dc.contributor.author | Christodoulou, Christodoulos I. | en |
dc.contributor.author | Kaplanis, P. A. | en |
dc.contributor.author | Murray, V. | en |
dc.contributor.author | Pattichis, Marios S. | en |
dc.contributor.author | Pattichis, Constantinos S. | en |
dc.creator | Christodoulou, Christodoulos I. | en |
dc.creator | Kaplanis, P. A. | en |
dc.creator | Murray, V. | en |
dc.creator | Pattichis, Marios S. | en |
dc.creator | Pattichis, Constantinos S. | en |
dc.date.accessioned | 2019-11-13T10:39:16Z | |
dc.date.available | 2019-11-13T10:39:16Z | |
dc.date.issued | 2009 | |
dc.identifier.isbn | 978-1-4244-5379-5 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/53717 | |
dc.description.abstract | The objective of this study was to evaluate the usefulness of AM-FM features extracted from surface electro myographic (SEMG) signals for the assessment of neuromuscular disorders at different force levels. SEMG signals were recorded from a total of 40 subjects, 20 normal and 20 patients, at 10%, 30%, 50%, 70% and 100% of maximum voluntary contraction (MVC), from the biceps brachii muscle. From the SEMG signals, we extracted the instantaneous amplitude, the instantaneous frequency and the instantaneous phase. For each AM-FM feature their histograms were computed for 32 bins. For the classification, three classifiers were used: (i) the statistical K-nearest neighbour (KNN), (ii) the neural self-organizing map (SOM) and (iii) the neural support vector machine (SVM). For all classifiers the leave-one-out methodology was implemented for the classification of the SEMG signals into normal or pathogenic. The test results reached a classification success rate of 80% when a combination of the three AM-FM features was used. ©2009 IEEE. | en |
dc.source | Final Program and Abstract Book - 9th International Conference on Information Technology and Applications in Biomedicine, ITAB 2009 | en |
dc.source | 9th International Conference on Information Technology and Applications in Biomedicine, ITAB 2009 | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-77949578719&doi=10.1109%2fITAB.2009.5394432&partnerID=40&md5=152aa99ff3387c7da9a5712b7b4b572d | |
dc.subject | Information technology | en |
dc.subject | Statistical methods | en |
dc.subject | Muscle | en |
dc.subject | Classification | en |
dc.subject | Conformal mapping | en |
dc.subject | Graphic methods | en |
dc.subject | Support vector machines | en |
dc.subject | Neuromuscular disorders | en |
dc.subject | AM-FM | en |
dc.subject | Amplitude modulation | en |
dc.subject | Biceps brachii muscle | en |
dc.subject | Classifiers | en |
dc.subject | Electromyographic signal | en |
dc.subject | Force level | en |
dc.subject | K nearest neighbours (k-NN) | en |
dc.subject | Leave-one-out | en |
dc.subject | Maximum voluntary contraction | en |
dc.subject | SEMG | en |
dc.subject | Test results | en |
dc.subject | Classification , | en |
dc.subject | Instantaneous amplitude | en |
dc.subject | Instantaneous frequency | en |
dc.title | Classification of surface electromyographic signals using AM-FM features | en |
dc.type | info:eu-repo/semantics/conferenceObject | |
dc.identifier.doi | 10.1109/ITAB.2009.5394432 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Conference Object | en |
dc.description.notes | <p>Sponsors: IBM Italia S.p.A. | en |
dc.description.notes | Datamed SA, Healthcare Integrator | en |
dc.description.notes | LinkSCEEM: Link. Sci. Comput. Eur. East. Mediterr. | en |
dc.description.notes | AGIOS THERISSOS M.R.1. Medical Diagnostic Center | en |
dc.description.notes | University of Cyprus | en |
dc.description.notes | Conference code: 79527 | en |
dc.description.notes | Cited By :4</p> | en |
dc.contributor.orcid | Pattichis, Constantinos S. [0000-0003-1271-8151] | |
dc.contributor.orcid | Pattichis, Marios S. [0000-0002-1574-1827] | |
dc.gnosis.orcid | 0000-0003-1271-8151 | |
dc.gnosis.orcid | 0000-0002-1574-1827 | |