Biased instantaneous regional muscle activation maps: Embedded fuzzy topology and image feature analysis - De la Fuente, Carlos; Weinstein, Alejandro; Neira, Alejandro; Valencia, Oscar; Cruz-Montecinos, Carlos; Silvestre, Rony; Pincheira, Patricio A.; Palma, Felipe; Carpes, Felipe P. (Frontiers in Bioengineering and Biotechnology, 2022)
The instantaneous spatial representation of the electrical propagation produced by the muscle contraction may introduce bias in the surface electromyographical (sEMG) activation maps. Here, we described the effect of instantaneous spatial representation (sEMG segmentation) on embedded fuzzy topological polyhedrons and image features extracted from sEMG activation maps. We analyzed 73,008 topographic sEMG activation maps from seven healthy participants (age 21.4 ± 1.5 years and body mass 74.5 ± 8.5 kg) who performed submaximal isometric plantar flexions with 64 surface electrodes placed over the Medial Gastrocnemius muscle. Windows lengths of 50, 100, 150, 250, 500, and 1000 ms and overlap of 0, 25, 50, 75, and 90% to change the sEMG map generation were tested in a factorial design (grid search). Shannon entropy and volume of global embedded tri-dimensional geometries (polyhedron projections), and Shannon entropy, location of the center (LoC), and image moments of maps were analyzed. The polyhedron volume increased when the overlap was <25% and >75%. Entropy decreases when overlap was <25% and >75%, and when window length was <100 ms and >500 ms. The LoC in the x-axis, entropy, and the histogram moments of maps showed effects for overlap (p<0.001), while LoC in the y-axis and entropy showed effects for both overlap and window length (p<0.001). In conclusion, the instantaneous sEMG maps are first affected by outer parameters of overlap followed by the length of the window. Thus, choosing window length and overlap parameters can introduce bias in sEMG activation maps, resulting in distorted regional muscle activation.