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https://olympias.lib.uoi.gr/jspui/handle/123456789/13747Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Papaloukas, C. | en |
| dc.contributor.author | Fotiadis, D. I. | en |
| dc.contributor.author | Liavas, A. P. | en |
| dc.contributor.author | Likas, A. | en |
| dc.contributor.author | Michalis, L. K. | en |
| dc.date.accessioned | 2015-11-24T17:32:33Z | - |
| dc.date.available | 2015-11-24T17:32:33Z | - |
| dc.identifier.issn | 0140-0118 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/13747 | - |
| dc.rights | Default Licence | - |
| dc.subject | ischaemic episodes detection | en |
| dc.subject | knowledge-based method | en |
| dc.subject | ecg noise handling | en |
| dc.subject | artificial neural-network | en |
| dc.subject | st-segment analysis | en |
| dc.subject | myocardial-infarction | en |
| dc.subject | ecg analysis | en |
| dc.subject | ischemia | en |
| dc.subject | recovery | en |
| dc.subject | system | en |
| dc.title | A knowledge-based technique for automated detection of ischaemic episodes in long duration electrocardiograms | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.secondary | <Go to ISI>://000166793200016 | - |
| heal.identifier.secondary | http://www.springerlink.com/content/5434012392701650/fulltext.pdf | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Επιστήμης Υλικών | el |
| heal.publicationDate | 2001 | - |
| heal.abstract | A novel method for the detection of ischaemic episodes in long duration ECGs is proposed. It includes noise handling, feature extraction, rule-based beat classification, sliding window classification and ischaemic episode identification, all integrated in a four-stage procedure. It can be executed in real time and is able to provide explanations for the diagnostic decisions obtained The method was tested on the ESC ST-T database and high scores were obtained for both sensitivity and positive predictive accuracy (93.8% and 78.5% respectively using aggregate gross statistics, and 90.7% and 80.7% using aggregate average statistics). | en |
| heal.publisher | Springer-Verlag | en |
| heal.journalName | Med Biol Eng Comput | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Papaloukas-2001-A knowledge-based te.pdf | 899.85 kB | Adobe PDF | View/Open Request a copy |
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