ArticleEntropy (Basel, Switzerland)2019
An Improved Method of Handling Missing Values in the Analysis of Sample Entropy for Continuous Monitoring of Physiological Signals.
Article in Entropy (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Same ages, different stages: Pubertal development in 9- and 10-year olds is associated with entropy of fMRI signals.Developmental cognitive neuroscience · 2026Article
- Optimizing the measurement of sample entropy in resting-state fMRI data.Frontiers in neurology · 2024Article
- Missing data imputation techniques for wireless continuous vital signs monitoring.Journal of clinical monitoring and computing · 2023Article
- Complexity analysis of head movements in autistic toddlers.Journal of child psychology and psychiatry, and allied disciplines · 2023Article
- A Pragmatic Ensemble Strategy for Missing Values Imputation in Health Records.Entropy (Basel, Switzerland) · 2022Article
- Development and validation of a sample entropy-based method to identify complex patient-ventilator interactions during mechanical ventilation.Scientific reports · 2020Article
- Multiscale Entropy Approaches and Their Applications.Entropy (Basel, Switzerland) · 2020Article
- A Novel Microwave Treatment for Sleep Disorders and Classification of Sleep Stages Using Multi-Scale Entropy.Entropy (Basel, Switzerland) · 2020Article
- Augmentation of Dispersion Entropy for Handling Missing and Outlier Samples in Physiological Signal Monitoring.Entropy (Basel, Switzerland) · 2020Article
- A comprehensive comparison and overview of R packages for calculating sample entropy.Biology methods & protocols · 2019Article
- Exploring Complexity of Facial Dynamics in Autism Spectrum Disorder.IEEE transactions on affective computingArticle
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.