Automatic recognition of preictal and interictal EEG signals using 1D-capsule networks

Epilepsy is the most common neurological disorder affecting people of all ages. Seizure prediction can be achieved by separating the preictal state in which the changes in the brain activities begin to occur from the interictal state. Therefore, in this study, a novel method for preictal/interictal recognition, the most important step in seizure prediction from scalp electroencephalogram signals, is proposed. In the proposed method, one-dimensional capsule networks, a novel neural network model, is used. The best classification accuracy for preictal/interictal recognition was achieved with 97.74% in F3-C3 channel pairs. Compared to other methods, our 1D-CapsNet model achieved the best performance. Moreover, the results indicated that the interval that ended 30 min before the onset of seizures contained important information about preictal/interictal recognition. We believe that the proposed method will bring a new perspective to the seizure prediction studies of capsule networks.

Automated summary

A novel method using one-dimensional capsule networks for preictal/interictal recognition in scalp electroencephalogram signals achieved the best classification accuracy. The results indicated that important information about preictal/interictal recognition can be found in the 30 minutes before the onset of seizures. The proposed method brings a new perspective to seizure prediction studies using capsule networks.