Scalogram-energy based segmentation of surface electromyography signals from swallowing related muscles. (October 2020)

• Record Type: Journal Article
• Authors: Sebastian, Roldan-Vasco; Estefania, Perez-Giraldo; Andres, Orozco-Duque
• Is Part Of: Computer methods and programs in biomedicine. Volume 194(2020)
• Publication Date: 2020-10
• Subjects: Swallowing -- Segmentation -- Surface electromyography -- sEMG -- Detection algorithm -- Wavelet transform; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105480
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The swallowing process could be evaluated by non invasive methods such as the surface electromyography (sEMG). Swallowing related muscles are hard to assess via sEMG due to small size, depth, and cross-talk that reduces the signal-to-noise ratio (SNR). The Scalogram-Energy based Segmentation is suitable to detect activations in swallowing related sEMG signals despite of the low SNR. Automatic segmentation is helpful to assess the swallowing process. Abstract: 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 F 1 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 F 1 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 F 1 score of 0.85 for S N R = 6 dB and 0.97 for S N R = 8 and 10 dB. The mean of the timing error was smaller than 9 ms for S N R = 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.