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JCR Impact Factor: 0.595
JCR 5-Year IF: 0.661
Issues per year: 4
Current issue: May 2018
Next issue: Aug 2018
Avg review time: 105 days


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

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


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Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

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  3/2009 - 16

An Efficient Technique for Classification of Electrocardiogram Signals

EBRAHIMZADEH, A. See more information about EBRAHIMZADEH, A. on SCOPUS See more information about EBRAHIMZADEH, A. on IEEExplore See more information about EBRAHIMZADEH, A. on Web of Science, KHAZAEE, A. See more information about KHAZAEE, A. on SCOPUS See more information about KHAZAEE, A. on SCOPUS See more information about KHAZAEE, A. on Web of Science
Click to see author's profile on 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 (484 KB) | Citation | Downloads: 921 | Views: 3,695

Author keywords
ECG beat classification, wavelet, radial basis function neural network

References keywords
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2009-10-26
Volume 9, Issue 3, Year 2009, On page(s): 89 - 93
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2009.03016
Web of Science Accession Number: 000271872000016
SCOPUS ID: 77954728832

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This work describes a Radial Basis Function (RBF) neural network method used to analyze ECG signals for diagnosing cardiac arrhythmias effectively. The proposed method can accurately classify and differentiate normal (Normal) and abnormal heartbeats. Abnormal heartbeats include left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature contractions (APC) and premature ventricular contractions (PVC). This paper proposes a three stage, preprocessing, feature extraction and classification method for the detection of ECG beat types. In the first stage, ECG beats is normalized to a mean of zero and standard deviation of unity. Feature extraction module extracts wavelet approximate coefficients of ECG signals in conjunction with three timing interval features. Then a number of radial basis function (RBF) neural networks with different value of spread parameter are designed. We compared the classification ability of five different classes of ECG signals that were achieved over eight files from the MIT/BIH arrhythmia database.

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

[1] Hu, G. M., S. Palreddy, and W., Tompkins, "Patient Adaptable ECG Beat Classifier Using a Mixture of Experts Approach", IEEE Trans. Biomed. Eng., Vol. 44, 1997, pp. 891-900
[CrossRef] [PubMed] [SCOPUS Times Cited 342]

[2] T.H. Yeap, F. Johnson, M. Rachniowski, "ECG Beat Classification by a Neural Network", Proceedings Annual International Conference of the IEEE EMBS Society pg 1457-1458, 1990

[3] O. T. Inan, L. Giovangrandi, and G. T. A. Kovacs., "Robust Neural-Network-Based Classification of Premature Ventricular Contractions Using Wavelet Transform and Timing Interval Features", IEEE Trans. Biomed. Eng., vol. 53, no. 12, pp. 2507-2515, Dec. 2006
[CrossRef] [PubMed] [Web of Science Times Cited 109] [SCOPUS Times Cited 172]

[4] S. N. Yu, and K. T. Chou., "Selection of significant for ECG beat classification", Expert Systems with Applications, Vol. 36, pp. 2088-2096, 2009
[CrossRef] [Web of Science Times Cited 44] [SCOPUS Times Cited 59]

[5] Chazal P., O'Dwyer M., Reilly R.B., "Automatic classification of heartbeats using ECG morphology and heartbeat interval features", IEEE Trans Biomed Eng 2004; 51:1196-1206
[CrossRef] [Web of Science Times Cited 355] [SCOPUS Times Cited 577]

[6] G. D. Clifford, F. Azuaje, P. E. McShary, "Advanced Methods and Tools for ECG Data Analysis", Artech House: Norwood, MA 02062, 2006

[7] R. Mark and G. Moody, "MIT-BIH Arrhythmia Database 1997",

[8] G. B. Moody and R. G. Mark, "The impact of the mit/bih arrhythmia database", IEEE Eng. Med. Biol. Mag., vol. 20, no. 3, May-Jun, 2001 [PubMed]

[9] Amara Graps, "An Introduction to Wavelets", IEEE Comp. Sc. And Eng., Vol. 2, No. 2, 1995
[CrossRef] [Web of Science Times Cited 497] [SCOPUS Times Cited 834]

References Weight

Web of Science® Citations for all references: 1,005 TCR
SCOPUS® Citations for all references: 1,984 TCR

Web of Science® Average Citations per reference: 112 ACR
SCOPUS® Average Citations per reference: 220 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 2018-06-18 09:46 in 42 seconds.

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

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