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Stefan cel Mare
University of Suceava
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Print ISSN: 1582-7445
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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
 
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Download PDF pdficon (1,245 KB) | Citation | Downloads: 860 | Views: 1,906

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.


References | Cited By  «-- Click to see who has cited this paper

[1] S. S. Anand; S. Yusuf; "Stemming the global tsunami of cardiovascular disease", The Lancet, vol. 377, no. 9765, pp. 529-532, 2011.
[CrossRef] [Web of Science Times Cited 155] [SCOPUS Times Cited 169]


[2] A. Ebrahimzadeh, A. Khazaee, "An efficient technique for classification of electrocardiogram signals", Advances in Electrical and Computer Engineering, vol. 9, no. 3, pp. 89-93, 2009.
[CrossRef] [Full Text] [Web of Science Times Cited 14] [SCOPUS Times Cited 20]


[3] A. Lanatá, G. Valenza, C. Mancuso, E.P. Scilingo, "Robust multiple cardiac arrhythmia detection through bispectrum analysis", Expert Systems with Applications, vol. 38, pp. 6798-6804, 2011.
[CrossRef] [Web of Science Times Cited 36] [SCOPUS Times Cited 46]


[4] A. F. Khalaf, M. I. Owis, I. A. Yassine, "A novel technique for cardiac arrhythmia classification using spectral correlation and support vector machines", Expert Systems with Applications, vol. 42, pp. 8361-8368, 2015.
[CrossRef] [Web of Science Times Cited 70] [SCOPUS Times Cited 87]


[5] B. M. Asl, S. K. Setarehdan, M. Mohebbi, "Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal", Artificial Intelligence in Medicine, vol. 44, pp. 51-64, 2008.
[CrossRef] [Web of Science Times Cited 202] [SCOPUS Times Cited 253]


[6] Y. Özbaya, R. Ceylana, B. Karlikb, "Fuzzy clustering neural network architecture for classification of ECG arrhythmias", Computers in Biology and Medicine, vol. 36, pp. 376-388, 2006.
[CrossRef] [Web of Science Times Cited 149] [SCOPUS Times Cited 199]


[7] O. Castillo, P. Melin, E. Ramírez, J. Soria, "Hybrid intelligent system for cardiac arrhythmia classification with Fuzzy K-Nearest Neighbors and neural networks combined with a fuzzy system", Expert Systems with Applications, vol. 39, pp. 2947-2955, 2012.
[CrossRef] [Web of Science Times Cited 71] [SCOPUS Times Cited 88]


[8] P. Melin, J. Amezcua, F. Valdez, O. Castillo, "A new neural network model based on the LVQ algorithm for multi-class classification of arrhythmias", Information Sciences, vol. 279, pp. 483-497, 2014.
[CrossRef] [Web of Science Times Cited 77] [SCOPUS Times Cited 108]


[9] S. Osowski, T. Markiewicz, L. T. Hoai, "Recognition and classification system of arrhythmia using ensemble of neural networks", Measurement, vol. 41, pp. 610-617, 2008.
[CrossRef] [Web of Science Times Cited 40] [SCOPUS Times Cited 53]


[10] C.-H. Lin, Y.-C. Du, T. Chen, "Adaptive wavelet network for multiple cardiac arrhythmias recognition", Expert Systems with Applications, vol. 34, pp. 2601-2611, 2008.
[CrossRef] [Web of Science Times Cited 68] [SCOPUS Times Cited 82]


[11] M. Korürek, A. Nizam, "A new arrhythmia clustering technique based on Ant Colony Optimization", Journal of Biomedical Informatics, vol. 41, pp. 874-881, 2008.
[CrossRef] [Web of Science Times Cited 44] [SCOPUS Times Cited 59]


[12] J. Park, K. Kang, "PcHD: Personalized classification of heartbeat types using a decision tree", Computers in Biology and Medicine, vol. 54, pp. 79-88, 2014.
[CrossRef] [Web of Science Times Cited 19] [SCOPUS Times Cited 25]


[13] E. J. da S. Luz, T.M. Nunes, V. H. C. de Albuquerque, J.P. Papa, D. Menotti, "ECG arrhythmia classification based on optimum-path forest", Expert Systems with Applications, vol. 40, pp. 3561-3573, 2013.
[CrossRef] [Web of Science Times Cited 103] [SCOPUS Times Cited 131]


[14] A. K. Mishra, S. Raghav, "Local fractal dimension based ECG arrhythmia classification", Biomedical Signal Processing and Control, vol. 5, pp. 114-123, 2010.
[CrossRef] [Web of Science Times Cited 59] [SCOPUS Times Cited 78]


[15] L. Xu, D. Zhang, K. Wang, L. Wang, "Arrhythmic Pulses Detection Using Lempel-Ziv Complexity Analysis", EURASIP Journal on Applied Signal Processing, pp. 1-12, 2006.
[CrossRef] [Web of Science Times Cited 33] [SCOPUS Times Cited 28]


[16] J. Pan, W. J. Tompkins, "A real-time QRS detection algorithm", IEEE Trans Biomed Eng., vol. 32, no. 2, pp. 230-236, 1985.
[CrossRef] [Web of Science Times Cited 4141] [SCOPUS Times Cited 5096]


[17] S. Dash, K. H. Chon, S. Lu, E. A. Raeder, "Automatic Real Time Detection of Atrial Fibrillation", Annals of Biomedical Engineering, vol. 37, no. 9, pp. 1701-1709, 2009.
[CrossRef] [Web of Science Times Cited 209] [SCOPUS Times Cited 234]


[18] R. Karlsson, R. Hörnsten, A. Rydberg, U. Wiklund, "Automatic filtering of outliers in RR intervals before analysis of heart rate variability in Holter recordings: a comparison with carefully edited data", BioMedical Engineering OnLine, pp. 1-12, 2012.
[CrossRef] [Web of Science Times Cited 24] [SCOPUS Times Cited 31]


[19] J. S. Richman, J. R. Moorman, "Physiological time-series analysis using approximate entropy and sample entropy", American Journal of Physiology, Heart and Circulatory Physiology, vol. 278, no. 6, pp. H2039-H2049, 2000. http://ajpheart.physiology.org/content/278/6/H2039.full

[20] D. E. Lake, J. S. Richman, M. P. Griffin, J. R. Moorman, "Sample entropy analysis of neonatal heart rate variability", Am. J. Physiol. Regul. Integr. Comp. Physiol., vol. 283, no. 3, pp. R789-97, 2002.
[CrossRef] [Web of Science Times Cited 838] [SCOPUS Times Cited 902]


[21] A. Lempel, J. Ziv, "On the Complexity of Finite Sequences", IEEE Transactions on Information Theory, vol. IT-22, no. 1, pp. 75-81, 1976.
[CrossRef] [Web of Science Times Cited 1590] [SCOPUS Times Cited 1874]


[22] J. Ziv, "Coding Theorems for Individual Sequences", IEEE Transactions on Information Theory, vol. IT-24, no. 4, pp. 405-412, 1978.
[CrossRef] [Web of Science Times Cited 96] [SCOPUS Times Cited 122]


[23] A. N. Kolmogorov, "Three approaches to the quantitative definition of information", Problems of Information Transmission, vol. 1, pp. 1-7, 1965.
[CrossRef] [SCOPUS Times Cited 204]


[24] M. R. Titchener, "Generalized T-Codes: An Extended Construction Algorithm for Self-synchronizing Codes", TAMAKI T-CODE PROJECT SERIES, vol. 1, no. 4, pp. 1-8, 1995.
[CrossRef] [Web of Science Times Cited 32] [SCOPUS Times Cited 45]


[25] U. Gunter, "Data compression and serial communication with generalized T-codes", Journal of Universal Computer Science, vol. 2, no. 11, pp. 769-795, 1996.
[CrossRef]


[26] K. Hamano, H. Yamamoto, "A Randomness Test based on T-Complexity", IIECE Trans. Fundamentals, vol. E93, no. 7, pp. 1346-1354, 2010.
[CrossRef] [SCOPUS Times Cited 8]


[27] Y.-P. Huang, C.-Y. Huanga, S.-I. Liu, "Hybrid intelligent methods for arrhythmia detection and geriatric depression diagnosis", Applied Soft Computing, vol. 14, pp. 38-46, 2014.
[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 17]


[28] H. Xia, I. Asif, X. Xiaopeng Zhao, "Cloud-ECG for real time ECG monitoring and analysis", Computer Methods and Programs in Biomedicine, vol. 110, pp. 253-259, 2013.
[CrossRef] [Web of Science Times Cited 67] [SCOPUS Times Cited 87]


[29] The same as [27]

[30] X.-S. Zhang, Y.-S Zhu, N. V. Thakor, Z.-Z. Wang, "Detecting Ventricular Tachycardia and Fibrillation by Complexity Measure", IEEE Transactions on Biomedical Engineering, vol. 46, no. 5, pp. 548-555, 1999.
[CrossRef] [Web of Science Times Cited 240] [SCOPUS Times Cited 289]


[31] D. Ge, N. Srinivasan, S. M. Krishnan, "Cardiac arrhythmia classification using autoregressive modeling", BioMedical Engineering OnLine, pp. 1-5, 2002.
[CrossRef] [Web of Science Times Cited 121] [SCOPUS Times Cited 195]


[32] S. W. Chen, "Two-stage discrimination of cardiac arrhythmias using a total least squares-based prony modeling algorithm", IEEE Trans Biomed Eng, vol. 47, pp. 1317-1326, 2000.
[CrossRef] [Web of Science Times Cited 52] [SCOPUS Times Cited 70]


[33] M. H. Song, J. Lee, S. P. Cho, K.J. Lee, S.K. Yoo, "Support Vector Machine Based Arrhythmia Classification Using Reduced Features", International Journal of Control, Automation, and Systems, vol. 3, no. 4, pp. 571-579, 2005. [Online] Available: Temporary on-line reference link removed - see the PDF document

[34] T. F. L. de Medeiros, et al. "Heart arrhythmia classification using the PPM algorithm", Biosignals and Biorobotics Conference ISSNIP, pp. 1-5, 2011.
[CrossRef] [SCOPUS Times Cited 4]


[35] C. Rotariu, V. Manta, R. Ciobotariu, "Integrated system based on wireless sensors network for cardiac arrhythmia monitoring", Advances in Electrical and Computer Engineering, vol. 13, no. 1, pp. 95-100, 2013.
[CrossRef] [Full Text] [Web of Science Times Cited 8] [SCOPUS Times Cited 10]


[36] V. Purdila, S.G. Pentiuc, "Fast decision tree algorithm", Advances in Electrical and Computer Engineering, vol. 14, no. 1, pp. 65-68, 2014.
[CrossRef] [Full Text] [Web of Science Times Cited 6] [SCOPUS Times Cited 9]


[37] J. Nowaková, M. Prílepok, V. Snase, "Medical Image Retrieval Using Vector Quantization and Fuzzy S-tree", Journal of Medical Systems, Vol. 41, Issue 2, pp. 1-16, Febr. 2017, Plenum Press, New York, USA.
[CrossRef] [Web of Science Times Cited 57] [SCOPUS Times Cited 66]


[38] C. Pozna, N. Minculete, R.-E. Precup, L.T. KóCzy, Á. Ballagi, "Signatures: Definitions, operators and applications to fuzzy modelling", Fuzzy Sets and Systems, vol. 201, pp. 86-104, August, 2012.
[CrossRef] [Web of Science Times Cited 70] [SCOPUS Times Cited 77]


[39] W. Chen, Z.Wang, H. Xie, W. Yu, "Characterization of surface EMG signal based on fuzzy entropy", IEEE Trans. Neural Syst. Rehabil. Eng., vol. 15, no. 2, pp. 266-272, 2007.
[CrossRef] [Web of Science Times Cited 465] [SCOPUS Times Cited 548]




References Weight

Web of Science® Citations for all references: 9,170 TCR
SCOPUS® Citations for all references: 11,314 TCR

Web of Science® Average Citations per reference: 229 ACR
SCOPUS® Average Citations per reference: 283 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2022-08-10 10:36 in 224 seconds.




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