Is Using Threshold-Crossing Method and Single Type of Features Sufficient to Achieve Realistic Application of Seizure Prediction?

Objective. This study aims to verify whether the simple threshold-crossing method can work well enough to achieve the realistic application of seizure prediction on the basis of a large public database, and examines how a more complex classifier can improve prediction performance. It also verified whether the combination of multiple types of features with a complex classifier can improve prediction performance. Method. Phase synchronization and spectral power features were extracted from electroencephalogram recordings. The threshold-crossing method and a support vector machine (SVM) were used to identify preictal and interictal samples. Based on the type of selected features and the manner of classification, 5 different methods were conducted on 19 patients. The performances of these methods were directly compared and tested using a random predictor. In-sample optimization problems were avoided in the feature and parameter selection procedure to obtain credible results. Results. The threshold-crossing method could only obtain satisfying prediction results for approximately half of the selected patients. The SVM classifier could significantly improve prediction performance compared with the threshold-crossing method for both types of features. Although the average performance was further improved when both types of features were combined with the SVM classifier, the improvement was insignificant. Conclusion. A complex classifier, such as the SVM, is recommended in a realistic prediction device, although it will increase the complexity of the device. Indeed, the simple threshold-crossing method performs well enough for some of the patients. The combination of phase synchronization and spectral power features is unnecessary because of the increased computation complexity.