Supervised Learning in Automatic Channel Selection for Epileptic Seizure Detection

Detecting seizure using brain neuroactivations recorded by intracranial electroencephalogram (iEEG) has been widely used for monitoring, diagnosing, and closed-loop therapy of epileptic patients, however, computational efficiency gains are needed if state-of-the-art methods are to be implemented in implanted devices. We present a novel method for automatic seizure detection based on iEEG data that outperforms current state-of-the-art seizure detection methods in terms of computational efficiency while maintaining the accuracy. The proposed algorithm incorporates an automatic channel selection (ACS) engine as a pre-processing stage to the seizure detection procedure. The ACS engine consists of supervised classifiers which aim to find iEEG channels which contribute the most to a seizure. Seizure detection stage involves feature extraction and classification. Feature extraction is performed in both frequency and time domains where spectral power and correlation between channel pairs are calculated. Random Forest is used in classification of interictal, ictal and early ictal periods of iEEG signals. Seizure detection in this paper is retrospective and patient-specific. iEEG data is accessed via Kaggle, provided by International Epilepsy Electro-physiology Portal. The dataset includes a training set of 6.5 hours of interictal data and 41 minin ictal data and a test set of 9.14 hours. Compared to the state-of-the-art on the same dataset, we achieve 49.4% increase in computational efficiency and 400 mins better in average for detection delay. The proposed model is able to detect a seizure onset at 91.95% sensitivity and 94.05% specificity with a mean detection delay of 2.77 s. The area under the curve (AUC) is 96.44%, that is comparable to the current state-of-the-art with AUC of 96.29%.