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Current Signal Transduction Therapy

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ISSN (Print): 1574-3624
ISSN (Online): 2212-389X

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Research Article

Removal of Interference from Electromyogram based on Empirical Mode Decomposition and Correlation Coefficient Thresholding

Author(s): M. Karuna and Sitaramanjaneya Reddy Guntur*

Volume 19, Issue 1, 2024

Published on: 19 January, 2024

Article ID: e190124225825 Pages: 9

DOI: 10.2174/0115743624268804231222042118

Price: $65

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Abstract

Introduction: Electromyography (EMG) signals are contaminated by various noise components. These noises directly degrade the EMG processing performance, thereby affecting the classification accuracy of the EMG signals for implementing various hand movements of the prosthetic arm from the amputee’s residual muscle.

Methods: This study mainly aims to denoise the EMG signals using the empirical mode decomposition (EMD) and correlation coefficient thresholding (CCT) methods. The noisy EMG signal is obtained from NinaPro Database 2. Then, EMD is used to decompose it into intrinsic mode functions. Each hand movement noise is identified within specific modes and removed separately using correlation coefficient–dependent thresholding and wavelet denoising. The performance metrics signal-to-noise ratio (SNR) and root mean square error (RMSE) were used to evaluate the noise removal performance from the EMG signals of five intact subjects. The proposed method outperforms the wavelet denoising method in terms of noise interference removal. In this method, the SNR is obtained in the 17-22 dB range with a very low RMSE.

Results: The experimental results illustrate that the proposed method removes noise from six repetitions of six movements performed by five subjects. This study explores the special characteristics of EMD and demonstrates the possibility of using the EMD-based CCT filter for denoising EMG signals.

Conclusion: The proposed filter is more efficient than wavelet denoising in removing noise interference. It can also be used in any application that requires EMG signal filtering at the preprocessing stage, such as EMG pattern recognition.

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