Quantifying the complexity of epileptic EEG

In this paper, the issue of automatic epileptic seizure detection is addressed, emphasizing how the huge amount of Electroencephalographic (EEG) data from epileptic patients can slow down the diagnostic procedure and cause mistakes. The EEG of an epileptic patient can last from minutes to many hours and the goal here is to automatically detect the seizures that occurr during the EEG recording. In other words, the goal is to automatically discriminate between the interictal and ictal states of the brain so that the neurologist can immediately focus on the ictal states with no need of detecting such events manually. In particular, the attention is focused on absence seizures. The goal is to develop a system that is able to extract meaningful features from the EEG and to learn how to classify the brain states accordingly. The complexity of the EEG is considered a key feature when dealing with an epileptic brain and two measures of complexity are here estimated and compared in the task of interictal-ictal states discrimination: Approximate Entropy (ApEn) and Permutation Entropy (PE). A Learning Vector Quantization network is then fed with ApEn and PE and trained. The ApEn+LVQ learning system provided a better sensitivity compared to the PE+LVQ one, nevertheless, it showed a smaller specificity.