Minireview of Epilepsy Detection Techniques Based on Electroencephalogram Signals

Epilepsy is one of the most common neurological disorders typically characterized by recurrent and uncontrollable seizures, which seriously affects the quality of life of epilepsy patients. The effective tool utilized in the clinical diagnosis of epilepsy is the Electroencephalogram (EEG). The emergence of machine learning promotes the development of automated epilepsy detection techniques. New algorithms are continuously introduced to shorten the detection time and improve classification accuracy. This minireview summarized the latest research of epilepsy detection techniques that focused on acquiring, preprocessing, feature extraction, and classification of epileptic EEG signals. The application of seizure prediction and localization based on EEG signals in the diagnosis of epilepsy was also introduced. And then, the future development trend of epilepsy detection technology has prospected at the end of the article.

Automated summary

Epilepsy is a common neurological disorder characterized by recurrent seizures, affecting patients' quality of life. The development of automated epilepsy detection techniques using machine learning and new algorithms has shown promise in improving detection accuracy and efficiency. Future trends suggest continued advancements in this field.