Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

Response to "Comment on 'Modified multiscale fuzzy entropy: A robust method for short-term physiologic signals"' [Chaos 30, 083135 (2020)].

Chaos (Woodbury, N.Y.)·2021

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 10, 2025

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

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

Published on: June 27, 2013

Modified multiscale fuzzy entropy: A robust method for short-term physiologic signals.

Airton Monte Serrat Borin¹, Luiz Eduardo Virgilio Silva², Luiz Otavio Murta³

¹Federal Institute for Education Science and Technology of Triângulo Mineiro, IFTM, Uberaba, MG 38064-790, Brazil.

Chaos (Woodbury, N.Y.)

|September 3, 2020

Summary

Modified multiscale fuzzy entropy (MMFE) offers a robust solution for analyzing short physiological signals, outperforming modified multiscale entropy (MMSE). MMFE demonstrates reliability comparable to multiscale entropy (MSE) even with limited data points.

More Related Videos

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

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

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

Published on: May 10, 2017

Related Experiment Videos

Last Updated: Dec 10, 2025

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

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

Published on: June 27, 2013

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

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

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

Published on: May 10, 2017

Area of Science:

Physiological signal analysis
Complex systems science
Biomedical engineering

Background:

Multiscale entropy (MSE) is a key method for analyzing complex physiological signals.
Existing variants like modified multiscale entropy (MMSE) have limitations with short time series due to hard similarity criteria.
There is a need for improved entropy analysis methods for short physiological data.

Purpose of the Study:

To introduce Modified Multiscale Fuzzy Entropy (MMFE) as an advancement over MMSE.
To evaluate the robustness of MMFE and MMSE for short physiological time series.
To compare MMFE and MMSE performance against classical MSE using varying signal lengths.

Main Methods:

Development of the Modified Multiscale Fuzzy Entropy (MMFE) method, integrating fuzzy entropy concepts with MMSE.
Evaluation of MMSE and MMFE robustness using segmented stochastic noise and heart rate variability (HRV) series.
Comparison of MMFE and MMSE results with classical MSE on full signals.

Main Results:

MMFE demonstrates significantly higher robustness than MMSE for short physiological time series.
MMFE performance closely approximates classical MSE results for time series as short as 400 samples.
An exponential relationship between the MMFE fuzzy parameter and signal size was identified.

Conclusions:

MMFE is a more robust and reliable method for analyzing short physiological time series compared to MMSE.
The identified exponential relationship provides a guideline for selecting optimal MMFE parameters based on signal length.
MMFE offers a promising tool for complex physiological signal analysis when data is limited.