Non-invasive detection of the electromyographic activity of the deep extrinsic thumb muscles using surface electrodes

 

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dc.contributor.advisor John, Lester en_ZA
dc.contributor.author Pitman, Jeremy David en_ZA
dc.date.accessioned 2016-02-05T07:23:15Z
dc.date.available 2016-02-05T07:23:15Z
dc.date.issued 2015 en_ZA
dc.identifier.citation Pitman, J. 2015. Non-invasive detection of the electromyographic activity of the deep extrinsic thumb muscles using surface electrodes. University of Cape Town. en_ZA
dc.identifier.uri http://hdl.handle.net/11427/16783
dc.description Includes bibliographical references en_ZA
dc.description.abstract Motivation: Conventional surface electromyography (EMG) methods cannot be used to detect deep muscle activation. A new non-invasive superficial and deep muscle EMG (sdEMG) technique has recently been used to derive the EMG activity of Brachialis and Tibialis Posterior muscles in the upper and lower limb respectively. The aim of the present study was to apply a modified version of sdEMG to the forearm to detect EMG activity of the deep extrinsic thumb muscles Flexor Pollicis Longus (FPL), Extensor Pollicis Longus (EPL), Extensor Pollicis Brevis (EPB) and Abductor Pollicis Longus (APL) using surface electrodes. Methods: High density monopolar EMG was detected from 2 concentric rings, each consisting of 20 custom designed and manufactured silver electrodes, placed at the distal and proximal thirds of the right forearm of 15 healthy male participants. The EMG signals were recorded by a custom synthesised from open source components, EMG amplifier system interfacing with a custom designed LabVIEW® program. The participants performed 10 repetitions of isometric thumb flexion (TFl), thumb extension (TEx), thumb abduction (TAb), thumb adduction (TAd), index finger flexion (IFFl) and index finger extension (IFEx). Each isometric contraction was performed in a randomized order at a standardized effort level of 30% of the participant's maximum voluntary contraction (verified by a custom designed and built thumb dynamometer). The Independent Component Analysis (ICA) algorithm, fastICA, was used to un-mix the 40 monopolar EMG waveforms (containing EMG activity attributable to both superficial and deep muscles) into 40 constitutive components, known as the Independent Components (ICs). The activation envelope of the ICs was found using a 250ms RMS smoothing filter and normalized between 0 and 1. A contraction sequence specific predicted EMG waveform based on intramuscular measurements (from existing studies in the literature) was created for each deep muscle and correlated with the processed ICs using Pearson's Correlation Coefficient (r). The ICs were ranked according to the corresponding r value and the highest r ranked IC for each muscle was considered to represent the recovered EMG activity from that particular muscle. Finally, a per sample basis accuracy, sensitivity and specificity analysis was conducted between each deep muscle's predicted EMG and highest r ranked IC at different activation thresholds. A linear mixed-effects statistical model was used to find the overall accuracy, sensitivity and specificity values over all the thresholds for each deep muscle. Results: Overall correlations of 0.81 for FPL (D), 0.88 for EPL (D), 0.92 for EPB (D) and 0.83 for APL (D) (p<0.001 for all muscles) were found between the predicted EMG waveforms and ICs. Using an activation threshold of 3 standard deviations above a resting baseline level, statistically significant (p<0.001) accuracy, sensitivity and specificity measures were found between the predicted EMG waveforms and top r ranked ICs for each of the deep muscles. The values of the 3 statistical measures (accuracy, sensitivity, specificity) for each of the deep muscles were: FPL (0.76, 0.88, 0.70); EPL (0.87, 0.85, 0.91); EPB (0.94, 0.93, 0.94); APL (0.80, 0.87, 0.87). Conclusions: The results indicate that this is the first non-invasive detection of the EMG activity of FPL (D), EPL (D), EPB (D) and APL (D). The ability to detect movement intention as a result of activation from these muscles may be of use for robot based targeted rehabilitation of the hand or in the control of prosthetic hand devices. en_ZA
dc.language.iso eng en_ZA
dc.subject.other Biomedical Engineering en_ZA
dc.title Non-invasive detection of the electromyographic activity of the deep extrinsic thumb muscles using surface electrodes en_ZA
dc.type Thesis / Dissertation en_ZA
uct.type.publication Research en_ZA
uct.type.resource Thesis en_ZA
dc.publisher.institution University of Cape Town
dc.publisher.faculty Faculty of Health Sciences en_ZA
dc.publisher.department Division of Biomedical Engineering en_ZA
dc.type.qualificationlevel Masters en_ZA
dc.type.qualificationname MSc (Med) en_ZA
uct.type.filetype Text
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