Evaluation of Wavelet Transformed Features on Detection of Epileptic Seizures using 2D Scalogram Images of EEG Signals

This research presents a completely new paradigm for seizure onset detection automation that is based on electroencephalography (EEG). In order to process the data, we used the CHB-MIT scalp EEG dataset. Data augmentation is utilised so that the dataset can be balanced out. We had to increase the size of the ictal data since, in compared to the interictal records, they were far too little. In order to deconstruct the EEG signal, one of the following techniques may be utilised: a discrete wavelet transform (DWT)-Daubechies Wavelet4(db4), a continuous wavelet transform (CWT), a wavelet packet decomposition (WPD), or a continuous wavelet transform (DWT)-Daubechies Wavelet4(dbS).It was determined that the VGG16 pre-trained model was the best option for automatically classifying 2D scalogram images into seizure and non-epileptic seizure states. The performance of four wavelet-based features was tested, and CWT-based features outperformed all others with an average accuracy of 97%.