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PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  3/2017 - 4

A Proposal for Cardiac Arrhythmia Classification using Complexity Measures

AROTARITEI, D. See more information about AROTARITEI, D. on SCOPUS See more information about AROTARITEI, D. on IEEExplore See more information about AROTARITEI, D. on Web of Science, COSTIN, H. See more information about  COSTIN, H. on SCOPUS See more information about  COSTIN, H. on SCOPUS See more information about COSTIN, H. on Web of Science, PASARICA, A. See more information about  PASARICA, A. on SCOPUS See more information about  PASARICA, A. on SCOPUS See more information about PASARICA, A. on Web of Science, ROTARIU, C. See more information about ROTARIU, C. on SCOPUS See more information about ROTARIU, C. on SCOPUS See more information about ROTARIU, C. on Web of Science
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,245 KB) | Citation | Downloads: 488 | Views: 1,565

Author keywords
complexity theory, decision trees, electrocardiography, random sequences, classification algorithms, fuzzy set

References keywords
classification(13), arrhythmia(13), systems(8), fuzzy(7), cardiac(7), biomedical(6), applications(6), analysis(6), algorithm(6), neural(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-08-31
Volume 17, Issue 3, Year 2017, On page(s): 29 - 34
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.03004
Web of Science Accession Number: 000410369500004
SCOPUS ID: 85028535223

Abstract
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Cardiovascular diseases are one of the major problems of humanity and therefore one of their component, arrhythmia detection and classification drawn an increased attention worldwide. The presence of randomness in discrete time series, like those arising in electrophysiology, is firmly connected with computational complexity measure. This connection can be used, for instance, in the analysis of RR-intervals of electrocardiographic (ECG) signal, coded as binary string, to detect and classify arrhythmia. Our approach uses three algorithms (Lempel-Ziv, Sample Entropy and T-Code) to compute the information complexity applied and a classification tree to detect 13 types of arrhythmia with encouraging results. To overcome the computational effort required for complexity calculus, a cloud computing solution with executable code deployment is also proposed.


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Cited-By CrossRef

[1] Effective Wavelet Algorithm for an Automatic Detection of Atrial Fibrillation, ARVINTI, B., ISAR, A., COSTACHE, M., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 3, Volume 20, 2020.
Digital Object Identifier: 10.4316/AECE.2020.03006
[CrossRef] [Full text]

[2] Microcontroller Implementation of Algorithms for Computational Complexity, Gheorghita, Andrei, Corciova, Calin, Turnea, Marius, Arotaritei, Dragos, 2019 11th International Symposium on Advanced Topics in Electrical Engineering (ATEE), ISBN 978-1-7281-0101-9, 2019.
Digital Object Identifier: 10.1109/ATEE.2019.8724898
[CrossRef]

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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