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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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Semi-supervised automatic seizure detection using personalized anomaly detecting variational autoencoder with

Sungmin You¹, Baek Hwan Cho², Young-Min Shon³

¹Department of Biomedical Engineering, Hanyang University, Seoul, South Korea.

Computer Methods and Programs in Biomedicine

|November 28, 2021

Summary

This study presents a personalized deep learning algorithm for detecting seizures using behind-the-ear electroencephalogram (EEG) signals. The novel approach significantly improves seizure detection accuracy and reduces false alarms for epilepsy patients.

Keywords:

Anomaly detection Behind-the-ear EEG Deep learning Epilepsy Seizure detection Variational autoencoder

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Published on: July 26, 2013

Area of Science:

Neurology
Biomedical Engineering
Artificial Intelligence

Background:

Epilepsy affects millions globally, with a significant portion experiencing uncontrolled seizures.
Sudden unexpected death is a risk for epilepsy patients, necessitating continuous seizure monitoring.
Existing machine learning methods struggle with subtle epileptic EEG changes and data imbalance.

Purpose of the Study:

To develop a personalized deep learning anomaly detection algorithm for seizure monitoring.
To utilize behind-the-ear electroencephalogram (EEG) signals for improved seizure detection.
To address the limitations of previous methods in identifying subtle epileptic patterns.

Main Methods:

Collected behind-the-ear EEG signals from 16 epilepsy patients.
Modified a variational autoencoder to learn normal EEG representations.
Developed a personalization method by calibrating anomaly scores in latent space.

Main Results:

The algorithm achieved 90.4% sensitivity and 0.83 false alarms/hour without calibration.
Personalized calibration improved sensitivity to 94.2% with only 0.29 false alarms/hour.
The personalized model successfully detected 49 out of 52 ictal events.

Conclusions:

A novel semi-supervised deep learning algorithm for seizure detection using behind-the-ear EEG was developed.
The algorithm leverages anomaly detection via variational autoencoders and personalization.
The proposed approach offers enhanced seizure detection with high sensitivity and low false alarm rates.