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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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Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Multi-view cross-subject seizure detection with information bottleneck attribution.

Yanna Zhao¹, Gaobo Zhang¹, Yongfeng Zhang¹

¹School of Information Science and Engineering, Shandong Normal University, Jinan 250358, People's Republic of China.

Journal of Neural Engineering

|June 29, 2022

Summary

This study introduces a novel multi-view seizure detection model for electroencephalography (EEG) data. The model effectively detects seizures across different subjects by learning seizure-specific features and enhancing interpretability with information bottleneck attribution.

Keywords:

EEG adversarial learning cross-subject information bottleneck attribution seizure detection

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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

Area of Science:

Biomedical Engineering
Neuroscience
Machine Learning

Background:

Within-subject seizure detection from electroencephalography (EEG) signals has advanced significantly.
The focus is shifting towards cross-subject seizure detection, but inter-patient variations pose a challenge.

Purpose of the Study:

To address the limitations of cross-subject seizure detection caused by inter-patient variability.
To propose a multi-view seizure detection model that enhances interpretability.

Main Methods:

Developed a multi-view cross-subject seizure detection model incorporating information bottleneck attribution (IBA).
Utilized adversarial deep learning to extract seizure-specific features from raw EEG data.
Integrated manually designed discriminative features with learned features for robust detection.

Main Results:

The proposed model demonstrated efficacy in cross-subject seizure detection on benchmark datasets.
Information Bottleneck Attribution (IBA) provided insights into the model's decision-making process, improving interpretability.
Extensive experiments validated the model's performance.

Conclusions:

The multi-view model effectively overcomes inter-patient variations in EEG seizure detection.
The integration of IBA enhances the transparency and trustworthiness of the seizure detection system.
This approach shows promise for real-world clinical applications of automated seizure detection.