title: Detection of genioglossus myoelectric activity using ICA of multi-channel mandible sEMG.
abstract: 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.
authors.affiliation: 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.
authors.forename: Tianyi
authors.initials: T
authors.lastname: Song
authors.affiliation: School of Biomedical Engineering, Fourth Military Medical University, Xi'an, Shaanxi, China.
authors.forename: Baowen
authors.initials: B
authors.lastname: Meng
authors.affiliation: School of Physics and Electronic Engineering, Shanxi University, Taiyuan, Shanxi, China.
authors.forename: Baoming
authors.initials: B
authors.lastname: Chen
authors.affiliation: Department of Otolaryngology, Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
authors.forename: Di
authors.initials: D
authors.lastname: Zhao
authors.affiliation: Center of Aviation Medical Engineering, Institute of Aviation Medicine Beijing, Beijing, China.
authors.forename: Zhengtao
authors.initials: Z
authors.lastname: Cao
authors.affiliation: Department of Otolaryngology, Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
authors.forename: Jingying
authors.initials: J
authors.lastname: Ye
authors.affiliation: Department of Medical Engineering, General Hospital of Jinan Military Command, Jinan, Shandong, China.|Center of Aviation Medical Engineering, Institute of Aviation Medicine Beijing, Beijing, China.
authors.forename: Mengsun
authors.initials: M
authors.lastname: Yu
mesh_terms: D000328:Adult; D000465:Algorithms; D004576:Electromyography / Q000295:instrumentation*; D005260:Female; D006801:Humans; D008297:Male; D009119:Muscle Contraction / Q000502:physiology; D018482:Muscle, Skeletal / Q000503:physiopathology*; D012815:Signal Processing, Computer-Assisted; D014059:Tongue / Q000503:physiopathology*
keywords: Electromyography; genioglossus muscle; independent component analysis