title: An EMG-based feature extraction method using a normalized weight vertical visibility algorithm for myopathy and neuropathy detection.
abstract: BACKGROUND
  Electromyography (EMG) signals recorded from healthy, myopathic, and amyotrophic lateral sclerosis (ALS) subjects are nonlinear, non-stationary, and similar in the time domain and the frequency domain. Therefore, it is difficult to classify these various statuses.
  
  
  METHODS
  This study proposes an EMG-based feature extraction method based on a normalized weight vertical visibility algorithm (NWVVA) for myopathy and ALS detection. In this method, sampling points or nodes based on sampling theory are extracted, and features are derived based on relations among the vertical visibility nodes with their amplitude differences as weights. The features are calculated via selective statistical mechanics and measurements, and the obtained features are assembled into a feature matrix as classifier input. Finally, powerful classifiers, such ask-nearest neighbor, multilayer perceptron neural network, and support vector machine classifiers, are utilized to differentiate signals of healthy, myopathy, and ALS cases.
  
  
  RESULTS
  Performance evaluation experiments are carried out, and the results revealed 98.36% accuracy, which corresponds to approximately a 2% improvement compared with conventional methods.
  
  
  CONCLUSIONS
  An EMG-based feature extraction method using a NWVVA is proposed and implemented to detect healthy, ALS, and myopathy statuses.
authors.affiliation: Department of Electronic and Telecommunication Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, 126 Pracha-uthit Rd., Bangmod, Thungkhru, Bangkok, 10140 Thailand.
authors.forename: Patcharin
authors.initials: P
authors.lastname: Artameeyanant
authors.affiliation: School of Business, University of the Thai Chamber of Commerce, 126/1 Vibhavadi Rd., Dindang, Bangkok, 10400 Thailand.
authors.forename: Sivarit
authors.initials: S
authors.lastname: Sultornsanee
authors.affiliation: Department of Electronic and Telecommunication Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, 126 Pracha-uthit Rd., Bangmod, Thungkhru, Bangkok, 10140 Thailand.
authors.forename: Kosin
authors.initials: K
authors.lastname: Chamnongthai
keywords: Complex network; EMG signal; Multilayer perceptron neural network; Network measurements; Normalized weight vertical visibility algorithm; Support vector machine; k-Nearest neighbor