![]() Automatic classification schema based on Machine Learning techniques were employed to categorize the MP patterns into non-ictal and ictal states. 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. METHODS: Based on the ictal EEG semiology, MP features were extracted able to track the ictal pattern. In this paper, we propose a method for absence seizures detection based on EEG signals decomposition via the Matching Pursuit (MP) algorithm. 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. INTRODUCTION: Absence seizures are characterized by a typical generalized spike-and-wave electroencephalographic (EEG) pattern around 3Hz.
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