کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
387357 660901 2010 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Least squares support vector machine employing model-based methods coefficients for analysis of EEG signals
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Least squares support vector machine employing model-based methods coefficients for analysis of EEG signals
چکیده انگلیسی

The aim of the study is classification of the electroencephalogram (EEG) signals by combination of the model-based methods and the least squares support vector machines (LS-SVMs). The LS-SVMs were implemented for classification of two types of EEG signals (set A – EEG signals recorded from healthy volunteers with eyes open and set E – EEG signals recorded from epilepsy patients during epileptic seizures). In order to extract the features representing the EEG signals, the spectral analysis of the EEG signals was performed by using the three model-based methods (Burg autoregressive – AR, moving average – MA, least squares modified Yule–Walker autoregressive moving average – ARMA methods). The present research demonstrated that the Burg AR coefficients are the features which well represent the EEG signals and the LS-SVM trained on these features achieved high classification accuracies.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 37, Issue 1, January 2010, Pages 233–239
نویسندگان
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