Absence seizure detection classifying matching pursuit features of EEG signals

INTRODUCTION: Absence seizures are characterized by a typical generalized spike-and-wave electroencephalographic (EEG) pattern around 3Hz. The automatic identification of this pattern and consequently its corresponding seizure is a valuable information towards the reliable patient’s clinical image and treatment planning. In this paper, we propose a method for absence seizures detection based on EEG signals decomposition via the Matching Pursuit (MP) algorithm. METHODS: Based on the ictal EEG semiology, MP features were extracted able to track the ictal pattern. Thisanalysis was performed in a clinical dataset of 8 pediatric patients (4 females, 4 males) suffering from activeabsence epilepsy, containing 123 absence seizures in total. Automatic classification schema based on Machine Learning techniques were employed to categorize the MP patterns into non-ictal and ictal states. RESULTS: The seizure detection system achieved a time window based discrimination accuracy of 97.3% byusing a Support Vector Machine (SVM) classifier and 10-fold cross-validation, in that way accomplishing a good state of the art performance. DISCUSSION: Compared to other popular spectral analysis methods, Matching Pursuit appears to be a robustand efficient method regarding absence seizures detection on EEG signals and our results indicate that the MP features proposed in this work are features that can be used effectively in seizure detection procedure.