DWT based transformed domain feature extraction approach for epileptic seizure detection

Abstract

Epileptic seizure is a neurological disorder that is prevalent in both males and females of all age ranges. Detection of epileptic seizure serves as an important role for epileptic patients as it allows the initiation of systems to prevent injuries and limiting the possibilities of risk by providing targeted therapy by anticipating their onset prior to presentation. Electroencephalography (EEG) plays an important role in seizure detection and is one of the most well-known techniques for determining stages of epilepsy. Since, EEG is a non-stationary signal it can be quite difficult to differentiate amongst seizure activity and normal neural activity. In this paper we have proposed an epilepsy detection method based on five different feature extraction methods and followed by that the original domain of the extracted features were transformed using Discrete Wavelet Transform (DWT) and three different classifiers-Decision Tree, Random Forest and KNN to classify into seizure and non-seizure stages. Results demonstrated in this paper have outperformed the existing state-of-The-Art methods with 97.22%, 100% and 83.33% for 2 class classification and 91.67%, 91.67% and 80.56% for 4 class classification for the aforementioned classification techniques accordingly.