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2018 | OriginalPaper | Buchkapitel

AWGN Suppression Algorithm in EMG Signals Using Ensemble Empirical Mode Decomposition

verfasst von : Ashita Srivastava, Vikrant Bhateja, Deepak Kumar Tiwari, Deeksha Anand

Erschienen in: Intelligent Computing and Information and Communication

Verlag: Springer Singapore

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Abstract

Surface Electromyogram (EMG) signals are often contaminated by background interferences or noises, imposing difficulties for myoelectric control. Among these, a major concern is the effective suppression of Additive White Gaussian Noise (AWGN), whose spectral components coincide with the spectrum of EMG signals; making its analysis problematic. This paper presents an algorithm for the minimization of AWGN from the EMG signal using Ensemble Empirical Mode Decomposition (EEMD). In this methodology, EEMD is first applied on the corrupted EMG signals to decompose them into various Intrinsic Mode Functions (IMFs) followed by Morphological Filtering. Herein, a square-shaped structuring element is employed for requisite filtering of each of the IMFs. The outcomes of the proposed methodology are found improved when compared with those of conventional EMD-and EEMD-based approaches.

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Metadaten
Titel
AWGN Suppression Algorithm in EMG Signals Using Ensemble Empirical Mode Decomposition
verfasst von
Ashita Srivastava
Vikrant Bhateja
Deepak Kumar Tiwari
Deeksha Anand
Copyright-Jahr
2018
Verlag
Springer Singapore
Buch
Intelligent Computing and Information and Communication

Print ISBN: 978-981-10-7244-4

Electronic ISBN: 978-981-10-7245-1

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-981-10-7245-1_50

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