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Hardware implementations of machine learning techniques for neural seizure detection / Mohamed Adel Attia Elhady Elgammal ; Supervised Ahmed Nader Mohieldien , Hassan Mostafa Hassan

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Mohamed Adel Attia Elhady Elgammal , 2018Description: 156 P. : charts , facsimiles ; 30cmOther title:
  • تصميم وتنفيذ عتاد لتقنيات تعليم الآلة لاستخدامها فى الكشف عن نوبات الصرع العصبية [Added title page title]
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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Electronics and Communications Summary: In this thesis an automatic seizure detection is proposed. For features extraction, more than 20 linear and nonlinear features are software implemented and tested to measure their efficiency in seizure detection. For classification block, two different algorithms are implemented: Artificial Neural Network (ANN) and Support Vector Machine (SVM). Support Vector Machine (SVM) training accelerators are also implemented using two different techniques: Gradient Ascent (GA) and Sequential Minimal Optimization (SMO). Finally, a new EEG dataset is extracted from rats in collaboration with a research team from the Faculty of Science, Cairo university and ONE lab
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Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.08.M.Sc.2018.Mo.H (Browse shelf(Opens below)) Not for loan 01010110077092000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.08.M.Sc.2018.Mo.H (Browse shelf(Opens below)) 77092.CD Not for loan 01020110077092000

Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Electronics and Communications

In this thesis an automatic seizure detection is proposed. For features extraction, more than 20 linear and nonlinear features are software implemented and tested to measure their efficiency in seizure detection. For classification block, two different algorithms are implemented: Artificial Neural Network (ANN) and Support Vector Machine (SVM). Support Vector Machine (SVM) training accelerators are also implemented using two different techniques: Gradient Ascent (GA) and Sequential Minimal Optimization (SMO). Finally, a new EEG dataset is extracted from rats in collaboration with a research team from the Faculty of Science, Cairo university and ONE lab

Issued also as CD

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