title: Is Using Threshold-Crossing Method and Single Type of Features Sufficient to Achieve Realistic Application of Seizure Prediction?
abstract: 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.
authors.affiliation: The Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.|National Engineering Research Center of Health Care and Medical Devices, Xi'an Jiaotong University Branch, Xi'an, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Zheng
authors.affiliation: The Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.|National Engineering Research Center of Health Care and Medical Devices, Xi'an Jiaotong University Branch, Xi'an, China.
authors.forename: Gang
authors.initials: G
authors.lastname: Wang
authors.affiliation: The Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China juewang1@126.com.|National Engineering Research Center of Health Care and Medical Devices, Xi'an Jiaotong University Branch, Xi'an, China.
authors.forename: Jue
authors.initials: J
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012640:Seizures / Q000175:diagnosis* / Q000503:physiopathology*; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
keywords: electroencephalogram; epilepsy; seizure prediction; support vector machine (SVM); synchronization