Comparison of Parametric and Non-parametric EEG Feature Extraction Methods in Detection of Pediatric Migraine without Aura
عنوان مقاله: Comparison of Parametric and Non-parametric EEG Feature Extraction Methods in Detection of Pediatric Migraine without Aura
شناسه ملی مقاله: JR_JBPE-8-3_009
منتشر شده در در سال 1397
شناسه ملی مقاله: JR_JBPE-8-3_009
منتشر شده در در سال 1397
مشخصات نویسندگان مقاله:
S Kazemi - School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
P Katibeh - Pediatric Neurologist Assistant professor of Shiraz University of medical science, Shiraz, Iran
خلاصه مقاله:
S Kazemi - School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
P Katibeh - Pediatric Neurologist Assistant professor of Shiraz University of medical science, Shiraz, Iran
Background: Migraine headache without aura is the most common type of migraine especially among pediatric patients. It has always been a great challenge of migraine diagnosis using quantitative electroencephalography measurements through feature classification. It has been proven that different feature extraction and classification methods vary in terms of performance regarding detection and diagnostic accuracy. Previous work on the subject was controversial, hence a comparison of these methods seems necessary.Objectives: The aim of this research is to compare two parametric and non-parametric feature extraction methods and also two classification methods in order to obtain optimal combinations of diagnostic accuracy.Materials and Methods: Having recorded background EEG from ۲۴ pediatric migraineurs and ۱۹ control subjects, data was processed by Welch’s and Yule-Walker’s methods. Features were selected using genetic algorithm, and then given to a support vector machine and the linear discriminant analysis for the classification. Accuracy was calculated for all combinations having the dominant frequency and the correlated absolute power of each EEG wave band (theta, alpha, and beta) and for all wave bands combined.Results: The highest migraine detection accuracy of ۹۳% was obtained utilizing Welch’s method for EEG feature extraction alongside support vector machine for a classifier. Besides, Yule-Walker autoregressive method showed better performance than Welch’s, when only power bands (and not the dominant frequency) were used as classification input.Conclusion: The superiority of Welch’s method over Yule-Walker’s and the support vector machine over linear discriminant analysis can be great help for further researches on computer aided EEG-based diagnosis of migraine.
کلمات کلیدی: migraine, Quantitative Electroencephalography, Welch, Yule-Walker Autoregressive Method, Support Vector Machine, Linear Discriminant Analysis
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/1891959/