Methods Inf Med 2015; 54(03): 221-226
DOI: 10.3414/ME13-02-0049
Focus Theme – Original Articles
Schattauer GmbH

Motor Unit Tracking Using High Density Surface Electromyography (HDsEMG)

Automated Correction of Electrode Displacement Errors
I. Gligorijević
1   Department of Electrical Engineering-ESAT, STADIUS, KU Leuven, Leuven, Belgium
2   iMinds Future Health Department, KU Leuven, Leuven, Belgium
3   Faculty of Engineering Sciences, University of Kragujevac, Kragujevac, Serbia
,
B. T. H. M. Sleutjes
4   Department of Clinical Neurophysiology, Erasmus MC, UMC Rotterdam, Rotterdam, The Netherlands
,
M. De Vos
1   Department of Electrical Engineering-ESAT, STADIUS, KU Leuven, Leuven, Belgium
2   iMinds Future Health Department, KU Leuven, Leuven, Belgium
5   Neuroscience Lab, Dep. of psychology, Oldenburg University, Oldenburg, Germany
,
J. H. Blok
4   Department of Clinical Neurophysiology, Erasmus MC, UMC Rotterdam, Rotterdam, The Netherlands
6   Department of Clinical Physics, Reinier de Graaf Hospital, Delft, The Netherlands
,
I. Montfoort
4   Department of Clinical Neurophysiology, Erasmus MC, UMC Rotterdam, Rotterdam, The Netherlands
,
B. Mijović
1   Department of Electrical Engineering-ESAT, STADIUS, KU Leuven, Leuven, Belgium
2   iMinds Future Health Department, KU Leuven, Leuven, Belgium
,
M. Signoretto
1   Department of Electrical Engineering-ESAT, STADIUS, KU Leuven, Leuven, Belgium
2   iMinds Future Health Department, KU Leuven, Leuven, Belgium
,
S. Van Huffel
1   Department of Electrical Engineering-ESAT, STADIUS, KU Leuven, Leuven, Belgium
2   iMinds Future Health Department, KU Leuven, Leuven, Belgium
› Author Affiliations
Further Information

Publication History

received: 01 November 2013

accepted: 07 August 2014

Publication Date:
22 January 2018 (online)

Summary

Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Biosignal Interpretation: Advanced Methods for Neural Signals and Images”.

Objectives: The study discusses a technique to automatically correct for effects of electrode grid displacement across serial surface EMG measurements with high-density electrode arrays (HDsEMG). The goal is to match motor unit signatures from subsequent measurements and by this, achieve automated motor unit tracking.

Methods: Test recordings of voluntary muscle contractions using HDsEMG were performed on three healthy individuals. Electrode grid displacements were mimicked in repeated recordings while measuring the exact position of the grid. A concept of accounting for translational and rotational displacements by making the projection of the recorded motor unit action potentials is first introduced. Then, this concept was tested for the performed measurements attempting the automated matching of the similar motor unit action potentials across different trials.

Results: The ability to perform automated correction (projection) of the isolated motor unit action potentials was first shown using large angular displacements. Then, for accidental (small) displacements of the recording grid, the ability to automatically track motor units across different measurement trials was shown. It was possible to track 10 –15% of identified motor units.

Conclusions: This proof of concept study demonstrates an automated correction allowing the identification of an increased number of same motor unit action potentials across different measurements. By this, great potential is demonstrated for assisting motor unit tracking studies, indicating that otherwise electrode displacements cannot always be precisely described.

 
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