Evidence map›Paper›PMID 37509944›Full record

ArticleEntropy (Basel, Switzerland)2023

Variable-Step Multiscale Fuzzy Dispersion Entropy: A Novel Metric for Signal Analysis.

Yuxing Li, Junxian Wu, Shuai Zhang, Bingzhao Tang, Yilan Lou

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yuxing LiSchool of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.ORCID 0000-0001-5035-223X
Junxian WuSchool of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.
Shuai ZhangSchool of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.
Bingzhao TangSchool of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.
Yilan LouSchool of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.

Funding

Natural Science Foundation of Shaanxi Province 2022JM-337Natural Science Foundation of Shaanxi Province 2022JQ-609Xi 'an University of Technology Excellent Seed Fund 252082218Xi 'an University of Technology Excellent Seed Fund 252082220
6 · The paper itself

Abstract

Fuzzy dispersion entropy (FuzDE) is a newly proposed entropy metric, which combines the superior characteristics of fuzzy entropy (FE) and dispersion entropy (DE) in signal analysis. However, FuzDE only reflects the feature from the original signal, which ignores the hidden information on the time scale. To address this problem, we introduce variable-step multiscale processing in FuzDE and propose variable-step multiscale FuzDE (VSMFuzDE), which realizes the characterization of abundant scale information, and is not limited by the signal length like the traditional multiscale processing. The experimental results for both simulated signals show that VSMFuzDE is more robust, more sensitive to dynamic changes in the chirp signal, and has more separability for noise signals; in addition, the proposed VSMFuzDE displays the best classification performance in both real-world signal experiments compared to the other four entropy metrics, the highest recognition rates of the five gear signals and four ship-radiated noises reached 99.2% and 100%, respectively, which achieves the accurate identification of two different categories of signals.

Indexed as

dispersion entropyfeature extractionfuzzy dispersion entropysignal analysisvariable-step multiscale fuzzy dispersion entropy

Identifiers

PMID37509944
PMCPMC10378684

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.