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

Lei Liu; Naimin Li; Wangmeng Zuo; David Zhang; Hongzhi Zhang

doi:10.1007/978-3-642-36669-7_58

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

Lei Liu
, Naimin Li
, Wangmeng Zuo
, David Zhang
, Hongzhi Zhang

School of Computer Science and Technology, Harbin Institute of Technology
Harbin Binghua Hospital
Hong Kong Polytechnic University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

18 Scopus citations

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.

Original language	English
Title of host publication	Intelligent Science and Intelligent Data Engineering - Third Sino-Foreign-Interchange Workshop, IScIDE 2012, Revised Selected Papers
Pages	475-482
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-642-36669-7_58
State	Published - 2013
Externally published	Yes
Event	3rd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2012 - Nanjing, China Duration: 15 Oct 2012 → 17 Oct 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7751 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2012
Country/Territory	China
City	Nanjing
Period	15/10/12 → 17/10/12

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Feature extraction
Multiscale sample entropy
Pulse diagnosis
Wrist pulse blood flow signal

Access to Document

10.1007/978-3-642-36669-7_58

Cite this

Liu, L., Li, N., Zuo, W., Zhang, D., & Zhang, H. (2013). Multiscale sample entropy analysis of wrist pulse blood flow signal for disease diagnosis. In Intelligent Science and Intelligent Data Engineering - Third Sino-Foreign-Interchange Workshop, IScIDE 2012, Revised Selected Papers (pp. 475-482). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7751 LNCS). https://doi.org/10.1007/978-3-642-36669-7_58

Liu, Lei ; Li, Naimin ; Zuo, Wangmeng et al. / Multiscale sample entropy analysis of wrist pulse blood flow signal for disease diagnosis. Intelligent Science and Intelligent Data Engineering - Third Sino-Foreign-Interchange Workshop, IScIDE 2012, Revised Selected Papers. 2013. pp. 475-482 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{0dba33da649c4ca5b682cce5f0be480c,

title = "Multiscale sample entropy analysis of wrist pulse blood flow signal for disease diagnosis",

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",

author = "Lei Liu and Naimin Li and Wangmeng Zuo and David Zhang and Hongzhi Zhang",

year = "2013",

doi = "10.1007/978-3-642-36669-7\_58",

language = "英语",

isbn = "9783642366680",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "475--482",

booktitle = "Intelligent Science and Intelligent Data Engineering - Third Sino-Foreign-Interchange Workshop, IScIDE 2012, Revised Selected Papers",

note = "3rd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2012 ; Conference date: 15-10-2012 Through 17-10-2012",

}

Liu, L, Li, N, Zuo, W, Zhang, D & Zhang, H 2013, Multiscale sample entropy analysis of wrist pulse blood flow signal for disease diagnosis. in Intelligent Science and Intelligent Data Engineering - Third Sino-Foreign-Interchange Workshop, IScIDE 2012, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7751 LNCS, pp. 475-482, 3rd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2012, Nanjing, China, 15/10/12. https://doi.org/10.1007/978-3-642-36669-7_58

Multiscale sample entropy analysis of wrist pulse blood flow signal for disease diagnosis. / Liu, Lei; Li, Naimin; Zuo, Wangmeng et al.
Intelligent Science and Intelligent Data Engineering - Third Sino-Foreign-Interchange Workshop, IScIDE 2012, Revised Selected Papers. 2013. p. 475-482 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7751 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Multiscale sample entropy analysis of wrist pulse blood flow signal for disease diagnosis

AU - Liu, Lei

AU - Li, Naimin

AU - Zuo, Wangmeng

AU - Zhang, David

AU - Zhang, Hongzhi

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - Feature extraction

KW - Multiscale sample entropy

KW - Pulse diagnosis

KW - Wrist pulse blood flow signal

UR - https://www.scopus.com/pages/publications/84892935209

U2 - 10.1007/978-3-642-36669-7_58

DO - 10.1007/978-3-642-36669-7_58

M3 - 会议稿件

AN - SCOPUS:84892935209

SN - 9783642366680

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 475

EP - 482

BT - Intelligent Science and Intelligent Data Engineering - Third Sino-Foreign-Interchange Workshop, IScIDE 2012, Revised Selected Papers

T2 - 3rd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2012

Y2 - 15 October 2012 through 17 October 2012

ER -

Liu L, Li N, Zuo W, Zhang D, Zhang H. Multiscale sample entropy analysis of wrist pulse blood flow signal for disease diagnosis. In Intelligent Science and Intelligent Data Engineering - Third Sino-Foreign-Interchange Workshop, IScIDE 2012, Revised Selected Papers. 2013. p. 475-482. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-36669-7_58

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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this