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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...

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Wearable Epileptic Seizure Prediction System Based on Machine Learning Techniques Using ECG, PPG and EEG Signals.

David Zambrana-Vinaroz¹, Jose Maria Vicente-Samper¹, Juliana Manrique-Cordoba¹

¹Neuroengineering Biomedical Research Group, Miguel Hernández University of Elche, 03202 Elche, Spain.

Sensors (Basel, Switzerland)

|December 11, 2022

Summary

This study introduces a novel seizure prediction model using ear EEG, ECG, and PPG signals. The developed wearable device accurately detects pre-seizure states, aiming to improve the quality of life for epilepsy patients.

Keywords:

ECG HRV PPG PTT ear EEG epilepsy machine learning outdoors seizure prediction wearable

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Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy

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Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy

Published on: January 29, 2018

Area of Science:

Biomedical Engineering
Neuroscience
Machine Learning

Background:

Epileptic seizures significantly impact patient quality of life and independence.
Current monitoring methods have limitations in providing timely warnings.
A proactive seizure detection system is needed to enhance patient safety and autonomy.

Purpose of the Study:

To develop and validate the first seizure predictive model using Ear Electroencephalography (EEG), Electrocardiography (ECG), and Photoplethysmography (PPG) signals.
To create a wearable device for real-time monitoring in static and outpatient settings.
To classify patient states as normal, pre-seizure, or seizure.

Main Methods:

Acquisition of Ear EEG, ECG, and PPG signals from epilepsy patients in a clinical setting.
Application of supervised machine learning techniques for predictive model development.
Validation of a reduced Boosted Trees model for seizure prediction.

Main Results:

Development of multiple predictive models capable of classifying epileptic patient states.
A validated Boosted Trees model achieved 91.5% prediction accuracy.
The model demonstrated 85.4% sensitivity in predicting seizures.

Conclusions:

The developed seizure predictive model shows significant potential as a support tool for detecting status epilepticus.
The wearable device and predictive model can improve the quality of life for individuals with epilepsy.
This technology offers a promising approach to proactive seizure management and prevention.