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Diversity-Based Hybrid Classifier Fusion

Sarbast Rasheed

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ISBN 10: 3836435330 / ISBN 13: 9783836435338
Published by VDM Verlag Dr. Mueller e.K.
New Condition: New Soft cover
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216 pages. Dimensions: 9.6in. x 6.7in. x 0.5in.Electromyographic (EMG) signal analysis is the process of resolving a composite EMG signal into its constituent motor unit potential trains (classes) and it can be configured as a classification problem. An EMG signal detected by the tip of an inserted needle electrode is the superposition of the individual electrical contributions of the different motor units that are active, during a muscle contraction, and background interference. This book addresses the process of EMG signal decomposition by developing an interactive classification system, which uses multiple classifier fusion techniques in order to achieve improved classification performance. The developed system combines heterogeneous sets of base classifier ensembles of different kinds and employs both a one level classifier fusion scheme and a hybrid classifier fusion approach. Performance of the developed system was evaluated using synthetic simulated signals of known properties and real signals and compared with the performance of the constituent base classifiers. This book is directed toward graduate students and researchers in the area of electromyography and professionals in electromyography clinics. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Bookseller Inventory # 9783836435338

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Bibliographic Details

Title: Diversity-Based Hybrid Classifier Fusion

Publisher: VDM Verlag Dr. Mueller e.K.

Binding: Paperback

Book Condition:New

Book Type: Paperback

About this title

Synopsis:

Electromyographic (EMG) signal analysis is the process of resolving a composite EMG signal into its constituent motor unit potential trains (classes) and it can be configured as a classification problem. An EMG signal detected by the tip of an inserted needle electrode is the superposition of the indivi­dual electrical contributions of the different motor units that are active, during a muscle contraction, and background interference. This book addresses the process of EMG signal decomposition by developing an interactive classification system, which uses multiple classifier fusion techniques in order to achieve improved classification performance. The developed system combines heterogeneous sets of base classifier ensembles of different kinds and employs both a one level classifier fusion scheme and a hybrid classifier fusion approach. Performance of the developed system was evaluated using synthetic simulated signals of known properties and real signals and compared with the performance of the constituent base classifiers. This book is directed toward graduate students and researchers in the area of electromyography and professionals in electromyography clinics.

About the Author:

Dr. Sarbast Rasheed, researcher in the Biomedical Engineering area. He has a Ph.D. degree in Systems Design Engineering from the University of Waterloo, Canada. He is interested in the application of pattern recognition techniques to electromyographic (EMG) signal analysis, MEMS (microelectromechanical systems) modelling, and Object Recognition.

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