title: Classification Preictal and Interictal Stages via Integrating Interchannel and Time-Domain Analysis of EEG Features.
abstract: The life quality of patients with refractory epilepsy is extremely affected by abrupt and unpredictable seizures. A reliable method for predicting seizures is important in the management of refractory epilepsy. A critical factor in seizure prediction involves the classification of the preictal and interictal stages. This study aimed to develop an efficient, automatic, quantitative, and individualized approach for preictal/interictal stage identification. Five epileptic children, who had experienced at least 2 episodes of seizures during a 24-hour video EEG recording, were included. Artifact-free preictal and interictal EEG epochs were acquired, respectively, and characterized with 216 global feature descriptors. The best subset of 5 discriminative descriptors was identified. The best subsets showed differences among the patients. Statistical analysis revealed most of the 5 descriptors in each subset were significantly different between the preictal and interictal stages for each patient. The proposed approach yielded weighted averages of 97.50% correctness, 96.92% sensitivity, 97.78% specificity, and 95.45% precision on classifying test epochs. Although the case number was limited, this study successfully integrated a new EEG analytical method to classify preictal and interictal EEG segments and might be used further in predicting the occurrence of seizures.
authors.affiliation: 1 Department of Pediatrics, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.|2 Department of Pediatrics, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan.
authors.forename: Lung-Chang
authors.initials: LC
authors.lastname: Lin
authors.affiliation: 3 Department of Medical Imaging and Radiological Sciences, College of Health Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan.
authors.forename: Sharon Chia-Ju
authors.initials: SC
authors.lastname: Chen
authors.affiliation: 4 Department of Computer and Communication, National Pingtung University, Pingtung, Taiwan.
authors.forename: Ching-Tai
authors.initials: CT
authors.lastname: Chiang
authors.affiliation: 2 Department of Pediatrics, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan.
authors.forename: Hui-Chuan
authors.initials: HC
authors.lastname: Wu
authors.affiliation: 2 Department of Pediatrics, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan.|5 Department of Pediatrics, Changhua Christian Hospital, Changhua, Taiwan.
authors.forename: Rei-Cheng
authors.initials: RC
authors.lastname: Yang
authors.affiliation: 6 Department of Information Engineering, I-Shou University, Kaohsiung, Taiwan.
authors.forename: Chen-Sen
authors.initials: CS
authors.lastname: Ouyang
mesh_terms: D000293:Adolescent; D000465:Algorithms; D002648:Child; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D003937:Diagnosis, Differential; D018450:Disease Progression; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis*; D005260:Female; D006801:Humans; D007231:Infant, Newborn; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
keywords: classification; epilepsy; feature selection; scalp EEG; support vector classifier