Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/20505
Title: Multiscale sample entropy analysis of wrist pulse blood flow signal for disease diagnosis
Authors: Liu, L
Li, N
Zuo, W
Zhang, D 
Zhang, H
Issue Date: 2013
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2013, v. 7751 LNCS, p. 475-482
Abstract: Recent study reported that wrist pulse blood flow signal is effective for disease diagnosis. The multiscale entropy, which was developed for quantifying the complexity of a time series of physiological signals over a range of scales, had been widely applied for feature extraction from medical signals. In this paper, using the multiscale sample entropy (Multi-SampEn) algorithm, we compute the value of SampEn of wrist pulse blood flow signal that includes 83 samples healthy persons, 45 samples of patients with liver diseases (LD), and 45 with sugar diabetes (SD). Then we use the values of SampEn as the feature input of the support vector machine classifier for disease diagnosis. Experimental results show that the proposed method could achieve the classification accuracy of 76.30% with the dimension m = 2 and the threshold r = 0.6, which is promising in diagnosing the healthy subjects, liver diseases, and sugar diabetes.
Keywords: Feature extraction
Multiscale sample entropy
Pulse diagnosis
Wrist pulse blood flow signal
Publisher: Springer
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
ISBN: 9783642366680
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-642-36669-7_58
Description: 3rd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2012, Nanjing, 15-17 October 2012
Appears in Collections:Conference Paper

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