Multiscale sample entropy analysis of wrist pulse blood flow signal for disease diagnosis

Authors:
Lei Liu;Naimin Li;Wangmeng Zuo;David Zhang;Hongzhi Zhang
Affiliations:
Biocomputing Research Centre, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;Biocomputing Research Centre, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China,Harbin Binghua Hospital, Harbin, China;Biocomputing Research Centre, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;Biocomputing Research Centre, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China,Biometrics Research Centre, Department of Computing, The Hong Kong Polytechni ...;Biocomputing Research Centre, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
Venue:
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2012

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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.