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https://olympias.lib.uoi.gr/jspui/handle/123456789/7687Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Exarchos, T. P. | en |
| dc.contributor.author | Papaloukas, C. | en |
| dc.contributor.author | Fotiadis, D. I. | en |
| dc.contributor.author | Michalis, L. K. | en |
| dc.date.accessioned | 2015-11-24T16:33:38Z | - |
| dc.date.available | 2015-11-24T16:33:38Z | - |
| dc.identifier.issn | 0018-9294 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/7687 | - |
| dc.rights | Default Licence | - |
| dc.subject | association rules | en |
| dc.subject | automated ischemic beat detection | en |
| dc.subject | data mining | en |
| dc.subject | rule-based classification | en |
| dc.subject | st-t database | en |
| dc.subject | neural-networks | en |
| dc.subject | patterns | en |
| dc.subject | discovery | en |
| dc.subject | episodes | en |
| dc.subject | signals | en |
| dc.title | An association rule mining-based methodology for automated detection of ischemic ECG beats | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.primary | Doi 10.1109/Tbme.2006.873753 | - |
| heal.identifier.secondary | <Go to ISI>://000239263400008 | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Επιστημών και Τεχνολογιών. Τμήμα Βιολογικών Εφαρμογών και Τεχνολογιών | el |
| heal.publicationDate | 2006 | - |
| heal.abstract | Currently, an automated methodology based on association rules is presented for the detection of ischemic beats in long duration electrocardiographic (ECG) recordings. The proposed approach consists of three stages. 1) Preprocessing: Noise is removed and all the necessary ECG features are extracted. 2) Discretization: The continuous valued features are transformed to categorical. 3) Classification: An association rule extraction algorithm is utilized and a rule-based classification model is created. According to the proposed methodology, electrocardiogram (ECG) features extracted from the ST segment and the T-wave, as well as the patient's age, were used as inputs. The output was the classification of the beat as ischemic or not. Various algorithms were tested both for discretization and for classification using association rules. To evaluate the methodology, a cardiac beat dataset was constructed using several recordings of the European Society of Cardiology ST-T database. The obtained sensitivity (Se) and specificity (Sp) was 87% and 93%, respectively. The proposed methodology combines high accuracy with the ability to provide interpretation for the decisions made, since it is based on a set of association rules. | en |
| heal.journalName | Ieee Transactions on Biomedical Engineering | 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-2006-n association rule mining-based methodology for automated detection of ischemic ECG beats.pdf | 650.22 kB | Adobe PDF | View/Open Request a copy |
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