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

Seizures: Classification01:13

Seizures: Classification

1.2K
Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:
1.2K
Epilepsy and Seizures: Overview01:24

Epilepsy and Seizures: Overview

1.0K
Epilepsy is a chronic neurological disease marked by recurrent, unpredictable seizures. These seizures are caused by abnormal electrical discharges in the brain, leading to behavior, sensation, or consciousness alterations. They can also cause transient impairment of awareness, interfering with daily activities.
Various factors can trigger epilepsy, including genetic factors, brain damage, metabolic causes, and unknown etiology. Diagnosis of epilepsy involves electroencephalography (EEG), which...
1.0K
Classification of Signals01:30

Classification of Signals

1.2K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.2K

You might also read

Related Articles

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

Sort by
Same author

Dimension-controlled formation of crease patterns on soft solids.

Soft matter·2016
Same author

Modeling Day-to-day Flow Dynamics on Degradable Transport Network.

PloS one·2016
Same author

Tetramethylpyrazine Protects Against Glucocorticoid-Induced Apoptosis by Promoting Autophagy in Mesenchymal Stem Cells and Improves Bone Mass in Glucocorticoid-Induced Osteoporosis Rats.

Stem cells and development·2016
Same author

Corrigendum: Lithium-ion-based solid electrolyte tuning of the carrier density in graphene.

Scientific reports·2016
Same author

PTEN/PI3K/AKT protein expression is related to clinicopathological features and prognosis in breast cancer with axillary lymph node metastases.

Human pathology·2016
Same author

Comparing the Diagnostic Accuracy of RTE and SWE in Differentiating Malignant Thyroid Nodules from Benign Ones: a Meta-Analysis.

Cellular physiology and biochemistry : international journal of experimental cellular physiology, biochemistry, and pharmacology·2016

Related Experiment Video

Updated: Dec 18, 2025

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings
08:20

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Published on: June 6, 2015

15.7K

Deep Convolutional Neural Network-Based Epileptic Electroencephalogram (EEG) Signal Classification.

Yunyuan Gao1,2, Bo Gao1, Qiang Chen1

  • 1School of Automation, Intelligent Control and Robotics Institute, College of Automation, Hangzhou Dianzi University, Hangzhou, China.

Frontiers in Neurology
|June 13, 2020
PubMed
Summary

A novel deep learning method accurately classifies epileptic states from electroencephalogram (EEG) signals. This approach achieves over 90% accuracy, improving epilepsy diagnosis efficiency.

Keywords:
EEGdeep convolutional neural networkselectroencephalogramepileptic EEG signal classificationpower spectrum density energy diagram

More Related Videos

Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy
10:23

Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy

Published on: June 23, 2023

2.5K
Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.8K

Related Experiment Videos

Last Updated: Dec 18, 2025

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings
08:20

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Published on: June 6, 2015

15.7K
Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy
10:23

Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy

Published on: June 23, 2023

2.5K
Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.8K

Area of Science:

  • Neurology
  • Artificial Intelligence
  • Signal Processing

Background:

  • Electroencephalogram (EEG) signals are crucial for understanding brain electrical activity and diagnosing epilepsy.
  • Accurate classification of epileptic states is a significant challenge in epilepsy diagnosis.

Purpose of the Study:

  • To propose a new deep learning-based classification methodology for epileptic EEG signals.
  • To enhance the accuracy and efficiency of classifying different epileptic states.

Main Methods:

  • Developed an Epileptic EEG Signal Classification (EESC) methodology.
  • Transformed EEG signals into power spectrum density energy diagrams (PSDEDs).
  • Utilized deep convolutional neural networks (DCNNs) and transfer learning for automated feature extraction from PSDEDs.

Main Results:

  • The EESC methodology successfully classifies four epileptic states: interictal, preictal (30 min), preictal (10 min), and seizure.
  • Achieved an average classification accuracy exceeding 90% on the CHB-MIT epileptic EEG dataset.
  • Demonstrated superior accuracy and efficiency compared to existing epilepsy classification methods.

Conclusions:

  • The proposed EESC methodology offers a highly accurate and efficient approach for epileptic EEG signal classification.
  • Deep learning techniques, particularly DCNNs and transfer learning applied to PSDEDs, show great promise for improving epilepsy diagnosis.