Electroencephalography Artifact Removal using Optimized Radial Basis Function Neural Networks
Publish place: majlesi Journal of Electrical Engineering، Vol: 14، Issue: 4
Publish Year: 1399
نوع سند: مقاله ژورنالی
زبان: English
View: 337
This Paper With 12 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
این Paper در بخشهای موضوعی زیر دسته بندی شده است:
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_MJEE-14-4_013
تاریخ نمایه سازی: 25 بهمن 1401
Abstract:
Electroencephalography (EEG) is a major clinical tool to diagnose, monitor and manage neurological disorders which is mostly affected by artifacts. Given the importance and the need for an automated method to remove artifacts, in this paper some intelligent automated methods are proposed which are composed of three main parts as extraction of effective input, filtering and filter optimization. Wavelet transform is utilized to extract the effective input, and the wavelet approximation coefficients are used as an effective input signal. In addition, Radial Basis Function Neural Network (RBFNN) has been used for filtering. The appropriate number of RBFs has been selected using extensive simulations, and the optimal value of spread parameter has been achieved by Bees algorithm (BA). Finally, the proposed artifact removal schemes have been evaluated on some real contaminated EEG signals in Mashad Ghaem hospital database. The results show that the proposed artifact removal schemes are able to effectively remove artifacts from EEG signals with little underlying brain signal distortion.
Keywords:
Artifacts , Bees Algorithm (BA) , Electroencephalography , Optimization , Radial Basis Function Neural Network (RBFNN) , Wavelet Transform (WT)
Authors
Shoorangiz Shams Shamsabad Farahani
۱- Department of Electrical Engineering, Islamshahr Branch, Islamic Azad University, Islamshahr, Iran
Mohammad Mahdi Arefi
Department of Power and Control Engineering, School of Electrical and Computer Engineering, Shiraz University, ۷۱۳۴۸-۵۱۱۵۴ Shiraz, Iran.
Amir Hossein Zaeri
Department of Electrical Engineering, Shahin shahr Branch, Islamic Azad University, Shahin shahr, Iran
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :