Focal Epileptic Area Recognition Employing Cross EEG Rhythm Spectrum Images and Convolutional Neural Network

Accurate detection of focal seizure area through EEG screening is important to remove the affected regions of human brain, prior to surgery. Considering the aforesaid fact, in this paper, we propose a novel approach for automated detection and classification of focal electroencephalography (EEG) signals. In this contribution, we procured the focal and non focal EEG signals recorded from the temporal lobe epileptic patients and decomposed the recorded EEG data into several brain rhythms to obtain their time variation of different neural oscillations. Then, we selected a random EEG signal from each rhythm as a reference and performed cross wavelet transform (XWT) of the rest of the EEG rhythms with their respective chosen reference rhythms to examine their nature of correlation in both time scale and time-frequency frame. Finally, the obtained cross-rhythm spectrum time-frequency image plots were fed to a customized CNN model for the purpose of automated feature extraction and classification of EEG signals. The performance of the proposed CNN model was also compared with two pre-trained benchmark CNN models namely AlexNet and VGGNet16, respectively. We observed that the proposed EEG signal classification framework has delivered 100% accuracy for the delta (
$\delta$
) rhythm and that too at a significantly reduced training time compared to the existing state-of-the-art CNN models. Interestingly, the detection accuracy was also found to be the highest for a particular EEG rhythm, which can be proven useful in real time analysis for accurate diagnosis of focal epilepsy.