EMG Signal Classification Using Support Vector Discriminant Analysis - Yousif, Ahmed
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Classification of neuromuscular disorders using the intramuscular Electromyograph signals was obtained by improve the quality of the signal before feature extraction, and optimize the feature space to provide better discrimination ability. The signal quality evaluation was considered based on determining the best wavelet function for Electromyograph identification and denoising. Optimizing the feature space was performed based on the supervised feature projection method, the Support Vector Discriminant Aanalysis.

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Produktbeschreibung
Classification of neuromuscular disorders using the intramuscular Electromyograph signals was obtained by improve the quality of the signal before feature extraction, and optimize the feature space to provide better discrimination ability. The signal quality evaluation was considered based on determining the best wavelet function for Electromyograph identification and denoising. Optimizing the feature space was performed based on the supervised feature projection method, the Support Vector Discriminant Aanalysis.
Autorenporträt
Ahmed Yousif, Education:Master degree in Biomedical Engineering from Cairo University, 2013.Bachelor degree in Biomedical Engineering from Sudan university of science and technology, 2007.