دانلود مقاله ISI انگلیسی شماره 79632
عنوان فارسی مقاله

یک روش برنامه ریزی ژنتیکی جدید برای تشخیص تشنج صرع

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
79632 2016 17 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
A novel genetic programming approach for epileptic seizure detection
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Computer Methods and Programs in Biomedicine, Volume 124, February 2016, Pages 2–18

کلمات کلیدی
برنامه نویسی ژنتیک؛ متقاطع سازنده؛ محاسبه ارزش تناسب اندام پویا؛ بیماری صرع
پیش نمایش مقاله
پیش نمایش مقاله یک روش برنامه ریزی ژنتیکی جدید برای تشخیص تشنج صرع

چکیده انگلیسی

The human brain is a delicate mix of neurons (brain cells), electrical impulses and chemicals, known as neurotransmitters. Any damage has the potential to disrupt the workings of the brain and cause seizures. These epileptic seizures are the manifestations of epilepsy. The electroencephalograph (EEG) signals register average neuronal activity from the cerebral cortex and label changes in activity over large areas. A detailed analysis of these electroencephalograph (EEG) signals provides valuable insights into the mechanisms instigating epileptic disorders. Moreover, the detection of interictal spikes and epileptic seizures in an EEG signal plays an important role in the diagnosis of epilepsy. Automatic seizure detection methods are required, as these epileptic seizures are volatile and unpredictable. This paper deals with an automated detection of epileptic seizures in EEG signals using empirical mode decomposition (EMD) for feature extraction and proposes a novel genetic programming (GP) approach for classifying the EEG signals. Improvements in the standard GP approach are made using a Constructive Genetic Programming (CGP) in which constructive crossover and constructive subtree mutation operators are introduced. A hill climbing search is integrated in crossover and mutation operators to remove the destructive nature of these operators. A new concept of selecting the Globally Prime offspring is also presented to select the best fitness offspring generated during crossover. To decrease the time complexity of GP, a new dynamic fitness value computation (DFVC) is employed to increase the computational speed. We conducted five different sets of experiments to evaluate the performance of the proposed model in the classification of different mixtures of normal, interictal and ictal signals, and the accuracies achieved are outstandingly high. The experimental results are compared with the existing methods on same datasets, and these results affirm the potential use of our method for accurately detecting epileptic seizures in an EEG signal.

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پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.