Preictal and Interictal Recognition for Epileptic Seizure Prediction Using Pre-trained 2D-CNN Models

Abstract

Epilepsy is a neurological disease affecting almost 1% of world population. Predicting a possible seizure will make a significant contribution to improving the quality of life of patients suffering from this disease. One of the most important steps in seizure prediction studies is the preictal activity recognition stage. In many previous studies, the preictal state was determined to end at the onset of the seizure, which makes it difficult for the physician to intervene in the patient in a possible seizure. In the proposed method, unlike previous studies, the preictal state was determined as the 30-minute interval ending 30 minutes before the onset of an epileptic seizure. The method consisted of three stages; (I) preictal and interictal activities were divided into five-second segments, (ii) the separated signals were converted into spectrograms, and (iii) the spectrogram images were classified using three different pre-trained CNN models (VGG19, ResNet, DenseNet) and the results were compared among these models. Classification was performed separately using the predetermined four EEG channels for 20 cases in the CHB-MIT dataset. The best classification accuracy value in preictal/interictal discrimination (91.05%) was obtained on channel 8 (P3-O1). An important contribution of this study was that the proposed approach provided important information about the preictal and interictal discrimination of the section 30 minutes before the onset of seizures. In addition, by examining the four channels separately, channel-based information on preictal/interictal discrimination was also obtained. Based on these results, we consider that the proposed method will bring a different perspective to seizure prediction studies.