A methodology for the automated creation of fuzzy expert systems for ischaemic and arrhythmic beat classification based on a set of rules obtained by a decision tree
Φόρτωση...
Ημερομηνία
Συγγραφείς
Exarchos, T. P.
Tsipouras, M. G.
Exarchos, C. P.
Papaloukas, C.
Fotiadis, D. I.
Michalis, L. K.
Τίτλος Εφημερίδας
Περιοδικό ISSN
Τίτλος τόμου
Εκδότης
Elsevier
Περίληψη
Τύπος
Είδος δημοσίευσης σε συνέδριο
Είδος περιοδικού
peer reviewed
Είδος εκπαιδευτικού υλικού
Όνομα συνεδρίου
Όνομα περιοδικού
Artif Intell Med
Όνομα βιβλίου
Σειρά βιβλίου
Έκδοση βιβλίου
Συμπληρωματικός/δευτερεύων τίτλος
Περιγραφή
Objective: In the current work we propose a methodology for the automated creation of fuzzy expert systems, applied in ischaemic and arrhythmic beat classification. Methods: The proposed methodology automatically creates a fuzzy expert system from an initial training dataset. The approach consists of three stages: (a) extraction of a crisp set of rules from a decision tree induced from the training dataset, (b) transformation of the crisp set of rules into a fuzzy model and (c) optimization of the fuzzy model's parameters using global optimization. Material: The above methodology is employed in order to create fuzzy expert systems for ischaemic and arrhythmic beat classification in ECG recordings. The fuzzy expert system for ischaemic beat detection is evaluated in a cardiac beat dataset that was constructed using recordings from the European Society of Cardiology ST-T database. The arrhythmic beat classification fuzzy expert system is evaluated using the MIT-BIH arrhythmia database. Results: The fuzzy expert system for ischaemic beat classification reported 91% sensitivity and 92% specificity. The arrhythmic beat classification fuzzy expert system reported 96% average sensitivity and 99% average specificity for all categories. Conclusion: The proposed methodology provides high accuracy and the ability to interpret the decisions made. The fuzzy expert systems for ischaemic and arrhythmic beat classification compare well with previously reported results, indicating that they could be part of an overall clinical system for ECG analysis and diagnosis. (C) 2007 Elsevier B.V. All rights reserved.
Περιγραφή
Λέξεις-κλειδιά
fuzzy expert system, data mining, optimization, ischaemia, arrhythmia, neural-networks, heart-rate, t-wave, database, optimization, recognition, framework, episodes, ecgs
Θεματική κατηγορία
Παραπομπή
Σύνδεσμος
<Go to ISI>://000248725700003
http://ac.els-cdn.com/S0933365707000504/1-s2.0-S0933365707000504-main.pdf?_tid=3799f42b9b7e79de02ae1781face61fb&acdnat=1339756637_387f0db6fd684becb0ae90c719c9e522
http://ac.els-cdn.com/S0933365707000504/1-s2.0-S0933365707000504-main.pdf?_tid=3799f42b9b7e79de02ae1781face61fb&acdnat=1339756637_387f0db6fd684becb0ae90c719c9e522
Γλώσσα
en
Εκδίδον τμήμα/τομέας
Όνομα επιβλέποντος
Εξεταστική επιτροπή
Γενική Περιγραφή / Σχόλια
Ίδρυμα και Σχολή/Τμήμα του υποβάλλοντος
Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Επιστήμης Υλικών