Related Experiment Video
Updated: Dec 21, 2025
Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
Published on: January 24, 2025
Scalogram-energy based segmentation of surface electromyography signals from swallowing related muscles
Roldan-Vasco Sebastian1, Perez-Giraldo Estefania2, Orozco-Duque Andres2
1Grupo de Investigación en Materiales Avanzados y Energía, Facultad de Ingenierías, Instituto Tecnológico Metropolitano, Medellín, Colombia; Grupo de Investigación en Telecomunicaciones Aplicadas, Facultad de Ingeniería, Universidad de Antioquia, Medellín, Colombia.
Background And Objective:
The swallowing is a complex process mediated by the central nervous system, that implies voluntary and involuntary components, including 26 pairs of muscles. Non-invasive strategies, including the surface electromyography (sEMG), have been proposed to evaluate the swallowing. However, such analyses have been mostly descriptive, and the detection of neuromuscular activity has been limited to the visual inspection (VIS). Nonetheless, the VIS lacks reliability since the swallowing related muscles have small size, they are not completely shallow, suffer from cross-talk and have low signal-to-noise ratio (SNR). In this way, we propose a wavelet based method to automatically detect activations in sEMG signals acquired during praxis and swallowing tasks.
Methods:
The proposed strategy, namely Scalogram-Energy based Segmentation method, was applied on sEMG signals recorded in masseteric, orbicular, supra- and infrahyoid muscles. The method was trained in a database of 35 healthy subjects by the use of multi-objective genetic algorithms and tested via cross-validation, aiming to maximize the F1 score and minimize the timing error between the automatic and VIS related marks. Furthermore, the proposed method was tested in a database of semi-synthetic signals with variable SNR built from signals collected from 10 individuals. Additionally, the method was compared with a double threshold based algorithm as well as with other based on energy and morphological operators.
Results:
The algorithm achieved a F1 score of 0.82 and almost 13 ms of error in the estimation of onset and offset. Afterwards, we applied the optimized algorithm to a set with semi-synthetic signals with variable SNR, that achieved F1 score of 0.85 for SNR=6 dB and 0.97 for SNR=8 and 10 dB. The mean of the timing error was smaller than 9 ms for SNR=6,8 and 10 dB. The method was also compared with a double threshold based algorithm as well as with other based on energy and morphological operators.
Conclusions:
The proposed method shown to be useful to automatically analyze the electrophysiological activity associated to praxis and swallowing process. Nonetheless, the obtained results could be extended to other sEMG related applications.
