Application of Electromyography Technology to Predict Chronic Diseases for COVID-19 Patients

Rafid A.  Doulab; Esraa A.  Qory

doi:10.18502/fbt.v12i4.19820

Rafid A. Doulab Electron Microscope Unit, College of Pharmacy, University of Basrah, Basrah, Iraq
Esraa A. Qory Electron Microscope Unit, College of Pharmacy, University of Basrah, Basrah, Iraq

DOI: https://doi.org/10.18502/fbt.v12i4.19820

Keywords: Chronic Diseases; Electromyography; COVID-19; Corona-V Variant; Cardiovascular Disease

Abstract

Purpose: Electromyography (EMG) is widely used to measure grip strength to evaluate neuromuscular activity, and thus it is possible to predict the health status of the heart muscle of those infected with the Coronavirus (COVID-19) and the extent of its relationship according to gender and age.

Materials and Methods: Fifty participants, equally divided between males and females, ages 18 to 65, were recorded for muscle force in kilogram and potential for action signal in mV (3-10 KHz) using skin surface EMG models with grip strength data acquisition. The outcomes were then compared to the volunteers' health status, familial relationships, case history, and history of COVID-19 variation infection.

Results: Based on the analysis of recorded data related to force, frequency, intervals, and amplitude, it was found that females exhibited a significant variation (p<0.05) in force and frequency over 5 minutes, in contrast to males, who showed a significant variation (p<0.05), particularly after 3 minutes when both genders showed signs of fatigue. However, certain chronic diseases such as hypertension, diabetes, and sudden deaths may have contributed to these variations. Particularly, SARS.CoV-2 variant infection showed a significant variation (p<0.05) in the EMG result for the delta variant more than the omicron for females and more impact in male smokers.

Conclusion: Findings indicated that EMG testing can predict the likelihood of Cardiovascular Disease (CVD) disease and health status based on a family history of chronic diseases like hypertension, diabetes, and CVD, which are independently connected to COVID-19 variant infections in both genders.