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Related Concept Videos

Seizures: Classification01:13

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: Overview01:24

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: Jun 26, 2026

Robotic-Guided Stereoelectroencephalography for Invasive Epilepsy Monitoring
11:28

Robotic-Guided Stereoelectroencephalography for Invasive Epilepsy Monitoring

Published on: June 13, 2025

Vision-Based Artificial Intelligence Technologies for Epilepsy Monitoring: Scoping Review and Taxonomy Development

Mirijana Irnich1, Jonas Hammer1, Aleksandra Flok1

  • 1Department of Management Accounting and Information Systems, University Osnabrück, Katharinenstraße 3, Osnabrück, 49074, Germany, 49 5419694926.

Journal of Medical Internet Research
|June 24, 2026
PubMed
Summary
This summary is machine-generated.

A new taxonomy classifies artificial intelligence (AI) for vision-based epilepsy monitoring, revealing gaps in prediction and real-time feedback. This framework aids in evaluating and deploying AI systems in healthcare settings.

Keywords:
artificial intelligenceclassification systemcomputer visiondigital healtheHealthepilepsyhealth carehealth information systemsmonitoringremote patient monitoring systemstaxonomy

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Stereo-Electro-Encephalo-Graphy (SEEG) With Robotic Assistance in the Presurgical Evaluation of Medical Refractory Epilepsy: A Technical Note
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Stereo-Electro-Encephalo-Graphy (SEEG) With Robotic Assistance in the Presurgical Evaluation of Medical Refractory Epilepsy: A Technical Note

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A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

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Area of Science:

  • Medical Informatics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) for vision-based epilepsy monitoring is rapidly advancing.
  • Existing research lacks a comprehensive framework to classify these technologies.

Purpose of the Study:

  • Develop and validate a taxonomy for AI technologies in vision-based epilepsy monitoring.
  • Characterize visual AI approaches in epilepsy care.

Main Methods:

  • Conducted a scoping review, market analysis, and expert evaluation using a Delphi technique.
  • Included 40 studies from 2013 onwards on AI/ML-based seizure monitoring or prediction using visual data.
  • Developed a taxonomy with 23 dimensions and 102 characteristics.

Main Results:

  • The final taxonomy includes 23 dimensions and 102 characteristics.
  • Identified evidence gaps in settings, evaluation maturity, and reporting practices.
  • Deep learning detection methods are common, but performance reporting and patient-facing functionalities are limited.

Conclusions:

  • Vision-based AI for epilepsy monitoring shows a gap between technical feasibility and deployment.
  • The developed taxonomy offers a shared structure for system-level characterization and comparison.
  • The taxonomy can support benchmarking, procurement, and translation into clinical and home settings.