Various epileptic seizure detection techniques using biomedical signals: a review

Area of Science:

• Neurology
• Biomedical Engineering
• Signal Processing

Background:

• Epilepsy is a chronic central nervous system disorder characterized by recurrent seizures, impacting over 2% of the global population.
• Seizures are transient disruptions in brain electrical activity, leading to diverse physical and cognitive symptoms.
• A significant portion of epilepsy patients experience frequent seizures resistant to current anti-epileptic drug treatments.

Purpose of the Study:

• To review and classify existing seizure detection techniques.
• To present the state-of-the-art in seizure detection methodologies.
• To identify promising research directions for improved seizure detection.

Main Methods:

• Classification of seizure detection techniques into categories: time domain, frequency domain, wavelet (time-frequency), empirical mode decomposition, and rational function methods.
• Review of current literature on various seizure detection algorithms.
• Analysis of the strengths and limitations of different detection approaches.

Main Results:

• The paper categorizes seizure detection methods based on their underlying signal processing principles.
• It highlights the diversity of techniques available for analyzing brain activity related to seizures.
• The review provides a structured overview of the current landscape of seizure detection.

Conclusions:

• Accurate and timely seizure detection remains a critical challenge in epilepsy management.
• Advancements in signal processing offer various approaches to improve detection accuracy.
• Further research is needed to develop more effective and robust seizure detection systems, particularly for the aging population and treatment-resistant cases.