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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: Oct 11, 2025

Long-term Continuous EEG Monitoring in Small Rodent Models of Human Disease Using the Epoch Wireless Transmitter System

Long-term Continuous EEG Monitoring in Small Rodent Models of Human Disease Using the Epoch Wireless Transmitter System

Published on: July 21, 2015

Big data analysis and artificial intelligence in epilepsy - common data model analysis and machine learning-based

Yoon Gi Chung¹, Yonghoon Jeon², Sooyoung Yoo³

¹Division of Pediatric Neurology, Department of Pediatrics, Seoul National University Bundang Hospital, Seongnam, Korea.

Clinical and Experimental Pediatrics

|November 30, 2021

Summary

Big data analysis and artificial intelligence (AI) are revolutionizing medicine, particularly in understanding epilepsy. This review covers AI applications in seizure detection and forecasting, offering insights for pediatricians.

Keywords:

Artificial intelligence Big data analysis Deep learning Epilepsy Machine learning

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Related Experiment Videos

Last Updated: Oct 11, 2025

Long-term Continuous EEG Monitoring in Small Rodent Models of Human Disease Using the Epoch Wireless Transmitter System

Long-term Continuous EEG Monitoring in Small Rodent Models of Human Disease Using the Epoch Wireless Transmitter System

Published on: July 21, 2015

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Published on: June 6, 2015

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Area of Science:

Medicine
Data Science
Computer Science

Background:

Medical data is rapidly increasing, driving interest in big data analysis and artificial intelligence (AI).
Advanced computing power supports the growth of AI and big data studies in healthcare.
Epilepsy research is a key area benefiting from these technological advancements.

Purpose of the Study:

To introduce epilepsy, big data, and AI in a medical context.
To review big data analysis methodologies, including the use of a common data model.
To examine the application of AI in epilepsy research, focusing on electroencephalography (EEG) analysis, seizure detection, and forecasting.

Main Methods:

Review of existing literature on big data analysis and AI in medicine.
Focus on common data models for big data analysis.
Analysis of AI applications in electroencephalography (EEG) epileptiform discharge detection, seizure detection, and forecasting.

Main Results:

AI is actively applied in detecting epileptiform discharges on EEG.
AI tools show promise in real-time seizure detection and prediction.
Big data analysis, using common data models, facilitates large-scale medical research.

Conclusions:

Big data and AI offer powerful tools for advancing epilepsy research and clinical practice.
Pediatricians can leverage these technologies with an understanding of their interpretation and application.
Collaboration between medical professionals and technical experts is crucial for successful implementation.