An improved method of handling missing values in the analysis of sample entropy for continuous monitoring of physiological signals

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

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


Medical devices generate huge amounts of continuous time series data. However, missing values commonly found in these data can prevent us from directly using analytic methods such as sample entropy to reveal the information contained in these data. To minimize the influence of missing points on the calculation of sample entropy, we propose a new method to handle missing values in continuous time series data. We use both experimental and simulated datasets to compare the performance (in percentage error) of our proposed method with three currently used methods: skipping the missing values, linear interpolation, and bootstrapping. Unlike the methods that involve modifying the input data, our method modifies the calculation process. This keeps the data unchanged which is less intrusive to the structure of the data. The results demonstrate that our method has a consistent lower average percentage error than other three commonly used methods in multiple common physiological signals. For missing values in common physiological signal type, different data size and generating mechanism, our method can more accurately extract the information contained in continuously monitored data than traditional methods. So it may serve as an effective tool for handling missing values and may have broad utility in analyzing sample entropy for common physiological signals. This could help develop new tools for disease diagnosis and evaluation of treatment effects.

Original languageEnglish
Article number274
Issue number3
StatePublished - Mar 1 2019

Bibliographical note

Publisher Copyright:
© 2019 by the authors.


  • Complexity
  • Medical information
  • Missing values
  • Physiological data
  • Sample entropy

ASJC Scopus subject areas

  • Information Systems
  • Mathematical Physics
  • Physics and Astronomy (miscellaneous)
  • Electrical and Electronic Engineering


Dive into the research topics of 'An improved method of handling missing values in the analysis of sample entropy for continuous monitoring of physiological signals'. Together they form a unique fingerprint.

Cite this