Literature DB >> 9785253

Estimation of the relationship between the noninvasively detected activity of single motor units and their characteristic pathological changes by modelling.

C Disselhorst-Klug1, J Silny, G Rau.   

Abstract

Neuromuscular disorders are often related to specific changes in the structure of single motor units (MUs). One approach for the detection of these changes is high-spatial-resolution EMG (HSR-EMG), which allows non-invasive recording of the activity of a single MU. Early investigations with patients suffering from various neuromuscular disorders have shown that there is a distinct difference between the HSR-EMG signals of healthy volunteers, patients with muscular disorders, and patients with neuronal disorders. In this study, the relationship between typical HSR-EMG patterns and characteristic pathological changes in the structure of the MUs is considered. Therefore, a muscle model has been developed which is adapted to the physiological properties of the m. abductor pollicis brevis. The effects of the loss of single muscle fibres (muscular disorders) and the loss of entire MUs (neuronal disorders) on the HSR-EMG pattern have been simulated. These simulations show the same HSR-EMG patterns as seen in patients and healthy volunteers. As a consequence, it can be assumed that the muscle model is an appropriate tool for the simulation of HSR-EMG signals. Furthermore, the simulation results support the hypothesis that the typical changes in the HSR-EMG pattern found in patients with neuromuscular disorders can be attributed to the characteristic changes in the structure of the MUs.

Entities:  

Mesh:

Year:  1998        PMID: 9785253     DOI: 10.1016/s1050-6411(98)00015-7

Source DB:  PubMed          Journal:  J Electromyogr Kinesiol        ISSN: 1050-6411            Impact factor:   2.368


  6 in total

1.  Single motor unit analysis from spatially filtered surface electromyogram signals. Part I: spatial selectivity.

Authors:  D Farina; E Schulte; R Merletti; G Rau; C Disselhorst-Klug
Journal:  Med Biol Eng Comput       Date:  2003-05       Impact factor: 2.602

Review 2.  Surface electromyogram signal modelling.

Authors:  K C McGill
Journal:  Med Biol Eng Comput       Date:  2004-07       Impact factor: 2.602

3.  A simulation study for a surface EMG sensor that detects distinguishable motor unit action potentials.

Authors:  Jin Lee; Alexander Adam; Carlo J De Luca
Journal:  J Neurosci Methods       Date:  2007-09-18       Impact factor: 2.390

4.  Multiscale entropy-based approach to automated surface EMG classification of neuromuscular disorders.

Authors:  Rok Istenic; Prodromos A Kaplanis; Constantinos S Pattichis; Damjan Zazula
Journal:  Med Biol Eng Comput       Date:  2010-05-21       Impact factor: 2.602

Review 5.  Fundamental Concepts of Bipolar and High-Density Surface EMG Understanding and Teaching for Clinical, Occupational, and Sport Applications: Origin, Detection, and Main Errors.

Authors:  Isabella Campanini; Andrea Merlo; Catherine Disselhorst-Klug; Luca Mesin; Silvia Muceli; Roberto Merletti
Journal:  Sensors (Basel)       Date:  2022-05-30       Impact factor: 3.847

6.  Age-Associated Changes in the Spectral and Statistical Parameters of Surface Electromyogram of Tibialis Anterior.

Authors:  Ariba Siddiqi; Sridhar Poosapadi Arjunan; Dinesh Kant Kumar
Journal:  Biomed Res Int       Date:  2016-08-17       Impact factor: 3.411

  6 in total

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