Electromyography (EMG) based classification of neuromuscular disorders using multi-layer perceptron

I. Elamvazuthi, N. H.X. Duy, Zulfiqar Ali, S. W. Su, M. K.A.Ahamed Khan, S. Parasuraman

    Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

    32 Citations (Scopus)

    Abstract

    Electromyography (EMG) signals are the measure of activity in the muscles. The aim of this study is to identify the neuromuscular disease based on EMG signals by means of classification. The neuromuscular diseases that have been identified are myopathy and neuropathy. The classification was carried out using Artificial Neural Network (ANN). There are five feature extraction techniques that were used to extract the signals such as Autoregressive (AR), Root Mean Square (RMS), Zero Crossing (ZC), Waveform length (WL) and Mean Absolute Value (MAV). A comparative analysis of these different techniques were carried out based on the results. The Multilayer Perceptron (MLP) was used for carrying out the classification.

    Original languageEnglish
    Title of host publicationIEEE International Symposium on Robotics and Intelligent Sensors, IEEE IRIS 2015
    Pages223-228
    Number of pages6
    Volume76
    DOIs
    Publication statusPublished - 2015
    EventIEEE International Symposium on Robotics and Intelligent Sensors 2015 - Langkawi, Malaysia
    Duration: 18 Oct 201520 Oct 2015
    https://www.sciencedirect.com/journal/procedia-computer-science/vol/76/suppl/C (Proceedings)

    Publication series

    NameProcedia Computer Science
    PublisherElsevier
    ISSN (Print)1877-0509

    Conference

    ConferenceIEEE International Symposium on Robotics and Intelligent Sensors 2015
    Abbreviated titleIRIS 2015
    CountryMalaysia
    CityLangkawi
    Period18/10/1520/10/15
    Internet address

    Keywords

    • Autoregressive method (AR)
    • Classification
    • Electromyography (EMG)
    • Feature Extraction
    • Multilayer Perceptron (MLP)
    • Neuromuscular Disease
    • Root mean square (RMS)
    • Waveform length (WL) and Mean Absolute Value (MAV)
    • Zero Crossing (ZC)

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