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A Proposal for Cardiac Arrhythmia Classification using Complexity MeasuresAROTARITEI, D. , COSTIN, H. , PASARICA, A. , ROTARIU, C.
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complexity theory, decision trees, electrocardiography, random sequences, classification algorithms, fuzzy set
classification(13), arrhythmia(13), systems(8), fuzzy(7), cardiac(7), biomedical(6), applications(6), analysis(6), algorithm(6), neural(5)
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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
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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 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]
 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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