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Epilepsy and Seizures: Overview

Epilepsy and Seizures: Overview

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...

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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

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Electroencephalography-Based Machine Learning Models for Predicting Ketogenic Diet Outcomes in Pediatric

Pi-Lien Hung¹, Jen-Ping Chen², Tzu-Ping Lin³

¹Division of Pediatric Neurology, Department of Pediatrics, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan; Rare Childhood Neurologic Disease Center, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan.

Pediatric Neurology

|March 13, 2026

Summary

Machine learning models can predict ketogenic diet therapy (KDT) effectiveness for drug-resistant epilepsy (DRE) patients using electroencephalography. This aids in tailoring KDT treatment for better seizure control.

Keywords:

Drug-resistant epilepsy EEG power spectrum Functional connectivity Ketogenic diet therapy Machine learning Outcome prediction

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Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy

Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy

Published on: January 29, 2018

Area of Science:

Neuroscience
Computational Biology
Medical Informatics

Background:

Ketogenic diet therapy (KDT) is a recognized treatment for drug-resistant epilepsy (DRE).
Predictive methods for KDT effectiveness in DRE patients are currently underdeveloped.
Electroencephalography (EEG) data offers potential for predicting KDT response.

Purpose of the Study:

To evaluate machine learning (ML) models for predicting KDT response in DRE patients.
To identify the most effective ML algorithms and EEG features for outcome prediction.
To assess the utility of EEG-based ML tools in guiding clinical KDT application.

Main Methods:

Ninety DRE patients undergoing KDT were analyzed.
Leave-one-out cross-validation was employed for model evaluation.
Nineteen ML classifiers were trained on EEG features including absolute/relative power and functional connectivity (e.g., PLI).

Main Results:

KDT significantly reduced seizure frequency at 3 and 6 months.
A Coarse Tree classifier using absolute power was optimal at 3 months (Recall=0.933).
A Gaussian Naive Bayes classifier using weighted PLI + relative power was optimal at 6 months (Precision=0.854).

Conclusions:

Specific ML models demonstrate efficacy in predicting KDT outcomes for DRE.
EEG-derived features are valuable predictors of KDT response.
ML-powered EEG analysis holds promise for personalized KDT treatment strategies.