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

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.