Combination method for denoising EMG signals using EWT and EMD techniques
Samir Elouaham, Azzedine Dliou, Boujemaa Nassiri, Hicham Zougagh
Abstract
Electromyography (EMG) is a diagnostic tool commonly used to assess the electrical activity of muscles. This test can help diagnose various neuromuscular conditions, evaluate muscle function, and determine the health and integrity of muscles. However, the electromyography (EMG) technique is often affected by various types of noise, making it challenging to diagnose muscle issues accurately. It is crucial to have an EMG signal without any noise in order to ensure the most accurate evaluation and interpretation. The aim of this study is to provide a hybrid denoising method that combines two techniques, namely the empirical wavelet transforms (EWT) and empirical mode decomposition (EMD). This hybrid approach combines the strengths of both methods. By utilizing the EWT-EMD method, researchers and practitioners can improve the accuracy of their signal analysis. In this research, we utilized Discrete wavelet transform (DWT) and proposed method EWT-EMD for denoising electromyography (EMG) signals. This method has proven to be practical and advantageous compared to other methods and it offers improved noise removal while preserving important signal characteristics. This makes it a promising solution for denoising EMG signals, enabling accurate analysis in both clinical and research settings.