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

Entropy

Entropy

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...

Entropy

Entropy

The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
When an ideal gas expands isothermally, the disorder in the gas increases. From the molecular perspective, the gas molecules have more volume to move around in.
Consider an infinitesimal step in the expansion, which...

Sampling Theorem

Sampling Theorem

In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.

Sampling Distribution

Sampling Distribution

Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...

Random Error

Random Error

Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...

Entropy and the Second Law of Thermodynamics

Entropy and the Second Law of Thermodynamics

The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...

You might also read

Related Articles

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

Sort by

Same author

Instability analysis of perturbed gait in dyads: holding a stick together provides effective assistance and is accompanied by information exchanges.

Journal of the Royal Society, Interface·2026

Same author

Universal and Cross-Cultural Variations in Audio-Motor Synchronization Between French and Indian Participants.

Annals of the New York Academy of Sciences·2026

Same author

Cognitive neurodegenerative diseases at a crossroads: implementing recent advances while continuing the search for effective therapeutic strategies.

Current opinion in neurology·2026

Same author

Progression of fiber bundle damage in amnestic Alzheimer's disease and LATE: a 2-year fixel-based study.

GeroScience·2026

Same author

Distinct Tau PET Dynamics in Early vs. Late Age-of-Onset Alzheimer's disease.

medRxiv : the preprint server for health sciences·2026

Same author

Biomarkers.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025

Same journal

Topological dependence of viral mutation spread in complex host-interaction networks.

Chaos (Woodbury, N.Y.)·2026

Same journal

Multifractal signatures of Hamiltonian chaos in Hyperion's rotational dynamics.

Chaos (Woodbury, N.Y.)·2026

Same journal

Exploring mechanisms for reversal of flow in tunicate hearts.

Chaos (Woodbury, N.Y.)·2026

Same journal

State estimation in spatiotemporal chaos via low-rank StatFEM.

Chaos (Woodbury, N.Y.)·2026

Same journal

Universal response functions in driven dissipative tunneling dynamics.

Chaos (Woodbury, N.Y.)·2026

Same journal

A network-based approach to characterize the dynamics of the coupling field of thermoacoustic oscillators in annular geometry.

Chaos (Woodbury, N.Y.)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 24, 2026

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

Influence of noise on the sample entropy algorithm.

Sofiane Ramdani¹, Frédéric Bouchara, Julien Lagarde

¹EA 2991 Efficience et Déficience Motrices, Université de Montpellier I, Montpellier 34090, France. sofiane.ramdani@univ-montp1.fr

Chaos (Woodbury, N.Y.)

|April 2, 2009

Summary

This summary is machine-generated.

Sample Entropy (SampEn) effectively detects time series nonlinearity even with added noise. This robust algorithm identifies complex patterns in noisy data, proving reliable for data analysis.

Related Experiment Videos

Last Updated: Jun 24, 2026

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

Area of Science:

Complexity Science
Nonlinear Dynamics
Time Series Analysis

Background:

Sample Entropy (SampEn) is a key algorithm for time series analysis.
Understanding its performance under noisy conditions is crucial for reliable data interpretation.

Purpose of the Study:

To evaluate the impact of static additive noise on Sample Entropy's ability to detect nonlinearity.
To assess the robustness of SampEn using surrogate data tests.

Main Methods:

Simulated time series from discrete and continuous chaotic and nonchaotic systems were generated.
Static additive noise (Gaussian and uniform) was introduced to the time series.
Surrogate data tests were employed to empirically assess SampEn's performance.

Main Results:

Sample Entropy demonstrated a robust ability to detect nonlinearity.
This capability was maintained despite increasing levels of both Gaussian and uniform noise.
The algorithm's performance was consistent across different types of systems.

Conclusions:

Sample Entropy is a reliable metric for identifying nonlinearity in time series data, even when corrupted by observational noise.
The findings support the use of SampEn in real-world applications where data is often noisy.