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https://olympias.lib.uoi.gr/jspui/handle/123456789/14025Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Katsis, C. D. | en |
| dc.contributor.author | Goletsis, Y. | en |
| dc.contributor.author | Likas, A. | en |
| dc.contributor.author | Fotiadis, D. I. | en |
| dc.contributor.author | Sarmas, I. | en |
| dc.date.accessioned | 2015-11-24T17:34:45Z | - |
| dc.date.available | 2015-11-24T17:34:45Z | - |
| dc.identifier.issn | 0933-3657 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/14025 | - |
| dc.rights | Default Licence | - |
| dc.subject | quantitative electromyography | en |
| dc.subject | etectromyogram decomposition | en |
| dc.subject | motor unit action potential detection and classification | en |
| dc.subject | support vector machine | en |
| dc.subject | electromyographic signals | en |
| dc.subject | action-potentials | en |
| dc.subject | quantitative-analysis | en |
| dc.title | A novel method for automated EMG decomposition and MUAP classification | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.primary | DOI 10.1016/j.artmed.2005.09.002 | - |
| heal.identifier.secondary | <Go to ISI>://000237744000006 | - |
| heal.identifier.secondary | http://ac.els-cdn.com/S0933365705001065/1-s2.0-S0933365705001065-main.pdf?_tid=60f5a20504bdabbca0b1543403c94757&acdnat=1339758072_311f1fd9edc64fc97ed2baeb25a89d74 | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Επιστήμης Υλικών | el |
| heal.publicationDate | 2006 | - |
| heal.abstract | Objective: This paper proposes a novel method for the extraction and classification of individual motor unit action potentials (MUAPs) from intramuscular electromyographic signals. Methodology: The proposed method automatically detects the number of template MUAP clusters and classifies them into normal, neuropathic or myopathic. It consists of three steps: (i) preprocessing of electromyogram (EMG) recordings, (ii) MUAP detection and clustering and (iii) MUAP classification. Results: The approach has been validated using a dataset of EMG recordings and an annotated collection of MUAPs. The correct identification rate for MUAP clustering is 93, 95 and 92% for normal, myopathic and neuropathic, respectively. Ninety-one percent of the superimposed MUAPs were correctly identified. The obtained accuracy for MUAP classification is about 86%. Conclusion: The proposed method, apart from efficient EMG decomposition addresses automatic MUAP classification to neuropathic, myopathic or normal classes directly from raw EMG signals. (C) 2005 Elsevier B.V. All rights reserved. | en |
| heal.publisher | Elsevier | en |
| heal.journalName | Artif Intell Med | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Katsis-2006-A novel method for a.pdf | 364.05 kB | Adobe PDF | View/Open Request a copy |
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