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  1. Medical and Biological Engineering and Computing
  2. Medical and Biological Engineering and Computing : Volume 51
  3. Medical and Biological Engineering and Computing : Volume 51, Issue 5, May 2013
  4. A new and fast approach towards sEMG decomposition
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Medical and Biological Engineering and Computing : Volume 55
Medical and Biological Engineering and Computing : Volume 54
Medical and Biological Engineering and Computing : Volume 53
Medical and Biological Engineering and Computing : Volume 52
Medical and Biological Engineering and Computing : Volume 51
Medical and Biological Engineering and Computing : Volume 51, Issue 12, December 2013
Medical and Biological Engineering and Computing : Volume 51, Issue 11, November 2013
Medical and Biological Engineering and Computing : Volume 51, Issue 10, October 2013
Medical and Biological Engineering and Computing : Volume 51, Issue 9, September 2013
Medical and Biological Engineering and Computing : Volume 51, Issue 8, August 2013
Medical and Biological Engineering and Computing : Volume 51, Issue 7, July 2013
Medical and Biological Engineering and Computing : Volume 51, Issue 6, June 2013
Medical and Biological Engineering and Computing : Volume 51, Issue 5, May 2013
A comprehensive survey of wearable and wireless ECG monitoring systems for older adults
Characterization of skin dermis microcirculation in flow-mediated dilation using optical sensor with pressurization mechanism
Detection of movement-related cortical potentials based on subject-independent training
Atherosclerotic plaque tissue characterization in 2D ultrasound longitudinal carotid scans for automated classification: a paradigm for stroke risk assessment
Theoretical development and critical analysis of burst frequency equations for passive valves on centrifugal microfluidic platforms
Risk stratification of cardiac autonomic neuropathy based on multi-lag Tone–Entropy
Spatio-spectral filters for low-density surface electromyographic signal classification
Parameterisation of multi-scale continuum perfusion models from discrete vascular networks
Evaluation of feature extraction methods for EEG-based brain–computer interfaces in terms of robustness to slight changes in electrode locations
Testing pattern synchronization in coupled systems through different entropy-based measures
A new and fast approach towards sEMG decomposition
Medical and Biological Engineering and Computing : Volume 51, Issue 4, April 2013
Medical and Biological Engineering and Computing : Volume 51, Issue 3, March 2013
Medical and Biological Engineering and Computing : Volume 51, Issue 1-2, February 2013
Medical and Biological Engineering and Computing : Volume 50
Medical and Biological Engineering and Computing : Volume 49
Medical and Biological Engineering and Computing : Volume 48
Medical and Biological Engineering and Computing : Volume 47
Medical and Biological Engineering and Computing : Volume 46
Medical and Biological Engineering and Computing : Volume 45
Medical and Biological Engineering and Computing : Volume 44
Medical and Biological Engineering and Computing : Volume 43
Medical and Biological Engineering and Computing : Volume 42
Medical and Biological Engineering and Computing : Volume 41
Medical and Biological Engineering and Computing : Volume 40
Medical and Biological Engineering and Computing : Volume 39
Medical and Biological Engineering and Computing : Volume 38
Medical and Biological Engineering and Computing : Volume 37
Medical and Biological Engineering and Computing : Volume 36
Medical and Biological Engineering and Computing : Volume 35

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A new and fast approach towards sEMG decomposition

Content Provider SpringerLink
Author Gligorijević, Ivan Dijk, Johannes P. Mijović, Bogdan Huffel, Sabine Blok, Joleen H. Vos, Maarten
Copyright Year 2013
Abstract 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 16:1661–1687, 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’s 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.
Starting Page 593
Ending Page 605
Page Count 13
File Format PDF
ISSN 01400118
Journal Medical and Biological Engineering and Computing
Volume Number 51
Issue Number 5
e-ISSN 17410444
Language English
Publisher Springer-Verlag
Publisher Date 2013-01-18
Publisher Place Berlin, Heidelberg
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Surface EMG Decomposition HD-sEMG Motor unit action potential Multichannel Superposition Alignment Human Physiology Biomedical Engineering Imaging Radiology Computer Applications
Content Type Text
Resource Type Article
Subject Biomedical Engineering Computer Science Applications
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