Literature DB >> 20699205

EMGTools, an adaptive and versatile tool for detailed EMG analysis.

Miki Nikolic1, Christian Krarup.   

Abstract

We have developed an electromyography (EMG) decomposition system called EMGTools that can extract the constituent MUAPs and firing patterns (FPs) for quantitative analysis from the EMG signal recorded at slight effort for clinical evaluation. The aim was to implement a robust system able to handle the challenges and variations in clinically recorded signals. The system extracts MUAPs recorded by concentric needle electrodes and resolves superimposed MUAPs to produce FPs. Thus, critical fixed thresholds/parameters are avoided and replaced with adaptive solutions. The decomposition algorithm consists of three stages: segmentation, clustering, and resolution of compound segments. The results are validated using three different methods, comparing mean MUAP duration with previous methods, comparing dual channel recordings, and assessing the residual signal after decomposition. The advantages and limitations of the system are discussed.

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Mesh:

Year:  2010        PMID: 20699205     DOI: 10.1109/TBME.2010.2064773

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  6 in total

1.  Transient impairment of the axolemma following regional anaesthesia by lidocaine in humans.

Authors:  Mihai Moldovan; Kai Henrik Wiborg Lange; Niels Jacob Aachmann-Andersen; Troels Wesenberg Kjær; Niels Vidiendal Olsen; Christian Krarup
Journal:  J Physiol       Date:  2014-04-07       Impact factor: 5.182

2.  Comparative Analysis of Wavelet-based Feature Extraction for Intramuscular EMG Signal Decomposition.

Authors:  M Ghofrani Jahromi; H Parsaei; A Zamani; M Dehbozorgi
Journal:  J Biomed Phys Eng       Date:  2017-12-01

3.  An Android Application for Estimating Muscle Onset Latency using Surface EMG Signal.

Authors:  M Karimpour; H Parsaei; Z Rojhani-Shirazi; R Sharifian; F Yazdani
Journal:  J Biomed Phys Eng       Date:  2019-04-01

4.  A Hybrid FPGA-Based System for EEG- and EMG-Based Online Movement Prediction.

Authors:  Hendrik Wöhrle; Marc Tabie; Su Kyoung Kim; Frank Kirchner; Elsa Andrea Kirchner
Journal:  Sensors (Basel)       Date:  2017-07-03       Impact factor: 3.576

5.  Intramuscular EMG Decomposition Basing on Motor Unit Action Potentials Detection and Superposition Resolution.

Authors:  Xiaomei Ren; Chuan Zhang; Xuhong Li; Gang Yang; Thomas Potter; Yingchun Zhang
Journal:  Front Neurol       Date:  2018-01-23       Impact factor: 4.003

6.  Comparison of Bagging and Boosting Ensemble Machine Learning Methods for Automated EMG Signal Classification.

Authors:  Emine Yaman; Abdulhamit Subasi
Journal:  Biomed Res Int       Date:  2019-10-31       Impact factor: 3.411

  6 in total

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