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

Seizures: Classification

Seizures: Classification

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:

You might also read

Related Articles

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

Sort by

Same author

Layer-specific facial soft-tissue thickness in 1174 Chinese adults: Implications for finite-element headforms and ergonomic design.

Ergonomics·2026

Same author

Independent and joint effects of ambient temperature and relative humidity on fetal distress: effect modification by air pollutants.

International journal of biometeorology·2026

Same author

POEMS syndrome with multiple ganglioside/paraneoplastic antibodies misdiagnosed as CIDP: a case report.

Frontiers in oncology·2026

Same author

Permeability prediction of 3D complex fracture networks in deep underground based on fractal analysis of trace maps.

Scientific reports·2026

Same author

Therapeutic leukapheresis with severe pertussis: evaluating efficacy, feasibility, and safety.

BMC pediatrics·2026

Same author

Assessing Executive Function in Patients With Temporal Lobe Epilepsy Through a Game-Based Assessment Task.

Behavioural neurology·2026

Search research articles

Related Experiment Video

Updated: Aug 28, 2025

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Published on: December 6, 2016

Automatic Recognition of High-Density Epileptic EEG Using Support Vector Machine and Gradient-Boosting Decision Tree.

Jiaxiu He¹, Li Yang¹, Ding Liu¹

¹Department of Neurology, The Third Xiangya Hospital of CSU, Tongzipo Street, Changsha 410013, China.

|September 23, 2022

Summary

Gradient-boosting decision tree (GBDT) outperforms Support Vector Machine (SVM) in recognizing epileptic electroencephalogram (EEG) signals. GBDT achieved higher accuracy and F1-score, demonstrating its effectiveness in machine learning for epilepsy diagnosis.

Keywords:

EEG GBDT SVM epilepsy machine learning

More Related Videos

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

Cortical Source Analysis of High-Density EEG Recordings in Children

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

Related Experiment Videos

Last Updated: Aug 28, 2025

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Published on: December 6, 2016

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

Cortical Source Analysis of High-Density EEG Recordings in Children

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

Area of Science:

Neurology
Artificial Intelligence
Biomedical Engineering

Background:

Epilepsy (Ep) is a chronic neurological disorder diagnosed via seizure history and electroencephalogram (EEG).
Machine learning (ML) models are increasingly applied for automated epileptic EEG recognition.
High-density EEG data acquisition is crucial for accurate diagnostic analysis.

Purpose of the Study:

To compare the classification performance of Support Vector Machine (SVM) and Gradient-Boosting Decision Tree (GBDT) algorithms.
To evaluate classifier efficacy using controlled EEG data sources and feature sets.
To identify the optimal machine learning model for epileptic EEG detection.

Main Methods:

Utilized high-density EEG data collected from Xiangya Third Hospital.
Extracted time-domain (EMD-processed), frequency-domain (PSD), and non-linear (Shannon entropy) features.
Implemented and compared SVM and GBDT classifiers for epileptic EEG recognition.

Main Results:

GBDT classifier achieved a superior accuracy of 90.00% and an F1-score of 93.40%.
SVM classifier yielded an accuracy of 72.00% and an F1-score of 82.28%.
GBDT demonstrated higher sensitivity (98.57%), precision (89.13%), and AUC (0.9119).

Conclusions:

Gradient-Boosting Decision Tree (GBDT) is more effective than Support Vector Machine (SVM) for classifying epileptic EEG.
Feature selection and classifier parameter control are key for optimizing ML model performance in epilepsy diagnosis.
This study highlights GBDT's potential in advancing automated epileptic EEG analysis.