  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 iEEGchannelswhich
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%.
