کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
5130 341 2016 7 صفحه PDF ندارد دانلود رایگان
عنوان انگلیسی مقاله
Application of empirical mode decomposition and artificial neural network for the classification of normal and epileptic EEG signals
ترجمه فارسی عنوان
استفاده از تجزیه حالت تجربی و شبکه عصبی مصنوعی برای طبقه بندی سیگنال EEG نرمال و صرع
کلمات کلیدی
الکتروانسفالوگرام (EEG)؛ تشخیص تشنج؛ صرع؛ تجزیه حالت تجربی (EMD)؛ آمار؛ شبکه عصبی مصنوعی (ANN)
موضوعات مرتبط
مهندسی و علوم پایه مهندسی شیمی بیو مهندسی (مهندسی زیستی)
چکیده انگلیسی

Epilepsy is a neurological disorder affecting more than 50 million individuals in the world. Analysis of the electroencephalogram (EEG) is a powerful tool to assist neurologists for diagnosis and treatment. In this paper a new feature extraction method based on empirical mode decomposition (EMD) is proposed. The EEG signal is decomposed into intrinsic mode functions (IMFs) by the EMD algorithm and four statistical parameters are calculated over these IMFs constituting the input feature vector to be fed to a multilayer perceptron neural network (MLPNN) classifier. Experimental results carried out on the publicly available Bonn dataset show that an accurate classification rate of 100% is achieved in the discrimination between normal and ictal EEG, and an accuracy of 97.7% is reached in the classification of interictal and ictal EEG signals. Our results are equivalent or outperform recent studies published in the literature.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Biocybernetics and Biomedical Engineering - Volume 36, Issue 1, 2016, Pages 285–291
نویسندگان
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