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Kazemi, S, Katibeh, P. (1397). Comparison of Parametric and Non-parametric EEG Feature Extraction Methods in Detection of Pediatric Migraine without Aura. سامانه مدیریت نشریات علمی, 8(3), 305-310. doi: 10.31661/jbpe.v0i0.980
S Kazemi; P Katibeh. "Comparison of Parametric and Non-parametric EEG Feature Extraction Methods in Detection of Pediatric Migraine without Aura". سامانه مدیریت نشریات علمی, 8, 3, 1397, 305-310. doi: 10.31661/jbpe.v0i0.980
Kazemi, S, Katibeh, P. (1397). 'Comparison of Parametric and Non-parametric EEG Feature Extraction Methods in Detection of Pediatric Migraine without Aura', سامانه مدیریت نشریات علمی, 8(3), pp. 305-310. doi: 10.31661/jbpe.v0i0.980
Kazemi, S, Katibeh, P. Comparison of Parametric and Non-parametric EEG Feature Extraction Methods in Detection of Pediatric Migraine without Aura. سامانه مدیریت نشریات علمی, 1397; 8(3): 305-310. doi: 10.31661/jbpe.v0i0.980

Comparison of Parametric and Non-parametric EEG Feature Extraction Methods in Detection of Pediatric Migraine without Aura

Journal of Biomedical Physics and Engineering
مقاله 9، دوره 8، شماره 3، آذر 2018، صفحه 305-310    اصل مقاله (559.38 K)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.980
نویسندگان
S Kazemi1؛ P Katibeh* 2
1School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
2Pediatric Neurologist Assistant professor of Shiraz University of medical science, Shiraz, Iran
چکیده
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 24 pediatric migraineurs and 19 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 93% 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.
کلیدواژه‌ها
migraine؛ Quantitative Electroencephalography؛ Welch؛ Yule-Walker Autoregressive Method؛ Support Vector Machine؛ Linear Discriminant Analysis
مراجع
  1. Peng KP, Wang SJ. Migraine diagnosis: screening items, instruments, and scales. Acta Anaesthesiol Taiwan. 2012;50:69-73. doi: 10.1016/j.aat.2012.05.002. PubMed PMID: 22769861.
  2. Olesen J, Steiner TJ. The International classification of headache disorders, 2nd edn (ICDH-II). J Neurol Neurosurg Psychiatry. 2004;75:808-11. PubMed PMID: 15145989; PubMed Central PMCID: PMC1739053.
  3. Hedges TR, Jr. Pediatric migraine. Am J Ophthalmol. 1983;95:844-5. PubMed PMID: 6859200.
  4. Teleanu RI, Vladacenco O, Teleanu DM, Epure DA. Treatment of Pediatric Migraine: a Review. Maedica (Buchar). 2016;11:136-43. PubMed PMID: 28461833; PubMed Central PMCID: PMC5394581.
  5. Kramer U, Nevo Y, Neufeld MY, Harel S. The value of EEG in children with chronic headaches. Brain Dev. 1994;16:304-8. PubMed PMID: 7818026.
  6. Akben SB, Tuncel D, Alkan A. Classification of multi-channel EEG signals for migraine detection. Biomedical Research. 2016;27:743-8.
  7. Bellotti R, De Carlo F, de Tommaso M, Lucente M. Migraine detection through spontaneous EEG analysis. Conf Proc IEEE Eng Med Biol Soc. 2007;2007:1834-7. doi: 10.1109/IEMBS.2007.4352671. PubMed PMID: 18002337.
  8. Schneider T, Neumaier A. Algorithm 808: ARfit—A Matlab package for the estimation of parameters and eigenmodes of multivariate autoregressive models. ACM Trans Math Softw. 2001;27:58-65. doi: 10.1145/382043.382316.
  9. Rejer I, Lorenz K. Genetic algorithm and forward method for feature selection in EEG feature space. J Theor Appl Comput Sci. 2013;7:72-82.
  10. Subasi A, Gursoy MI. EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst Appl. 2010;37:8659-66. doi: 10.1016/j.eswa.2010.06.065.
  11. Hosseinifard B, Moradi MH, Rostami R. Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal. Comput Methods Programs Biomed. 2013;
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