Wavelet Domain Approximate Entropy-Based Epileptic Seizure Detection

The electroencephalogram (EEG) signal plays an important role in the detection of epilepsy. The EEG recordings of the ambulatory recording systems generate very lengthy data and the detection of the epileptic activity requires a timeconsuming analysis of the entire length of the EEG data by an expert. The aim of this work is to develop a new method for automatic detection of EEG patterns using wavelet based approximate entropy (ApEn) and probabilistic neural network (PNN). Our method consists of EEG data collection, feature extraction and classification stages. ApEn is a statistical parameter that measures the predictability of the current amplitude values of a physiological signal based on its previous amplitude values. In feature extraction stage we use best basis mother wavelet functions and wavelet thresholding technique. For the feature selection we have used a new methodology, that is minimal variance within class and maximal absolute difference between classes are used for feature selection. In classification stage we implement PNN to detect epileptic seizure detection. It is known that the value of the ApEn drops sharply during an epileptic seizure and this fact is used in the proposed system and overall accuracies as high as 100% can be achieved by using the proposed system.. Keywords— approximate entropy (ApEn), wavelet transform, artificial neural network (ANN), electroencephalogram (EEG), EEG classification, epilepsy, seizure detection, probabilistic neural network (PNN).