عرض عادي

Practical guide for biomedical signals analysis using machine learning techniques : a MATLAB based approach / Abdulhamit Subasi.

بواسطة:نوع المادة : نصنصاللغة: الإنجليزية الناشر:London ; San Diego, CA : Academic Press, an imprint of Elsevier, 2019وصف:xi, 443 pages : illustrations ; 28 cmنوع المحتوى:
  • text
نوع الوسائط:
  • unmediated
نوع الناقل:
  • volume
تدمك:
  • 9780128174449
  • 0128174447
الموضوع:تصنيف مكتبة الكونجرس:
  • R857.B54 S83 2019
ملخص:Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach presents how machine learning and biomedical signal processing methods can be used in biomedical signal analysis. Different machine learning applications in biomedical signal analysis, including those for electrocardiogram, electroencephalogram and electromyogram are described in a practical and comprehensive way, helping readers with limited knowledge. Sections cover biomedical signals and machine learning techniques, biomedical signals, such as electroencephalogram (EEG), electromyogram (EMG) and electrocardiogram (ECG), different signal-processing techniques, signal de-noising, feature extraction and dimension reduction techniques, such as PCA, ICA, KPCA, MSPCA, entropy measures, and other statistical measures, and more. This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need a cogent resource on the most recent and promising machine learning techniques for biomedical signals analysis.-- Source other than Library of Congress.
المقتنيات
نوع المادة المكتبة الحالية رقم الطلب رقم النسخة حالة تاريخ الإستحقاق الباركود
كتاب كتاب UAE Federation Library | مكتبة اتحاد الإمارات General Collection | المجموعات العامة R857.B54 S83 2019 (إستعراض الرف(يفتح أدناه)) C.1 Library Use Only | داخل المكتبة فقط 30020000208199

Includes bibliographical references and index.

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach presents how machine learning and biomedical signal processing methods can be used in biomedical signal analysis. Different machine learning applications in biomedical signal analysis, including those for electrocardiogram, electroencephalogram and electromyogram are described in a practical and comprehensive way, helping readers with limited knowledge. Sections cover biomedical signals and machine learning techniques, biomedical signals, such as electroencephalogram (EEG), electromyogram (EMG) and electrocardiogram (ECG), different signal-processing techniques, signal de-noising, feature extraction and dimension reduction techniques, such as PCA, ICA, KPCA, MSPCA, entropy measures, and other statistical measures, and more. This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need a cogent resource on the most recent and promising machine learning techniques for biomedical signals analysis.-- Source other than Library of Congress.

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