Automated epilepsy detection techniques from electroencephalogram signals: a review study

Area of Science:

• Neurology
• Biomedical Engineering
• Medical Informatics

Background:

• Epilepsy is a significant global health concern, ranking among the top five causes of preventable mortality in young individuals (ages 5-29).
• Developing automated systems for epilepsy detection and prediction is crucial for mitigating avoidable deaths associated with this neurological condition.

Purpose of the Study:

• To review and analyze existing epilepsy detection and classification techniques.
• To highlight the benefits and drawbacks of current epilepsy detection methodologies.
• To provide insights for developing robust automated epilepsy identification software.

Main Methods:

• Literature review of existing epilepsy detection and classification techniques.
• Analysis of the advantages and disadvantages of various approaches.
• Synthesis of information to guide the selection of appropriate methods for automated systems.

Main Results:

• Current epilepsy detection methods vary in efficacy and have associated limitations.
• Understanding these techniques is essential for improving diagnostic accuracy and treatment support.
• A comprehensive overview of existing approaches is necessary for future advancements.

Conclusions:

• This review provides valuable information for neuroscientists, researchers, and technicians.
• It aids in selecting reliable techniques for epilepsy analysis and automated system development.
• Further research into efficient automated epilepsy detection is warranted to reduce mortality.