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Application of higher order statistics techniques to EMG signals to characterize the motor unit action potential

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Show simple item record Shahid, Shahjahan Walker, Jacqueline Lyons, Gerard M. Byrne, Ciaran A. Nene, Anand 2018-02-23T14:23:02Z 2018-02-23T14:23:02Z 2005
dc.identifier.issn 0018-9294
dc.description peer-reviewed en_US
dc.description.abstract The electromyographic (EMG) signal provides information about the performance of muscles and nerves. At any instant, the shape of the muscle signal, motor unit action potential (MUAP), is constant unless there is movement of the position of the electrode or biochemical changes in the muscle due to changes in contraction level. The rate of neuron pulses, whose exact times of occurrence are random in nature, is related to the time duration and force of a muscle contraction. The EMG signal can be modeled as the output signal of a filtered impulse process where the neuron firing pulses are assumed to be the input of a system whose transfer function is the motor unit action potential. Representing the neuron pulses as a point process with random times of occurrence, the higher order statistics based system reconstruction algorithm can be applied to the EMG signal to characterize the motor unit action potential. In this paper, we report results from applying a cepstrum of bispectrum based system reconstruction algorithm to real wired-ENIG (wENIG) and surface-EMG (sEMG) signals to estimate the appearance of MUAPs in the Rectus Femoris and Vastus Lateralis muscles while the muscles are at rest and in six other contraction positions. It is observed that the appearance of MUAPs estimated from any EMG (wEMG or sEMG) signal clearly shows evidence of motor unit recruitment and crosstalk, if any, due to activity in neighboring muscles. It is also found that the shape of MUAPs remains the same on loading. en_US
dc.language.iso eng en_US
dc.publisher IEEE Computer Society en_US
dc.relation.ispartofseries IEEE Transactions on Biomedical Engineering;52 (7), pp. 1195-1209
dc.rights © 2005IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. en_US
dc.subject electromyographic signals en_US
dc.subject higher order statistics theory en_US
dc.subject HOS-based blind deconvolution en_US
dc.subject motor unit action potential en_US
dc.title Application of higher order statistics techniques to EMG signals to characterize the motor unit action potential en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US 2018-02-23T14:17:23Z
dc.identifier.doi 10.1109/TBME.2005.847525
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US
dc.internal.rssid 1130548
dc.internal.copyrightchecked Yes
dc.description.status peer-reviewed

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