A new and fast approach towards sEMG decomposition

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• 作者：Ivan Gligorijevi膰 (1) (2)
  Johannes P. van Dijk (3) (4)
  Bogdan Mijovi膰 (1) (2)
  Sabine Van Huffel (1) (2)
  Joleen H. Blok (5)
  Maarten De Vos (1) (2) (6)
  
• 关键词：Surface EMG ; Decomposition ; HD ; sEMG ; Motor unit action potential ; Multichannel ; Superposition ; Alignment
• 刊名：Medical & Biological Engineering & Computing
• 出版年：2013
• 出版时间：May 2013
• 年：2013
• 卷：51
• 期：5
• 页码：593-605
• 全文大小：1088KB
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• 作者单位：Ivan Gligorijevi膰 (1) (2)
  Johannes P. van Dijk (3) (4)
  Bogdan Mijovi膰 (1) (2)
  Sabine Van Huffel (1) (2)
  Joleen H. Blok (5)
  Maarten De Vos (1) (2) (6)
  
  1. Department of Electrical Engineering, SCD-SISTA, KU Leuven, Kasteelpark Arenberg 10, 3001, Leuven, Belgium
  2. IBBT Future Health Department, SCD-SISTA, KU Leuven, 3001, Leuven, Belgium
  3. Department of Neurology/Clinical Neurophysiology, Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen Medical Centre, PO Box 9101, 6500 HB, Nijmegen, The Netherlands
  4. Department of Orthodontics, Center of Dentistry, Ulm University, Medical Centre, Ulm, Germany
  5. Department of Clinical Neurophysiology, Erasmus MC Rotterdam, Rotterdam, The Netherlands
  6. Department of Psychology, Neuroscience Lab, Oldenburg University, Oldenburg, Germany
  
• ISSN：1741-0444

文摘

The decomposition of high-density surface EMG (HD-sEMG) interference patterns into the contribution of motor units is still a challenging task. We introduce a new, fast solution to this problem. The method uses a data-driven approach for selecting a set of electrodes to enable discrimination of present motor unit action potentials (MUAPs). Then, using shapes detected on these channels, the hierarchical clustering algorithm as reported by Quian Quiroga et al. (Neural Comput 16:1661鈥?687, 2004) is extended for multichannel data in order to obtain the motor unit action potential (MUAP) signatures. After this first step, more motor unit firings are obtained using the extracted signatures by a novel demixing technique. In this demixing stage, we propose a time-efficient solution for the general convolutive system that models the motor unit firings on the HD-sEMG grid. We constrain this system by using the extracted signatures as prior knowledge and reconstruct the firing patterns in a computationally efficient way. The algorithm performance is successfully verified on simulated data containing up to 20 different MUAP signatures. Moreover, we tested the method on real low contraction recordings from the lateral vastus leg muscle by comparing the algorithm鈥檚 output to the results obtained by manual analysis of the data from two independent trained operators. The proposed method showed to perform about equally successful as the operators.