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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...

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 1, 2026

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

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

Published on: June 23, 2023

Real-time EEG-based epileptic seizure prediction using artificial intelligence: A systematic review.

Zikang Song¹, Kim Arrowsmith², Dion Henare¹

¹Auckland University of Technology, Auckland, New Zealand.

Artificial Intelligence in Medicine

|June 29, 2026

Summary

AI models show promise for epilepsy seizure prediction, but real-world clinical use is limited by a lack of standardized evaluation and generalizability. Further research needs prospective validation and deployment metrics for reliable patient outcomes.

Keywords:

Clinical deployment Deep learning Electroencephalography (EEG)Epilepsy Machine learning Real-time seizure prediction

Related Experiment Videos

Last Updated: Jul 1, 2026

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

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

Published on: June 23, 2023

Area of Science:

Neurology
Artificial Intelligence
Biomedical Engineering

Background:

Epilepsy impacts 50 million globally, with AI seizure prediction facing a significant bench-to-bedside gap.
Current AI translation is hindered by limited generalizability and lack of standardized real-world deployment evaluation.

Purpose of the Study:

Evaluate the operational readiness of AI models for real-time epileptic seizure prediction.
Examine AI architectural performance, real-time responsiveness, interpretability, and multimodal integration for clinical deployment.

Main Methods:

Systematic review adhering to PRISMA 2020 guidelines, searching major scientific databases (PubMed, Scopus, IEEE Xplore, ScienceDirect) from Jan 2017 to May 2025.
Included 23 studies on EEG-based real-time prediction, assessing risk of bias using ROBINS-I, and standardizing false-alarm rate (FAR) to events per hour.

Main Results:

Deep learning models, particularly CNNs and hybrids, demonstrated high sensitivity (up to 99.81%).
Over-reliance on the CHB-MIT dataset (74% of studies) and predominantly patient-specific validation (PS) were noted.
Limited reporting on prediction horizon, end-to-end latency, energy metrics, and lack of patient-independent (PI) or external validation (EXT) were critical findings.

Conclusions:

High accuracy in AI seizure prediction is insufficient for clinical translation due to a lack of prospective validation and standardized deployment metrics.
Proposed operational framework emphasizes standardized evaluation (PS/PI/EXT), defined latency budgets, and prospective FAR reporting for clinical viability.