Detection of genioglossus myoelectric activity using ICA of multi-channel mandible sEMG
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Published on: April 21, 2020
Tianyi Song1,2, Baowen Meng1, Baoming Chen3
1School of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi, China.
Background:
Genioglossus myoelectric activity is of great significance in evaluating clinical respiratory function. However, there is a tradeoff in genioglossus EMG measurement with respect to accuracy versus convenience.
Objective:
This paper presents a way to separate the characteristics of genioglossus myoelectric activity from multi-channel mandible sEMG through independent component analysis.
Methods:
First, intra-oral genioglossus EMGgenioglossus EMG and three-channel mandible sEMG were recorded simultaneously. The FastICA algorithm was applied to three independent components from the sEMG signals. Then the independent components with the intra-oral genioglossus EMG were compared by calculating the Pearson correlation coefficient between them.
Results:
An examination of 60 EMG samples showed that the FastICA algorithm was effective in separating the characteristics of genioglossus myoelectric activity from multi-channel mandible sEMG. The results of analysis were coincident with clinical diagnosis through intra-oral electrodes.
Conclusions:
Genioglossus myoelectric activity can be evaluated accurately by multi-channel mandible sEMG, which is non-invasive and easy to record.
