Seizure Prediction Using EEG Channel Selection Method

Abstract

Seizure prediction using intracranial electroencephalogram (iEEG) is still challenging because of complicated signals in spatial and time domains. Feature selection in the spatial domain (i.e., channel selection) has been largely ignored in this field. Hence, in this paper, a novel approach of iEEG channel selection strategy combined with one-dimensional convolutional neural networks (1D-CNN) was presented for seizure prediction. First, 15-sec and 30-sec iEEG segments with an increasing number of channels (from one channel to all channels) were sequentially fed into 1D-CNN models for training and testing. Then, the channel case with the best classification rate was selected for each participant. We tested our method on the Freiburg iEEG dataset. A sensitivity of 89.03-90.84%, specificity of 98.99-99.73%, and accuracy of 98.07-98.99% were achieved at the segment-based level. At the event-based level, we attained a sensitivity of 98.48-98.85% and a false prediction rate (FPR) of 0-0.02/h.