Towards fully automated detection of epileptic disorders: a novel CNSVM approach with Clough-Tocher interpolation

Epilepsy is a neurological disorder requiring specialists to scrutinize medical data at diagnosis. Diagnosis stage is both time consuming and challenging, requiring expertise in detection of epileptic seizures from multi-channel noisy EEG data. It is crucial that EEG signals be automatically classified in order to help experts detect epileptic seizures correctly. In this study, a novel hybrid deep learning and SVM technique is employed on a restructured EEG data. EEG signals were transformed into a two-dimensional image sequence. Clough-Tocher technique is employed for interpolation of the values obtained from the electrodes placed on the skull during EEG measurements in order to estimate the signal strength in the missing places over the picture. After the parameters in the deep learning architecture were optimized on the validation data, it is observed that the proposed technique's performance for classifying epilepsy moments over EEG signals demonstrated unmatched performance. This study fills a gap in the literature in terms of demonstrating a superior performance in automatic detection of epileptic episodes on a benchmark EEG data set and takes a substantial leap towards fully automated detection of epileptic disorders.