Classification of Pre-Clinical Seizure States Using Scalp EEG Cross-Frequency Coupling Features

This work proposes a machine-learning based system for a scalp EEG that flags an alarm in advance of a clinical seizure onset.

EEG recordings from 12 patients with drug resistant epilepsy were marked by an expert neurologist for clinical seizure onset. Scalp EEG recordings consisted of 56 seizures and 9.67 h of interictal periods. Data from six patients were reserved for testing, and the rest was split into training and testing sets. A global spatial average of a cross-frequency coupling (CFC) index, , was extracted in 2 s windows, and used as the feature for the machine learning. A multistage state classifier (MSC) based on random forest algorithms was trained and tested on these data. Training was conducted to classify three states: interictal baseline, and segments prior to and following EG onset. Classifier performance was assessed using a receiver-operating characteristic (ROC) analysis.

The MSC produced an alarm 45 16 s in advance of a clinical seizure onset across seizures from the 12 patients. It performed with a sensitivity of 87.9%, a specificity of 82.4%, and an area-under-the-ROC of 93.4%. On patients for whom it received training, performance metrics increased. Performance metrics did not change when the MSC used reduced electrode ring configurations.

Using the scalp , the MSC produced an alarm in advance of a clinical seizure onset for all 12 patients. Patient-specific training improved the specificity of classification.

The MSC is noninvasive, and demonstrates that CFC features may be suitable for use in a home-based seizure monitoring system.