Diversity-Based Hybrid Classifier Fusion
A Practical Approach to Motor Unit Potential Classification for Electromyographic Signal Decomposition
(Sprache: Englisch)
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...
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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.
Klappentext zu „Diversity-Based Hybrid Classifier Fusion “
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 configuredas 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.
Autoren-Porträt von Sarbast Rasheed
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.
Bibliographische Angaben
- Autor: Sarbast Rasheed
- 2008, 216 Seiten, Maße: 17 x 24 cm, Kartoniert (TB), Englisch
- Verlag: VDM Verlag Dr. Müller
- ISBN-10: 3836435330
- ISBN-13: 9783836435338
Sprache:
Englisch
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