Sebastian Roldan-Vasco, Estefania Perez-Giraldo, Andres Orozco-Duque
Grupo 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.
Grupo de Investigación e Innovación Biomédica, Facultad de Ciencias Exactas y Aplicadas, Instituto Tecnológico Metropolitano, Medellín, Colombia.
Comput Methods Programs Biomed. 2020 Oct;194:105480. doi: 10.1016/j.cmpb.2020.105480. Epub 2020 Apr 25.
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.
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 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.
The algorithm achieved a F 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 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.
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.
吞咽是一个由中枢神经系统介导的复杂过程，涉及自主和非自主成分，包括26对肌肉。已经提出了包括表面肌电图（sEMG）在内的非侵入性策略来评估吞咽。然而，此类分析大多是描述性的，并且神经肌肉活动的检测仅限于目视检查（VIS）。尽管如此，VIS缺乏可靠性，因为与吞咽相关的肌肉尺寸小，并非完全浅表，存在串扰且信噪比（SNR）低。因此，我们提出一种基于小波的方法来自动检测在实践和吞咽任务期间采集的sEMG信号中的激活。
所提出的策略，即基于小波能量图的分割方法，应用于在咬肌、口轮匝肌、舌骨上下肌群记录的sEMG信号。该方法通过使用多目标遗传算法在35名健康受试者的数据库中进行训练，并通过交叉验证进行测试，旨在最大化F分数并最小化自动标记与VIS相关标记之间的时间误差。此外，所提出的方法在由从10个人收集的信号构建的具有可变SNR的半合成信号数据库中进行测试。另外，该方法与基于双阈值的算法以及基于能量和形态学算子的其他算法进行比较。
该算法在估计起始和偏移时的F分数为0.82，误差约为13毫秒。之后，我们将优化后的算法应用于一组具有可变SNR的半合成信号，对于SNR = 6 dB，F分数为0.85，对于SNR = 8和10 dB，F分数为0.97。对于SNR = 6、8和10 dB，时间误差的平均值小于9毫秒。该方法还与基于双阈值的算法以及基于能量和形态学算子的其他算法进行比较。
所提出的方法被证明有助于自动分析与实践和吞咽过程相关的电生理活动。尽管如此，所获得的结果可以扩展到其他与sEMG相关的应用。
