An Improved Method for Using Sample Entropy to Reveal Medical Information in Data from Continuously Monitored Physiological Signals

Xinzheng Dong, Chang Chen, Qingshan Geng, Zhixin Cao, Yu Jin, Yan Shi, Xiaohua Douglas Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Medical devices, especially wearables, are being under fast development for continuous monitoring of physiological signals. These devices generate a huge amount of continuous time series data. To derive meaningful and useful information out of these data, the adoption of nonlinear statistics is usually essential. Sample entropy is becoming a widely used nonlinear statistics to extract the information contained in continuous time series data for disease diagnosis and prognosis. However, missing values commonly exist in the physiological time series data. How to minimize the influence of missing points on the calculation of entropy remains an important problem in practice. In this paper, we propose a new method to handle missing values in this area. Unlike the usual ways by modifying the input data, such as direct deletion, our method keeps the data unchanged and modifies the calculation process, which employs a less intrusive way of dealing with missing values. Our research demonstrates that our method is effective and applicable to RR interval data in entropy analysis. Therefore, our proposed method may serve as an effective tool for dealing with missing values in the analysis of sample entropy for physiological signals.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
EditorsHarald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
Pages2502-2506
Number of pages5
ISBN (Electronic)9781538654880
DOIs
StatePublished - Jan 21 2019
Event2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain
Duration: Dec 3 2018Dec 6 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Country/TerritorySpain
CityMadrid
Period12/3/1812/6/18

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work was supported by University of Macau through Research Grants SRG2016-00083-FHS, MYRG2018-00071-FHS, and FHS-CRDA-029-002-2017.

Publisher Copyright:
© 2018 IEEE.

Keywords

  • complexity
  • entropy
  • missing values
  • physiological data
  • time series

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

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