A multiclass classification of epileptic activity in patients using wavelet decomposition

Abstract

Epilepsy is a chronic disorder caused in the brain where seizures occur multiple times unreliably causing unconsciousness or tremendous convulsions over the entire body. The identification of epileptic seizure activities in electroencephalography (EEG) signals by manual inspection is prone to errors and time-consuming. The proposed study suggests using Discrete Wavelet Transform to decompose the EEG signals into frequency sub-bands. A certain subset of the frequency sub-bands was chosen for feature selection. Following the DWT decomposition, the proposed method calculates the standard deviation for each sub-band present in the subset. Finally, it feeds the standard deviation values of the sub-bands to the classifiers. This work investigated the three-class classification problem focused on classifying an EEG signal into one of the three classes, which are (1) healthy patient with eyes closed, (2) patients in inter-ictal stage whose EEG recordings have been recorded from the hippocampal formation of the opposite hemisphere of the brain, and (3) patients experiencing seizure activities. The accuracy achieved in proposed work is 98.45% which beats the state-of-the-art accuracy in this three-class problem. Additionally, the proposed method achieves the highest accuracy of 100% in classifying normal EEG signals (eyes open and eyes closed) and seizure EEG signal in two separate experiments which is comparable with the existing state of the art EEG signal classification techniques. The proposed work uses six different classifiers in each of the three experiments conducted where every classifier has been used with 8 different Daubechies wavelets db1 to db8. The results obtained from these experiments provide valuable insights establishing that SVM performs the best in most of the experiments with the db4 wavelet among the 8 wavelets achieving the highest accuracy.