Using Muscle Synergy to Evaluate the Neck Muscular Activities during Normal Swallowing
文献类型:会议论文
作者 | Ka Ying Karman Leung; Shixiong Chen; Guanglin Li; Mingxing Zhu; Oluwarotimi Williams Samuel; Zijian Yang; Wanhua Lin; Zhen Huang; Peng Fang; Peng Li |
出版日期 | 2018 |
会议日期 | 2018 |
会议地点 | Honolulu, Hawaii, USA |
英文摘要 | Swallowing is an extremely complex motion controlled by multiple muscles on the front neck region. Normal swallowing is dependent on orderly activation and co-coordination of the associated neck muscles, known as muscle synergy. However, evidence for muscle synergy during normal swallowing is rarely investigated. In this study, we studied the muscle synergy associated with swallowing saliva based on high-density (HD) surface electromyography (sEMG) signals acquired from four healthy subjects. The non-negative matrix factorization algorithm was applied to reconstruct the muscle activation patterns, and the values of variance accounted for (VAF) coefficients were computed to determine the number of muscle synergies. The results showed that the VAF values raised with the increase in the number of synergies on both the left and right sides of the neck. And the variation tendency of the VAF values was almost similar between the left and right area with a significant correlation (r=0.9902±0.0046, p<0.05). Furthermore, it was observed that an average of 5 muscle synergies was the minimum number required to sufficiently reconstruct the spatial characteristics of the synergism between both sides of the neck. These results suggest that the muscle synergy approach could serve as a promising candidate to evaluate the muscular co-contractions during swallowing, and it might be a useful method for dysphagia monitoring and diagnoses. |
源URL | [http://ir.siat.ac.cn:8080/handle/172644/14455] |
专题 | 深圳先进技术研究院_医工所 |
推荐引用方式 GB/T 7714 | Ka Ying Karman Leung,Shixiong Chen,Guanglin Li,et al. Using Muscle Synergy to Evaluate the Neck Muscular Activities during Normal Swallowing[C]. 见:. Honolulu, Hawaii, USA. 2018. |
入库方式: OAI收割
来源:深圳先进技术研究院
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