Bibliographic Metadata

EMG-signal processing for neuro-excitability test using Matlab / von Sara Riegler
Additional Titles
Automatisierte EMG-Signalanalyse für Patienten mit nicht-dystrophen Myotonien zum Nachweis der Behandlung und dem Vergleich mit gesunden Probanden
AuthorRiegler, Sara
CensorKaniusas, Eugenijus
PublishedWien, 2017
Descriptionviii, 89 Seiten : Illustrationen, Diagramme
Institutional NoteTechnische Universität Wien, Diplomarbeit, 2017
Zusammenfassung in deutscher Sprache
Abweichender Titel nach Übersetzung der Verfasserin/des Verfassers
Document typeThesis (Diplom)
Keywords (DE)EMG / nicht-dystrophe Myotonien
Keywords (EN)EMG / non-dystrophic myotonia
URNurn:nbn:at:at-ubtuw:1-99400 Persistent Identifier (URN)
 The work is publicly available
EMG-signal processing for neuro-excitability test using Matlab [3.81 mb]
Abstract (English)

Introduction: Electromyography (EMG) is a standard practice in various felds such as prosthetics, rehabilitation, sport analysis or research. Due to its diversity of application, there is a high demand for individual signal processing solutions to the specic requirements. The task was to develop a semi-automatic signal processing interface in Matlab for different electromyographic tests, to validate its results by comparison with values in the literature and to apply it to control subject and patient data. The application was customized for the specic testing protocol comprising stimulation evoked as well as voluntary contractions and should give the user a general overview over the data. Methods: This thesis is based on an internship at the Myology Institute in Paris. The EMG signals of non-dystrophic myotonia patients as well as healthy control subjects were analysed.^ The signals were recorded during 6 different neuromuscular excitability tests (maximum M response, 5Hz stimulation, refractory, supernormality, maximum voluntary contraction, fatigue), using laplacian electrodes. Matlab was used for further analysis of the data. The signals were altered, their quality classifed and test specific parameters were calculated. The results were compared with already published results of the same dataset in order to gain information about validity. Results: All non signal-quality related processing steps were automatized and the quality classification decisions were guided by the developed program. All but one stimulation evoked control EMG file passed the signal quality assessment, whereas only 38% of the patients compound muscle action potential and 42% of the patients 5Hz tests were valid. The remaining poor quality signals were classified into 4 different error classes.^ Due to the combination of the patients poor quality CMAP and double stimulation recordings, neither the supernormality nor the refractory test could be processed. The control group results were all comparable with the literature and previous calculations. The patients force as well as root mean square and mean power frequency values were generally lower than those of the control group. Myotonia congenita and paramyotonia congenita patients tend to have different EMG behaviour but no general statements could be made due to small group numbers and mostly inconsistent results on the different days of examination. Conclusion: The application of the algorithms on healthy control data showed that it is generally possible to semi-automatically process EMG data. The adaptable user interfaces created for this testing protocol are applicable on any compound muscle action potential, 5Hz, refractory, supernormality, maximum voluntary contraction or fatigue recordings.^ However, basic requirements such as adequate signal quality and subgroup numbers still have to be fulfilled. For patient data, this might be especially diffcult as for some rare diseases it is simply not possible to obtain more participants or disease specific characteristics complicate the recording of EMG signals.

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