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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:

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Related Experiment Video

Updated: Jul 18, 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

Optimizing detection and deep learning-based classification of pathological high-frequency oscillations in epilepsy.

Tonmoy Monsoor¹, Yipeng Zhang¹, Atsuro Daida²

¹Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA.

Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology

|August 21, 2023

Summary

Improving high-frequency oscillation (HFO) detection methods, including deep learning, enhances the prediction of seizure outcomes after epilepsy surgery. This aids in identifying optimal surgical targets for better patient results.

Keywords:

Deep learning HFO MNI Machine learning STE

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Last Updated: Jul 18, 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

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

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

Published on: December 18, 2016

Area of Science:

Neuroscience
Epilepsy Research
Signal Processing

Background:

Accurate detection of pathological high-frequency oscillations (HFOs) is crucial for improving surgical outcomes in epilepsy.
Current automated HFO detection methods have limitations in sensitivity and specificity.
Deep learning (DL) offers potential for refining HFO analysis.

Purpose of the Study:

To explore and compare sensitive detection methods for pathological HFOs.
To evaluate the utility of different HFO detectors and DL-based purification for predicting seizure outcomes.
To determine the optimal HFO detection strategy for epilepsy surgery planning.

Main Methods:

Analysis of interictal HFOs (80-500 Hz) in 15 children with drug-resistant focal epilepsy using chronic intracranial EEG.
Comparison of short-term energy (STE) and Montreal Neurological Institute (MNI) detectors for HFO identification.
Application of a deep learning (DL) model for pathological HFO purification and correlation with postoperative seizure outcomes.

Main Results:

The MNI detector identified more pathological HFOs than STE, though some were exclusively detected by STE.
HFOs detected by both MNI and STE showed the highest spike association.
A 'Union' detector, combining MNI and STE, along with DL purification, best predicted postoperative seizure outcomes based on HFO-resection ratios.

Conclusions:

Standard automated HFO detectors exhibit varying signal and morphological characteristics.
Deep learning-based classification effectively purifies pathological HFOs.
Enhanced HFO detection and classification methods are vital for improving their predictive value in epilepsy surgery.