An arrhythmia classification system based on the RR-interval signal
| dc.contributor.author | Tsipouras, M. G. | en |
| dc.contributor.author | Fotiadis, D. I. | en |
| dc.contributor.author | Sideris, D. | en |
| dc.date.accessioned | 2015-11-24T17:31:19Z | |
| dc.date.available | 2015-11-24T17:31:19Z | |
| dc.identifier.issn | 0933-3657 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/13573 | |
| dc.rights | Default Licence | - |
| dc.subject | arrhythmia classification | en |
| dc.subject | rr-interval signal | en |
| dc.subject | knowledge-based system | en |
| dc.subject | deterministic automaton | en |
| dc.subject | threatening cardiac-arrhythmias | en |
| dc.subject | ventricular-fibrillation | en |
| dc.subject | neural-networks | en |
| dc.subject | wavelet transformation | en |
| dc.subject | detection algorithm | en |
| dc.subject | ecg | en |
| dc.subject | recognition | en |
| dc.subject | tachycardia | en |
| dc.subject | tachyarrhythmia | en |
| dc.subject | discrimination | en |
| dc.title | An arrhythmia classification system based on the RR-interval signal | en |
| heal.abstract | Objective: This paper proposes a knowledge-based method for arrhythmic beat classification and arrhythmic episode detection and classification using only the RR-interval signal extracted from ECG recordings. Methodology: A three RR-interval sliding window is used in arrhythmic beat classification algorithm. Classification is performed for four categories of beats: normal, premature ventricular contractions, ventricutar flutter/fibrillation and 2 degrees heart block. The beat classification is used as input of a knowledge-based deterministic automaton to achieve arrhythmic episode detection and classification. Six rhythm types are classified: ventricular bigeminy, ventricutar trigeminy, ventricular couplet, ventricular tachycardia, ventricutar flutter/fibrillation and 2 degrees heart block. Results: The method is evaluated by using the MIT-BIH arrhythmia database. The achieved scores indicate high performance: 98% accuracy for arrhythmic beat classification and 94% accuracy for arrhythmic episode detection and classification. Conclusion: The proposed method is advantageous because it uses only the RR-interval signal for arrhythmia beat and episode classification and the results compare well with more complex methods. (c) 2004 Elsevier B.V. All rights reserved. | en |
| heal.access | campus | - |
| heal.fullTextAvailability | TRUE | - |
| heal.identifier.primary | DOI 10.1016/j.artmed.2004.03.007 | - |
| heal.identifier.secondary | <Go to ISI>://000228673900004 | - |
| heal.identifier.secondary | http://ac.els-cdn.com/S0933365704000806/1-s2.0-S0933365704000806-main.pdf?_tid=2cd7b69b60536d36bffd3a3eb1ddf9d4&acdnat=1339758741_c86f72427a33b6747933b3794e06fed9 | - |
| heal.journalName | Artif Intell Med | en |
| heal.journalType | peer reviewed | - |
| heal.language | en | - |
| heal.publicationDate | 2005 | - |
| heal.publisher | Elsevier | en |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Επιστήμης Υλικών | el |
| heal.type | journalArticle | - |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.type.en | Journal article | en |
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