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  • 1251.
    Östlund, Nils
    et al.
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Neuroscience and Locomotion, Rehabilitation Medicine.
    Gerdle, Björn
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Neuroscience and Locomotion, Rehabilitation Medicine. Östergötlands Läns Landsting, Centre for Medicine, Pain and Rehabilitation Centre.
    Karlsson, Stefan
    Umeå Universitetssjukhus.
    Location of innervation zone determined with multichannel surface electromyography using an optical flow technique2007In: Journal of Electromyography & Kinesiology, ISSN 1050-6411, E-ISSN 1873-5711, Vol. 17, no 5, p. 549-555Article in journal (Refereed)
    Abstract [en]

    Multichannel surface electromyography has developed towards more channels and higher spatial resolution. This allows the study of multichannel electromyograms as images of the potential distribution on the skin. In this paper, a method that estimates the motion of the potential distribution using an optical-flow-based technique is introduced. The optical flow is a vector field that describes how images change with time. The aim of this study was to introduce a new method for innervation zone (IZ) localization and to evaluate its performance. The new method was compared with a method that uses the position of the lowest root-mean-square (RMS) value in an electrode array as an estimate of the IZ localization. Comparisons were made with both simulated signals and with recorded multichannel electromyogram signals. Simulations showed that the methods performed similarly for high signal-to-noise ratio (SNR) and that the optical-flow-based method was superior for lower SNR. When the experimental signals were used, localization with the optical-flow-based method gave a mean absolute deviation of 2.4 mm from the location given by an expert group. The lowest RMS method gave a significantly higher deviation (13.6 mm). Due to the low computational complexity of the optical flow algorithm it is possible to get the estimations of the IZ localization in real time. © 2006 Elsevier Ltd. All rights reserved.

  • 1252.
    Östlund, Nils
    et al.
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Neuroscience and Locomotion, Rehabilitation Medicine.
    Yu, J
    Roeleveld, K
    Karlsson, J. S
    Adaptive spatial filtering of multichannel surface electromyogram signals2004In: Medical and Biological Engineering and Computing, ISSN 0140-0118, E-ISSN 1741-0444, Vol. 42, no 6, p. 825-831Article in journal (Refereed)
    Abstract [en]

    Spatial filtering of surface electromyography (EMG) signals can be used to enhance single motor unit action potentials (MUAPs). Traditional spatial filters for surface EMG do not take into consideration that some electrodes could have poor skin contact. In contrast to the traditional a priori defined filters, this study introduces an adaptive spatial filtering method that adapts to the signal characteristics. The adaptive filter, the maximum kurtosis filter (MKF), was obtained by using the linear combination of surrounding channels that maximises kurtosis. The MKF and conventional filters were applied to simulated EMG signals and to real EMG signals recorded with an electrode grid to evaluate their performance in detecting single motor units. The MKF was compared with conventional spatial filtering methods. Simulated signals, with different levels of spatially correlated noise, were used for comparison. The influence of one electrode with poor skin contact was also investigated. The MKF was found to be considerably better at enhancing a single MUAP than conventional methods for all levels of spatial correlation of the noise. For a spatial correlation of 0.97 of the noise, the improvement in the signal-to-noise ratio, where a MUAP could be detected, was at least 6 dB. With a simulated poor skin contact for one electrode, the improvement over the other methods was at least. 19 dB.

23242526 1251 - 1252 of 1252
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