Analysis of epileptic seizures based on EEG using recurrence plot images and deep learning

This work proposes deep learning (DL) based epileptic seizure detection by generating 2D recurrence plot (RP) images of EEG signals for specific brain rhythms. The DL bypasses hand-crafted feature engineering, but extracts feature automatically from input images has displayed significant performance in various domain classification tasks. However, generating 2D images from 1D EEG signals and its quality assessment for DL pipeline has not been addressed properly, which is very crucial as the performance of the DL highly relies on input quality. Besides, suitable brain rhythm for seizure analysis has not been explored properly. Therefore, in this work, 2D input images have been generated by the RP technique from EEG signals for specific brain rhythms by preserving the nonlinear characteristics of EEG and employed a well-known DL, called convolution neural network (CNN). For, experimental validation, two well recognized EEG databases for seizure analysis from Bonn University and CHB-MIT (PhysioNet) have been considered. Eventually, three major parameters — recurrence threshold, time delay, and embedding dimension for an RP image generation have been evaluated and detailed. The results show that the proposed method can achieve classification accuracy up to 93%, which is significantly higher and the δ rhythm has been found suitable for seizure detection. The entropy of RP has been found as a suitable parameter for image quality assessment along with two global statistical parameters such as skewness of root mean square and standard of RP images. In performance evaluation, the proposed method demonstrates its competency by displaying the best classification accuracy compared to related works.