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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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EEG seizure detection: concepts, techniques, challenges, and future trends.

Athar A Ein Shoka¹, Mohamed M Dessouky^1,2, Ayman El-Sayed¹

¹Faculty of Electronic Engineering, Computer Science and Engineering Department, Menoufia University, Menouf, Egypt.

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|June 26, 2023

Summary

This study reviews automated epilepsy detection using electroencephalography (EEG) signals. It explores machine learning and IoT for remote patient monitoring, aiming to improve seizure management and daily life for epilepsy patients.

Keywords:

Artificial intelligence Classification Electroencephalography (EEG)Epilepsy Features extraction IoT

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Area of Science:

Neurology
Biomedical Engineering
Computer Science

Background:

Epilepsy is a central nervous system disorder characterized by abnormal brain activity leading to seizures.
Manual analysis of electroencephalography (EEG) signals for seizure detection is time-consuming and challenging.
Developing automated systems is crucial for timely interventions and improved patient care.

Purpose of the Study:

To review current methods for automated epileptic seizure detection using EEG signals.
To explore the integration of the Internet of Things (IoT) and machine learning for remote patient monitoring.
To identify challenges and future research directions in EEG-based epilepsy detection.

Main Methods:

Review of existing literature on epilepsy, seizure types, and EEG signal processing.
Analysis of feature extraction and classification techniques for EEG data.
Discussion of IoT and machine learning approaches for automated detection systems.

Main Results:

The review highlights the importance of automated EEG analysis for epilepsy management.
Machine learning classifiers show promise for accurate seizure detection.
IoT integration enables remote patient monitoring, enhancing healthcare accessibility.

Conclusions:

Automated EEG-based seizure detection systems, particularly those using IoT and machine learning, offer significant potential for epilepsy care.
Further research is needed to address current challenges and optimize these systems.
These advancements can lead to improved patient outcomes and quality of life.