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 Experiment Videos

Approximate entropy-based epileptic EEG detection using artificial neural networks.

Vairavan Srinivasan1, Chikkannan Eswaran, Natarajan Sriraam

  • 1Institute of Advanced Biomedical Techniques, G. D. Annunzio University, 66100 Chieti, Italy. v.srinivasan@ieee.org

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society
|May 25, 2007
PubMed
Summary
This summary is machine-generated.

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

Application of a Low-Cost mHealth Solution for the Remote Monitoring of Patients With Epilepsy: Algorithm Development and Validation.

JMIR neurotechnology·2025
Same author

Data analysis strategies for the Accelerating Medicines Partnership® Schizophrenia Program.

Schizophrenia (Heidelberg, Germany)·2025
Same author

Ultrasound imaging based recognition of prenatal anomalies: a systematic clinical engineering review.

Progress in biomedical engineering (Bristol, England)·2024
Same author

Signal Processing Framework for the Detection of Ventricular Ectopic Beat Episodes.

Journal of medical signals and sensors·2023
Same author

Review of the Requirements for a Wearable, Low-Cost External Cardiac Loop Recorder with WBAN in Resource-Constrained Settings.

Critical reviews in biomedical engineering·2023
Same author

A Computer-Aided Markov Random Field Segmentation Algorithm for Assessing Fetal Ventricular Chambers.

Critical reviews in biomedical engineering·2023

This study introduces a novel automated system for detecting epilepsy using neural networks and approximate entropy (ApEn). The system achieves up to 100% accuracy in identifying epileptic seizures from electroencephalogram (EEG) data.

Area of Science:

  • Biomedical Engineering
  • Computational Neuroscience
  • Medical Diagnostics

Background:

  • Epilepsy diagnosis relies heavily on electroencephalogram (EEG) analysis, which is time-consuming.
  • Automated systems are needed to expedite the detection of epileptic activity in lengthy EEG recordings.
  • Traditional EEG analysis methods are often tedious and require expert interpretation.

Purpose of the Study:

  • To propose a neural-network-based automated system for epileptic EEG detection.
  • To utilize approximate entropy (ApEn) as a key input feature for epilepsy detection.
  • To evaluate the efficacy of Elman and probabilistic neural networks for this task.

Main Methods:

  • Developed an automated system using neural networks for epileptic EEG detection.

Related Experiment Videos

  • Employed approximate entropy (ApEn) to measure signal predictability, noting its drop during seizures.
  • Implemented and compared Elman and probabilistic neural networks.

    • Main Results:

    • Approximate entropy (ApEn) was used for the first time with neural networks for epilepsy detection.
    • The proposed system demonstrated high accuracy in detecting epileptic activity.
    • Achieved overall accuracy values as high as 100%.

    Conclusions:

    • The proposed neural-network-based system effectively detects epilepsy using approximate entropy.
    • This automated approach offers a promising solution for efficient and accurate epileptic seizure detection.
    • The system's high accuracy highlights the potential of ApEn as a feature for neurological disorder analysis.