An Approach for the Preprocessing of EMG Signals Using Canonical Correlation Analysis
| Autor: | Deeksha Anand, Deepak Kumar Tiwari, Ashita Srivastava, Vikrant Bhateja |
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| Rok vydání: | 2017 |
| Předmět: |
Structuring element
business.industry Computer science 0206 medical engineering Morphological filtering Pattern recognition 02 engineering and technology Overlay 020601 biomedical engineering Signal 030218 nuclear medicine & medical imaging 03 medical and health sciences symbols.namesake 0302 clinical medicine Additive white Gaussian noise symbols Preprocessor Artificial intelligence Canonical correlation business |
| Zdroj: | Smart Computing and Informatics ISBN: 9789811055461 |
| Popis: | EMG signals are generally contaminated by various kinds of noises in a heterogeneous way. Among these various noises, major issue is the proper removal of Additive White Gaussian Noise (AWGN), whose spectral components overlay the spectrum of EMG signals; making its analysis troublesome. This paper presents an approach for AWGN removal from the EMG signal using Canonical Correlation Analysis (CCA). In this approach, CCA is first performed on the noisy EMG signals to break them into various canonical components followed by Morphological Filtering. Herein, a square-shaped structuring element is deployed which filters the canonical components. After that, the outcomes of the proposed methodology are contemplated with the approaches adopted in CCA-Gaussian filtering and CCA-thresholding. Outcomes of simulations show that the preprocessing approach used in this work suppresses AWGN from EMG signal while preserving the original content. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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