Epileptic Seizures Classification Based on Long-Term EEG Signal Wavelet Analysis

摘要

Epilepsy is a complex neurological disorder recognized by abnormal synchronization of cerebral neurons, named seizures. During the last decades, significant progress has been done in automated detection and prediction of seizures, aiming to develop personalized closed-loop intervention systems. In this paper, a methodology for automated seizure detection based on Discrete Wavelet Transform (DWT) is presented. Twenty-one intracranial ictal recordings acquired from the database of University Hospital of Freiburg are firstly segmented in 2 s epochs. Then, a five-level decomposition is applied in each segment and five features are extracted from the wavelet coefficients. The extracted feature vector is used to train a Support Vector Machines (SVM) classifier. Average sensitivity and specificity reached above 93% and 99% respectively.