Non-Invasive Muscular Atrophy Causes Evaluation for Limb Fracture Based on Flexible Surface Electromyography System

Abstract

Muscular atrophy after limb fracture is a frequently occurring complication with multiple causes. Different treatments and targeted rehabilitation procedures should be carried out based on the causes. However, bedside evaluation methods are invasive in clinical practice nowadays, lacking reliable non-invasive methods. In this study, we propose a non-invasive flexible surface electromyography system with machine learning algorithms to distinguish nerve-injury and limb immobilization-related atrophy. First, a flexible surface electromyography sensor was designed and verified by in vitro tests for its robustness and flexibility. Then, in vivo tests on rats proved the reliability compared with the traditional invasive diagnosis method. Finally, this system was applied for the diagnosis of muscular atrophy in 10 patients. The flexible surface electromyography sensor can achieve a max strain of 12.0%, which ensures close contact with the skin. The in vivo tests on rats show great comparability with the traditional invasive diagnosis method. It can achieve a high specificity of 95.28% and sensitivity of 98.98%. Application on patients reaches a relatively high specificity of 89.44% and sensitivity of 91.94%. The proposed painless surface electromyography system can be an easy and accurate supplementary for bedside muscular atrophy causes evaluation, holding excellent contact with the body.

Beyond Tissue replacement: The Emerging role of smart implants in healthcare

“…It is used to aid in muscle recovery post-injury or surgery. Non-invasive methods are the most popular approach because invasive techniques are accompanied by painful sensations and do not allow long-term data collection [ 161 ]. However, invasive techniques offer more precise stimulation of specific muscles.…”

A review on the recent developments in design and integration of electromyography textile electrodes for biosignal monitoring

“…117 The noninvasive flexible sensor was successfully used to monitor patients having limb fractures. 118 In the same study, EMG was recorded on animals like rats using flexible wearable sensors. This work used machine learning based on various parameters such as, RMS, iEMG, ARV in time-domain features, and MDF, MPF, entropy and recurrence quantification analysis (RQA), in frequency-domain.…”

“…The studies relied on the prior knowledge that muscle strength has a strong correlation with RMS and iEMG, and muscle fatigue is correlated with MDF and MPF. 118 Azadeh Soroudi et al, developed an elastic paste that is electro-conductive, compatible with skin and are applied onto textile electrodes. The elastic paste enhanced the stability of the electrode surface in the presence of external touch, uniformity and smoothness.…”