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

Binomial Probability Distribution01:15

Binomial Probability Distribution

16.2K
A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
16.2K
2D NMR: Overview of Homonuclear Correlation Techniques01:16

2D NMR: Overview of Homonuclear Correlation Techniques

754
Homonuclear correlation spectroscopy (COSY) is a powerful technique used in Nuclear Magnetic Resonance (NMR) spectroscopy to study the correlations between nuclei of the same type within a molecule. It provides information about scalar couplings between adjacent nuclei, which helps determine connectivity and structural information. There are several COSY variants, each with its unique strengths and experimental parameters.
COSY90 is the standard two-dimensional (2D) COSY experiment that...
754
Expected Value01:15

Expected Value

8.0K
The expected value is known as the "long-term" average or mean. This means that over the long term of experimenting over and over, you would expect this average. The expected average is represented by the symbol μ. It is calculated as follows:
8.0K
2D NMR: Overview of Heteronuclear Correlation Techniques01:18

2D NMR: Overview of Heteronuclear Correlation Techniques

849
Heteronuclear correlation spectroscopy is an analytical technique that investigates the coupling between different types of nuclei, often a proton and an X-nucleus, such as carbon-13 or nitrogen-15. This method is commonly used in nuclear magnetic resonance (NMR) spectroscopy to gain insights into complex chemical compounds' structural and compositional aspects. A typical heteronuclear correlation spectrum displays X-nucleus chemical shifts on one axis and a proton spectrum on the other...
849

You might also read

Related Articles

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

Sort by
Same author

Sustainable diets and functional foods for the prevention of cardio-metabolic diseases and sustainable development goals of the UNO. An international consensus of scientific statement of the international college of nutrition and 28th world congress on clinical nutrition, Bogor, Indonesia.

BMC cardiovascular disorders·2026
Same author

Histomorphometric Evaluation of Subchronic and Chronic Effects of Novel Experimental Calcium Aluminate- and Calcium Silicate-Based Dental Cement Materials on Rat Liver, Kidney, Brain, and Spleen Tissues.

Journal of functional biomaterials·2026
Same author

Associations of Poincaré Plot-Derived Parameters with Heart Rate Variability and Autonomic Reflex Testing in a Real-World Clinical Population.

Diagnostics (Basel, Switzerland)·2026
Same author

Assessment of Autonomic Nervous System Function in Patients with Aortic Stenosis and Diabetes Mellitus.

Diagnostics (Basel, Switzerland)·2026
Same author

Short-Term Heart Rate Variability Dynamics and Mortality Risk After Acute Coronary Syndrome.

Diagnostics (Basel, Switzerland)·2026
Same author

Autonomic Nervous System Dysfunction in Diabetic Patients After Myocardial Infarction: Prognostic Role of the Valsalva Maneuver.

Medicina (Kaunas, Lithuania)·2026

Related Experiment Video

Updated: Mar 10, 2026

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

34.5K

Binarized cross-approximate entropy in crowdsensing environment.

Tamara Skoric1, Omer Mohamoud1, Branislav Milovanovic2

  • 1University of Novi Sad Faculty of Technical Sciences, Centre of excellence CEVAS, Trg Dositeja Obradovica 6, 21000 Novi Sad, Serbia.

Computers in Biology and Medicine
|December 13, 2016
PubMed
Summary

A new method called binarised cross-approximate entropy ((X)BinEn) offers efficient cardiovascular signal processing for mobile health applications. This computationally inexpensive technique is suitable for wearable sensors with limited resources.

Keywords:
Cardiovascular signalsConditional entropyCross-approximate entropyCrowdsensingDifferential coding

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K

Related Experiment Videos

Last Updated: Mar 10, 2026

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

34.5K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Mobile Health

Background:

  • Personalized health monitoring utilizes mobile crowdsensing, where sensors collect data for individual or collective benefit.
  • Limited transmission and processing resources in mobile health applications necessitate local data analysis.
  • Traditional analytical tools often require stationary, artifact-free data, posing challenges for mobile crowdsensing.

Purpose of the Study:

  • To introduce a computationally efficient binarized cross-approximate entropy ((X)BinEn) method.
  • To enable unsupervised cardiovascular signal processing in resource-constrained environments.
  • To adapt analytical tools for mobile, battery-operated sensing devices.

Main Methods:

  • The (X)BinEn method is derived from cross-approximate entropy ((X)ApEn).
  • It processes binary, differentially encoded data series using m-sized vectors and Hamming distance.
  • The procedure was validated on rat models subjected to shaker and restraint stress.

Main Results:

  • Reduced processing operations compared to existing methods.
  • (X)BinEn effectively captures entropy changes, similar to (X)ApEn.
  • While coding coarseness slightly reduces sensitivity, it mitigates parameter inconsistency and binary bias.

Conclusions:

  • The (X)BinEn method is suitable for both auto-entropy (single time series) and cross-entropy (dual time series) analyses.
  • Its low computational demands make it ideal for mobile, self-attached sensing devices with limited power and processor capabilities.
  • This approach facilitates advanced signal processing in wearable health technology.