Electroencephalogram signal classification based on shearlet and contourlet transforms

Detection of epilepsy patterns in EEG signals with high accuracy.Development of a novel methodology based on curvelet and shearlet transforms.Extraction of a set of discriminative characteristics from the signals.Evaluation on a public data set.Results superior/comparable to the literature. Epilepsy is a disorder that affects approximately 50 million people of all ages, according to World?Health Organization?(2016), which makes it one of the most common neurological diseases worldwide. Electroencephalogram (EEG) signals have been widely used to detect epilepsy and other brain abnormalities. In this work, we propose and evaluate a novel methodology based on shearlet and contourlet transforms to decompose the EEG signals into frequency bands. A set of features are extracted from these time-frequency coefficients and used as input to different classifiers. Experiments are conducted on a public data set to demonstrate the effectiveness of the proposed classification method. The developed system can help neurophysiologists identify EEG patterns in epilepsy diagnostic tasks.