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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:

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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Related Experiment Video

Updated: Feb 16, 2026

Use of a Wireless Video-EEG System to Monitor Epileptiform Discharges Following Lateral Fluid-Percussion Induced Traumatic Brain Injury

Use of a Wireless Video-EEG System to Monitor Epileptiform Discharges Following Lateral Fluid-Percussion Induced Traumatic Brain Injury

Published on: June 21, 2019

Epileptic Seizure Prediction Using Big Data and Deep Learning: Toward a Mobile System.

Isabell Kiral-Kornek¹, Subhrajit Roy¹, Ewan Nurse²

¹IBM Research - Australia, 204 Lygon Street, 3053 Carlton, VIC, Australia.

|December 22, 2017

Summary

This study developed an accurate, automated seizure prediction system using deep learning and neuromorphic hardware. The wearable system offers patient-specific seizure warnings with low power consumption.

Keywords:

Artificial intelligence Deep neural networks Epilepsy Mobile medical devices Precision medicine Seizure prediction

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Inducing Post-Traumatic Epilepsy in a Mouse Model of Repetitive Diffuse Traumatic Brain Injury

Inducing Post-Traumatic Epilepsy in a Mouse Model of Repetitive Diffuse Traumatic Brain Injury

Published on: February 10, 2020

Area of Science:

Neuroscience
Biomedical Engineering
Artificial Intelligence

Background:

Epilepsy affects millions, necessitating improved seizure management strategies.
Current seizure prediction methods lack accuracy, automation, and patient-specific tuning.
Wearable, real-time seizure prediction systems can enhance patient independence and enable timely intervention.

Purpose of the Study:

To develop and validate a proof-of-concept for an accurate, automated, patient-specific, and tunable seizure prediction system.
To demonstrate the feasibility of deploying this system on an ultra-low power neuromorphic chip for autonomous operation.
To improve seizure management for epilepsy patients through advanced technology.

Main Methods:

Intracranial electroencephalography (iEEG) data from ten epilepsy patients were analyzed.
A deep learning classifier was trained to differentiate preictal and interictal brain signals.
The system's performance was evaluated in a pseudoprospective study and benchmarked against random prediction.
Feasibility of deployment on a neuromorphic chip for wearable applications was demonstrated.

Main Results:

The seizure prediction system achieved a mean sensitivity of 69% and a mean time in warning of 27%.
Performance significantly surpassed a random predictor by 42% across all patients.
The system demonstrated successful tuning for prioritizing sensitivity or time in warning.

Conclusions:

Deep learning combined with neuromorphic hardware forms the basis for a reliable, wearable seizure warning system.
The system offers real-time, always-on, patient-specific seizure prediction with low power requirements.
This technology holds promise for enhancing the quality of life for individuals with epilepsy.