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

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dc.contributor.advisorJohn, Lesteren_ZA
dc.contributor.authorPitman, Jeremy Daviden_ZA
dc.date.accessioned2016-02-05T07:23:15Z
dc.date.available2016-02-05T07:23:15Z
dc.date.issued2015en_ZA
dc.descriptionIncludes bibliographical referencesen_ZA
dc.description.abstractMotivation: 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.identifier.apacitationPitman, J. D. (2015). <i>Non-invasive detection of the electromyographic activity of the deep extrinsic thumb muscles using surface electrodes</i>. (Thesis). University of Cape Town ,Faculty of Health Sciences ,Division of Biomedical Engineering. Retrieved from http://hdl.handle.net/11427/16783en_ZA
dc.identifier.chicagocitationPitman, Jeremy David. <i>"Non-invasive detection of the electromyographic activity of the deep extrinsic thumb muscles using surface electrodes."</i> Thesis., University of Cape Town ,Faculty of Health Sciences ,Division of Biomedical Engineering, 2015. http://hdl.handle.net/11427/16783en_ZA
dc.identifier.citationPitman, 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.ris TY - Thesis / Dissertation AU - Pitman, Jeremy David AB - 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. DA - 2015 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2015 T1 - Non-invasive detection of the electromyographic activity of the deep extrinsic thumb muscles using surface electrodes TI - Non-invasive detection of the electromyographic activity of the deep extrinsic thumb muscles using surface electrodes UR - http://hdl.handle.net/11427/16783 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/16783
dc.identifier.vancouvercitationPitman JD. Non-invasive detection of the electromyographic activity of the deep extrinsic thumb muscles using surface electrodes. [Thesis]. University of Cape Town ,Faculty of Health Sciences ,Division of Biomedical Engineering, 2015 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/16783en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDivision of Biomedical Engineeringen_ZA
dc.publisher.facultyFaculty of Health Sciencesen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherBiomedical Engineeringen_ZA
dc.titleNon-invasive detection of the electromyographic activity of the deep extrinsic thumb muscles using surface electrodesen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMSc (Med)en_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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