title: Application of the Teager-Kaiser Energy Operator in an autonomous burst detector to create onset and offset profiles of forearm muscles during reach-to-grasp movements.
abstract: OBJECTIVE
  The primary aim of this study is to investigate the potential benefit of the Teager-Kaiser Energy Operator (TKEO) as data pre-processor, in an autonomous burst detection method to classify electromyographic signals of the (fore)arm and hand. For this purpose, optimal settings of the burst detector, leading to minimal detection errors, need to be known. Additionally, the burst detector is applied to real muscle activity recorded in healthy adults performing reach-to-grasp movements.
  
  
  METHODS
  The burst detector was based on the Approximated Generalized Likelihood Ratio (AGLR). Simulations with synthesized electromyographic (EMG) traces with known onset and offset times, yielded optimal settings for AGLR parameters "window width" and "threshold value" that minimized detection errors. Next, comparative simulations were done with and without TKEO data pre-processing. Correct working of the burst detector was verified by applying it to real surface EMG signals obtained from arm and hand muscles involved in a submaximal reach-to-grasp task, performed by healthy adults.
  
  
  RESULTS
  Minimal detection errors were found with a window width of 100 ms and a detection threshold of 15. Inclusion of the TKEO contributed significantly to a reduction of detection errors. Application of the autonomous burst detector to real data was feasible.
  
  
  CONCLUSIONS
  The burst detector was able to classify muscle activation and create Muscle Onset Offset Profiles (MOOPs) autonomously from real EMG data, which allows objective comparison of MOOPs obtained from movement tasks performed in different conditions or from different populations. The TKEO contributed to improved performance and robustness of the burst detector.
authors.affiliation: Roessingh Research and Development, Enschede, the Netherlands.|Department of Biomechanical Engineering, University of Twente, the Netherlands.
authors.forename: Thijs
authors.initials: T
authors.lastname: Krabben
authors.affiliation: Roessingh Research and Development, Enschede, the Netherlands.|Department of Biomechanical Engineering, University of Twente, the Netherlands.
authors.forename: Gerdienke B
authors.initials: GB
authors.lastname: Prange
authors.affiliation: Roessingh Research and Development, Enschede, the Netherlands.
authors.forename: Hermen J
authors.initials: HJ
authors.lastname: Kobus
authors.affiliation: Roessingh Research and Development, Enschede, the Netherlands.|Department of Biomechanical Engineering, University of Twente, the Netherlands.|Rehabilitation Centre "het Roessingh", Enschede, the Netherlands.|Medical Spectrum Twente, Enschede, the Netherlands.
authors.forename: Johan S
authors.initials: JS
authors.lastname: Rietman
authors.affiliation: Roessingh Research and Development, Enschede, the Netherlands.|Rehabilitation Centre "het Roessingh", Enschede, the Netherlands.|Department of Biomedical Signals and Systems, University of Twente, Enschede, the Netherlands.
authors.forename: Jaap H
authors.initials: JH
authors.lastname: Buurke
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D004576:Electromyography / Q000379:methods*; D005260:Female; D005542:Forearm / Q000502:physiology*; D018737:Hand Strength / Q000502:physiology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D009048:Motor Skills / Q000502:physiology; D009068:Movement / Q000502:physiology*; D009119:Muscle Contraction / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted