Diversity-Based Hybrid Classifier Fusion - Rasheed, Sarbast
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Revision with unchanged content. 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 indivi dual 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…mehr

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Produktbeschreibung
Revision with unchanged content. 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 indivi dual 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 realsignals 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.
Autorenporträt
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.