Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Downsampling01:20

Downsampling

460
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
460
Upsampling01:22

Upsampling

469
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
469
Ordinal Level of Measurement00:55

Ordinal Level of Measurement

29.3K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
29.3K
Entropy02:39

Entropy

33.4K
Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
33.4K
Ranks01:02

Ranks

343
Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
343
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

12.1K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
12.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Ordinal Pattern-Based Mode Decomposition for Phonocardiogram Signal Analysis.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Chemical Optimization of Cerebral Embolectomy 2 (CHOICE 2) trial: study protocol.

Stroke and vascular neurology·2025
Same author

Legendre Polynomial Fitting-Based Permutation Entropy Offers New Insights into the Influence of Fatigue on Surface Electromyography (sEMG) Signal Complexity.

Entropy (Basel, Switzerland)·2024
Same author

Comparison of the Accuracy of Ground Reaction Force Component Estimation between Supervised Machine Learning and Deep Learning Methods Using Pressure Insoles.

Sensors (Basel, Switzerland)·2024
Same author

Impact of PCA Pre-Normalization Methods on Ground Reaction Force Estimation Accuracy.

Sensors (Basel, Switzerland)·2024
Same author

On the Genuine Relevance of the Data-Driven Signal Decomposition-Based Multiscale Permutation Entropy.

Entropy (Basel, Switzerland)·2023

Related Experiment Video

Updated: Nov 23, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.1K

Improvement of Statistical Performance of Ordinal Multiscale Entropy Techniques Using Refined Composite Downsampling

Antonio Dávalos1, Meryem Jabloun1, Philippe Ravier1

  • 1Laboratoire Pluridisciplinaire de Recherche en Ingénierie des Systèmes, Mécanique, Énergétique (PRISME), University of Orléans, 45100 Orléans, France.

Entropy (Basel, Switzerland)
|December 31, 2020
PubMed
Summary

Refined Composite Downsampling Permutation Entropy (rcDPE) offers improved time series analysis. This new method provides more precise entropy estimation than previous techniques, showing minimal bias and variance for better signal classification.

Keywords:
bearing faultsdownsamplingmultiscale permutation entropy

More Related Videos

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities
08:08

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Published on: May 10, 2017

15.0K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.8K

Related Experiment Videos

Last Updated: Nov 23, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.1K
Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities
08:08

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Published on: May 10, 2017

15.0K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.8K

Area of Science:

  • Complexity Science
  • Time Series Analysis
  • Information Theory

Background:

  • Multiscale Permutation Entropy (MPE) is a key method for quantifying time series information content.
  • Existing refinements like Composite MPE (cMPE) and Refined Composite MPE (rcMPE) enhance precision but lack theoretical grounding.
  • There is a need for a robust theoretical framework and improved methods for entropy estimation.

Purpose of the Study:

  • To develop the statistical theory underpinning cMPE and rcMPE.
  • To introduce Refined Composite Downsampling Permutation Entropy (rcDPE) as a novel method for enhanced entropy estimation precision.
  • To compare the performance of MPE, cMPE, rcMPE, and rcDPE in signal analysis applications.

Main Methods:

  • Theoretical development of the statistical underpinnings for cMPE and rcMPE.
  • Algorithmic design and implementation of the proposed Refined Composite Downsampling Permutation Entropy (rcDPE).
  • Application of MPE, cMPE, rcMPE, and rcDPE to classify faults in bearing vibration signals.

Main Results:

  • While cMPE and rcMPE showed improvements over MPE on uncorrelated noise, their performance exceeded predictions due to algorithm-inherent redundancies.
  • The proposed rcDPE method aligned with theoretical predictions and significantly outperformed MPE, cMPE, and rcMPE.
  • rcDPE demonstrated the smallest bias and variance in entropy estimation, leading to enhanced discrimination between faulty and non-faulty bearing vibration signals when combined with appropriate filtering.

Conclusions:

  • The statistical theory for cMPE and rcMPE has been established.
  • rcDPE offers superior precision and reduced bias/variance in entropy estimation compared to existing multiscale permutation entropy methods.
  • rcDPE shows significant potential for improving fault diagnosis in mechanical systems by enhancing the analysis of vibration signals.