On the proper selection of preictal period for seizure prediction

- A statistical approach is developed to find the optimal preictal period (OPP).
- The OPP values can be used for the proper training of a classifier, and building a more robust model for seizure prediction.
- It can also quantify the prediction capability of a feature, or the predictability of a seizure.
- Monopolar montage provided better results than bipolar using iEEG, whereas no significant difference was observed using sEEG.

Supervised machine learning-based seizure prediction methods consider preictal period as an important prerequisite parameter during training. However, the exact length of the preictal state is unclear and varies from seizure to seizure. We propose a novel statistical approach for proper selection of the preictal period, which can also be considered either as a measure of predictability of a seizure or as the prediction capability of an understudy feature. The optimal preictal periods (OPPs) obtained from the training samples can be used for building a more accurate classifier model. The proposed method uses amplitude distribution histograms of features extracted from electroencephalogram (EEG) recordings. To evaluate this method, we extract spectral power features in different frequency bands from monopolar and space-differential EEG signals of 18 patients suffering from pharmacoresistant epilepsy. Furthermore, comparisons among monopolar channels with space-differential channels, as well as intracranial EEG (iEEG) and surface EEG (sEEG) signals, indicate that while monopolar signals perform better in iEEG recordings, no significant difference is noticeable in sEEG recordings.