Comparative Analysis of Non-Stationary Signal Processing Approaches for Epileptic Disease Detection

Epilepsy is a prevalent and unpredictable neurological disorder that requires EEG analysis for diagnosis. Given the non-stationary nature of EEG recordings, advanced signal processing techniques are essential for classifying epilepsy EEG signals accurately into normal, pre-ictal, and ictal signals. Recent research has suggested using temporal frequency representations like Spectrogram, Scalogram, Smoothed Pseudo Wigner-Ville distribution (SPWVD), and Hilbert-Huang Transform (HHT). In this study, we proposed a computerized classification method for epileptic EEG signals. We conduct a comparative analysis of nonstationary signal processing techniques alongside a deep convolutional neural network (CNN) classifier. Our research utilizes the EEG database from the University of Bonn. Our findings indicate variations in classification results among the employed feature extraction methods. Spectrogram techniques, in particular, show promising results with an average accuracy of $98.66 \%$.