Automatic Absence Seizure Detection Evaluating Matching Pursuit Features of EEG Signals

Abstract

This paper evaluates the usage of matching pursuit (MP) features of electroencephalographic (EEG) signals and classification techniques on automatic absence seizure detection. Absence epileptic seizures are neurological disorders which are manifested as abnormal EEG patterns. Matching pursuit algorithm is able to decompose a signal into components with specific time-frequency characteristics. It is a robust technique especially when there is complex, multicomponent signal. In the present study, a clinical dataset containing 40 annotated absence seizures in long-term EEG recordings from pediatric epileptic patients (with age 6.0±2.9 years) was analyzed. The extracted MP features fed an automatic classification schema which achieved a time window based discrimination accuracy of 98.5%. As indicated by the study's results, the proposed features and analysis methods can be a promising addition to the area of automatic absence seizures detection.