Journal
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
Volume 65, Issue 11, Pages 2440-2449Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2018.2797919
Keywords
Epilepsy; clinical seizure onset; early detection; cross-frequency coupling features; machine learning; EEG; random forest; multi-stage state classification
Categories
Funding
- Natural Sciences and Engineering Research Council of Canada
- Canadian Institutes of Health Research
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Objective: This work proposes a machine-learning based system for a scalp EEG that flags an alarm in advance of a clinical seizure onset. Methods: 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, (I) over bar (c)(fc), 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. Results: 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. Conclusion: Using the scalp (I) over bar (c)(fc), the MSC produced an alarm in advance of a clinical seizure onset for all 12 patients. Patient-specific training improved the specificity of classification. Significance: The MSC is noninvasive, and demonstrates that CFC features may be suitable for use in a home-based seizure monitoring system.
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