issue date:April 2009 Publication year:2009 Pages:139-147
Language:English ISSN:10162372 CODEN:YIGOEO
Document type:Journal article (JA)
Publisher:World Scientific Publishing Co. Pte. Ltd, 5 Toh Tuck Link, Singapore, 596224, Singapore
Abstract:The purpose of this study is to build an automatic disease classification algorithm by pulse waveform analysis, based on a Fuzzy C-means clustering algorithm. A self designed three-axis mechanism was used to detect the optimal position to accurately measure the pressure pulse waveform (PPW). Considering the artery as a cylinder, the sensor should detect the PPW with the lowest possible distortion, and hence an analysis of the vascular geometry and an arterial model were used to design a standard positioning
procedure based on the arterial diameter changed waveform for the X-axes (perpendicular to the forearm) and Z-axes (perpendicular to the radial artery). A fuzzy C-means algorithm was used to estimate the myocardial ischemia symptoms in 35 elderly subjects with the PPW of the radial artery. Two type parameters were used to make the features, one was a harmonic value of Fourier transfer, and the other was a form factor value. A receiver operating characteristics curve was used to determine the optimal decision function. The harmonic feature vector contain second, third and fourth harmonics (H<inf>2</inf>, H<inf>3</inf>, H<inf>4</inf>) performed at the level of 69% for sensitivity and 100% for specificity while the form factor feature vector derived from left hand (LFF) and right hand (RFF) performed at the level of 100% for sensitivity and 53% for specificity. The FCM- and ROC-based clustering approach may
become an efficient alternative for distinguishing patients in the risk of myocardial ischemia, besides the traditional exercise ECG
examination. © 2009 World Scientific Publishing Company.
Number of references:24
Main heading:Clustering algorithms
Controlled terms: Approximation theory - Copying - Fuzzy clustering - Fuzzy systems - Harmonic analysis - Image retrieval - Risk
perception - Tools - Waveform analysis
Uncontrolled terms: Form factor - Fuzzy C-means - Harmonic - Myocardial ischemia - Pulse waveform analysis
Classification code:903.2 Information Dissemination - 914.1 Accidents and Accident Prevention - 921 Mathematics - 903.1 Information Sources and Analysis - 921.4 Combinatorial
Mathematics, Includes Graph Theory, Set Theory - 922.1 Probability Theory - 961 Systems Science - 921.6 Numerical Methods - 745.2 Reproduction, Copying - 605 Small Tools and Hardware - 721 Computer Circuits and Logic Elements - 723 Computer Software, Data Handling and Applications - 603 Machine Tools - 723.2 Data Processing and Image Processing - 731.1 Control Systems - 741 Light, Optics and Optical Devices - 723.4 Artificial Intelligence DOI:10.4015/S1016237209001143
Database:Compendex
Compilation and indexing terms, Copyright 2009 Elsevier Inc.
