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Open AccessArticle

An Improved Method of Handling Missing Values in the Analysis of Sample Entropy for Continuous Monitoring of Physiological Signals

School of Software Engineering, South China University of Technology, Guangzhou 510006, China
Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Zhuhai College of Jilin University, Zhuhai 519041, China
Faculty of Health Sciences, University of Macau, Taipa, Macau 999078, China
Guangdong General Hospital, Guangdong Academy of Medical Science, Guangzhou 510080, China
Beijing Engineering Research Center of Diagnosis and Treatment of Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Beijing 100043, China
Department of Endocrinology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China
School of Law, Washington University, St. Louis, MO 63130, USA
Department of Mechanical and Electronic Engineering, Beihang University, Beijing 100191, China
BARDS, Merck Research Laboratories, Upper Gwynedd, PA 19454, USA
Author to whom correspondence should be addressed.
These authors contributed equally.
Entropy 2019, 21(3), 274;
Received: 11 January 2019 / Revised: 8 March 2019 / Accepted: 9 March 2019 / Published: 12 March 2019
(This article belongs to the Special Issue Multiscale Entropy Approaches and Their Applications)
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. View Full-Text
Keywords: sample entropy; missing values; physiological data; complexity; medical information sample entropy; missing values; physiological data; complexity; medical information
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Dong, X.; Chen, C.; Geng, Q.; Cao, Z.; Chen, X.; Lin, J.; Jin, Y.; Zhang, Z.; Shi, Y.; Zhang, X.D. An Improved Method of Handling Missing Values in the Analysis of Sample Entropy for Continuous Monitoring of Physiological Signals. Entropy 2019, 21, 274.

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