Literature DB >> 16890457

Location of innervation zone determined with multichannel surface electromyography using an optical flow technique.

Nils Ostlund1, Björn Gerdle, J Stefan Karlsson.   

Abstract

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 2.4mm from the location given by an expert group. The lowest RMS method gave a significantly higher deviation (13.6mm). 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.

Mesh:

Year:  2006        PMID: 16890457     DOI: 10.1016/j.jelekin.2006.06.002

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


  7 in total

1.  Global Innervation Zone Identification With High-Density Surface Electromyography.

Authors:  Chuan Zhang; Nicholas Dias; Jinbao He; Ping Zhou; Sheng Li; Yingchun Zhang
Journal:  IEEE Trans Biomed Eng       Date:  2019-05-30       Impact factor: 4.538

2.  Imaging three-dimensional innervation zone distribution in muscles from M-wave recordings.

Authors:  Chuan Zhang; Yun Peng; Yang Liu; Sheng Li; Ping Zhou; William Zev Rymer; Yingchun Zhang
Journal:  J Neural Eng       Date:  2017-03-30       Impact factor: 5.379

3.  Mapping the spatiotemporal dynamics of calcium signaling in cellular neural networks using optical flow.

Authors:  Marius Buibas; Diana Yu; Krystal Nizar; Gabriel A Silva
Journal:  Ann Biomed Eng       Date:  2010-03-19       Impact factor: 3.934

4.  Distribution of innervation zone and muscle fiber conduction velocity in the biceps brachii muscle.

Authors:  Xiaoyan Li; Chengjun Huang; Zhiyuan Lu; Inga Wang; Cliff S Klein; Liqun Zhang; Ping Zhou
Journal:  J Electromyogr Kinesiol       Date:  2022-02-04       Impact factor: 2.368

5.  Motor unit innervation zone localization based on robust linear regression analysis.

Authors:  Jie Liu; Sheng Li; Faezeh Jahanmiri-Nezhad; William Zev Rymer; Ping Zhou
Journal:  Comput Biol Med       Date:  2019-01-14       Impact factor: 4.589

6.  Detection of Multiple Innervation Zones from Multi-Channel Surface EMG Recordings with Low Signal-to-Noise Ratio Using Graph-Cut Segmentation.

Authors:  Hamid Reza Marateb; Morteza Farahi; Monica Rojas; Miguel Angel Mañanas; Dario Farina
Journal:  PLoS One       Date:  2016-12-15       Impact factor: 3.240

7.  Neurophysiological Factors Affecting Muscle Innervation Zone Estimation Using Surface EMG: A Simulation Study.

Authors:  Chengjun Huang; Maoqi Chen; Xiaoyan Li; Yingchun Zhang; Sheng Li; Ping Zhou
Journal:  Biosensors (Basel)       Date:  2021-09-27
  7 in total

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