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

You might also read

Related Articles

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

Sort by
Same author

Geographical distribution and genotypic diversity of Theileria orientalis with emphasis on Indian isolates.

Scientific reports·2026
Same author

Dendritic Cell α-Ketoglutarate Regulates Tfh Polarization in Allergy.

Allergy·2026
Same author

Retraction Note: COVID-19 Detection using adopted convolutional neural networks and high-performance computing.

Multimedia tools and applications·2026
Same author

A hybrid quantum-classical framework for MRI-based deep brain tumor segmentation and classification.

Scientific reports·2026
Same author

Dual inhibition strategy against EGFR utilizing quercetin and 5-fluorouracil: A computational analysis for oral cancer treatment.

Computational biology and chemistry·2026
Same author

Wax-printing-free fabrication of paper-supported 3D cancer cell culture.

Analytical methods : advancing methods and applications·2026

Related Experiment Video

Updated: Jul 20, 2025

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.2K

Analyzing physical activity impact based on ubiquitous nonlinear dynamics and electroencephalography data.

Prashant Kumar Shukla1, Priti Maheshwary2, Shakti Kundu3

  • 1Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine
|August 7, 2023
PubMed
Summary

This study uses nonlinear dynamics theory to analyze electroencephalogram (EEG) signals, developing an algorithm to accurately distinguish between sober and intoxicated states for alcoholism diagnosis and monitoring.

Keywords:
ApEnEEGFEKSEPESVMSampEnTSWE

More Related Videos

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.4K
A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.0K

Related Experiment Videos

Last Updated: Jul 20, 2025

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.2K
Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.4K
A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.0K

Area of Science:

  • Neuroscience
  • Signal Processing
  • Machine Learning

Background:

  • Nonlinear dynamic systems theory offers tools for analyzing complex physiological signals.
  • This approach is increasingly applied to understand the evolution of physiological data.

Purpose of the Study:

  • To apply nonlinear dynamics to electroencephalogram (EEG) signals for understanding alcoholic mental states.
  • To develop an algorithm for automatic classification of sober versus intoxicated EEG signals.

Main Methods:

  • Extracted entropy-based features (ApEn, SampEn, Shannon, Renyi, PE, TS, FE, WE, KSE) from EEG signals.
  • Employed T-test, Wilcoxon, and Bhattacharyya ranking methods to select relevant features.
  • Trained Support Vector Machine (SVM) classifiers with selected features, optimizing with a radial basis function kernel.

Main Results:

  • The Bhattacharyya ranking method combined with SVM achieved high classification performance.
  • Achieved 95.89% classification accuracy, 94.43% sensitivity, and 96.67% specificity.
  • The SVM classifier with a radial basis function kernel demonstrated optimal results.

Conclusions:

  • The developed model accurately distinguishes between sober and intoxicated EEG signals.
  • This provides a cost-effective decision-support tool for diagnosing alcoholism.
  • It can also monitor intervention effectiveness in rehabilitation centers.