Song Tianyi, Meng Baowen, Chen Baoming, Zhao Di, Cao Zhengtao, Ye Jingying, Yu Mengsun
School of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi, China.
Department of Medical Engineering, General Hospital of Jinan Military Command, Jinan, Shandong, China.
Technol Health Care. 2015;23 Suppl 2:S495-500. doi: 10.3233/THC-150987.
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.
This paper presents a way to separate the characteristics of genioglossus myoelectric activity from multi-channel mandible sEMG through independent component analysis.
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.
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.
Genioglossus myoelectric activity can be evaluated accurately by multi-channel mandible sEMG, which is non-invasive and easy to record.
颏舌肌肌电活动在评估临床呼吸功能方面具有重要意义。然而，颏舌肌肌电图测量在准确性与便利性之间存在权衡。
本文提出一种通过独立成分分析从多通道下颌表面肌电图中分离出颏舌肌肌电活动特征的方法。
首先，同时记录口内颏舌肌肌电图和三通道下颌表面肌电图。将快速独立成分分析算法应用于表面肌电信号的三个独立成分。然后通过计算它们之间的皮尔逊相关系数，比较含有口内颏舌肌肌电图的独立成分。
对60个肌电图样本的检测表明，快速独立成分分析算法能有效从多通道下颌表面肌电图中分离出颏舌肌肌电活动特征。分析结果与通过口内电极进行的临床诊断结果相符。
多通道下颌表面肌电图可准确评估颏舌肌肌电活动，其具有非侵入性且易于记录的特点。
