Discrete wavelet transform based processing of embroidered textile-electrode electromyography signal acquired with load and pressure effect

The diagnosis of neuromuscular diseases is complicated by overlapping symptoms from other conditions. Textile-based surface electromyography (sEMG) of skeletal muscles, offer promising potential in diagnosis, treatment, and rehabilitation of various neuromuscular disorders. However, it is important to consider the impact of load and pressure on EMG signals, as this can significantly affect the signal’s accuracy. This study seeks to investigate the influence of load and pressure on EMG signals and establish a processing framework for these signals in the diagnosis of neuromuscular diseases. The sEMG data were collected from healthy subjects using a textile electrode developed from polyester multi-filament conductive hybrid thread (CleverTex). The textrode was embroidered directly on an elastic bandage (Velcro® strap) placed on volunteer’s muscles while different activities were performed with varying loads and pressure. The collected data were pre-processed using standard techniques of the discrete wavelet transform to remove noise and artifacts. The performance of the proposed denoising algorithm was evaluated using the signal-to-noise ratio (SNR), percentage root mean square difference (PRD), and root mean square error (RMSE). Various signal processing approaches (filters) were considered and the results were compared with the proposed EMG noise reduction algorithms. Based on the experimental results, the fourth level of decomposition for the sym5 wavelets with the Rigrsure threshold method achieved the highest signal-to-noise ratio (SNR) values of 16.69 and 21.91, for soft and hard thresholding functions, respectively. The SNR values of 22.11, 21.54, and 2.78 at three different pressure levels 5 mmHg, 10 mmHg, and 20 mmHg, respectively, indicate the superior performance of wavelet multiresolution filter in de-noising applications. The results of this study suggest that our methodology is effective, precise, and reliable for analysing sEMG data and provide insights into both physiological and pathological neuromuscular conditions.