dc.contributor.author | Istenič, R. | en |
dc.contributor.author | Kaplanis, P. A. | en |
dc.contributor.author | Pattichis, Constantinos S. | en |
dc.contributor.author | Zazula, D. | en |
dc.creator | Istenič, R. | en |
dc.creator | Kaplanis, P. A. | en |
dc.creator | Pattichis, Constantinos S. | en |
dc.creator | Zazula, D. | en |
dc.date.accessioned | 2019-11-13T10:40:24Z | |
dc.date.available | 2019-11-13T10:40:24Z | |
dc.date.issued | 2010 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/54103 | |
dc.description.abstract | We introduce a novel method for an automatic classification of subjects to those with or without neuromuscular disorders. This method is based on multiscale entropy of recorded surface electromyograms (sEMGs) and support vector classification. The method was evaluated on a single-channel experimental sEMGs recorded from biceps brachii muscle of nine healthy subjects, nine subjects with muscular and nine subjects with neuronal disorders, at 10%, 30%, 50%, 70% and 100% of maximal voluntary contraction force. Leave-one-out cross-validation was performed, deploying binary (healthy/patient) and three-class classification (healthy/myopathic/neuropathic). In the case of binary classification, subjects were distinguished with 81.5% accuracy (77.8% sensitivity at 83.3% specificity). At three-class classification, the accuracy decreased to 70.4% (myopathies were recognized with a sensitivity of 55.6% at specificity 88.9%, neuropathies with a sensitivity of 66.7% at specificity 83.3%). The proposed method is suitable for fast and non-invasive discrimination of healthy and neuromuscular patient groups, but it fails to recognize the type of pathology. © 2010 International Federation for Medical and Biological Engineering. | en |
dc.source | Medical and Biological Engineering and Computing | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-77955471137&doi=10.1007%2fs11517-010-0629-7&partnerID=40&md5=b41cd61f3275636b9f25854aedfa0dc8 | |
dc.subject | methodology | en |
dc.subject | article | en |
dc.subject | human | en |
dc.subject | Humans | en |
dc.subject | adult | en |
dc.subject | aged | en |
dc.subject | female | en |
dc.subject | male | en |
dc.subject | middle aged | en |
dc.subject | pathophysiology | en |
dc.subject | evaluation | en |
dc.subject | Muscle | en |
dc.subject | skeletal muscle | en |
dc.subject | physiology | en |
dc.subject | sensitivity and specificity | en |
dc.subject | Entropy | en |
dc.subject | automated pattern recognition | en |
dc.subject | Pattern Recognition, Automated | en |
dc.subject | signal processing | en |
dc.subject | Signal Processing, Computer-Assisted | en |
dc.subject | Wavelet transforms | en |
dc.subject | Continuous wavelet transform | en |
dc.subject | Muscle, Skeletal | en |
dc.subject | electromyography | en |
dc.subject | Healthy subjects | en |
dc.subject | Neuromuscular disorders | en |
dc.subject | Biceps brachii muscle | en |
dc.subject | Leave-one-out | en |
dc.subject | muscle contraction | en |
dc.subject | Sleep research | en |
dc.subject | Surface electromyogram | en |
dc.subject | Surface EMG | en |
dc.subject | Automatic classification | en |
dc.subject | Automatic indexing | en |
dc.subject | Binary classification | en |
dc.subject | Cross validation | en |
dc.subject | Maximal voluntary contractions | en |
dc.subject | Multiscale entropy | en |
dc.subject | neuromuscular disease | en |
dc.subject | Neuromuscular Diseases | en |
dc.subject | Non-invasive | en |
dc.subject | Novel methods | en |
dc.subject | Single-channel | en |
dc.subject | Support vector classification | en |
dc.subject | Three-class classification | en |
dc.title | Multiscale entropy-based approach to automated surface EMG classification of neuromuscular disorders | en |
dc.type | info:eu-repo/semantics/article | |
dc.identifier.doi | 10.1007/s11517-010-0629-7 | |
dc.description.volume | 48 | |
dc.description.issue | 8 | |
dc.description.startingpage | 773 | |
dc.description.endingpage | 781 | |
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 | Article | en |
dc.description.notes | <p>Cited By :38</p> | en |
dc.source.abbreviation | Med.Biol.Eng.Comput. | en |
dc.contributor.orcid | Pattichis, Constantinos S. [0000-0003-1271-8151] | |
dc.gnosis.orcid | 0000-0003-1271-8151 | |