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JCR Impact Factor: 0.595
JCR 5-Year IF: 0.661
Issues per year: 4
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Next issue: Nov 2017
Avg review time: 105 days


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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LATEST NEWS

2017-Jun-14
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.

2017-Apr-04
We have the confirmation Advances in Electrical and Computer Engineering will be included in the EBSCO database.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

2017-Jan-30
We have the confirmation Advances in Electrical and Computer Engineering will be included in the Gale database.

2016-Dec-17
IoT is a new emerging technology domain which will be used to connect all objects through the Internet for remote sensing and control. IoT uses a combination of WSN (Wireless Sensor Network), M2M (Machine to Machine), robotics, wireless networking, Internet technologies, and Smart Devices. We dedicate a special section of Issue 2/2017 to IoT. Prospective authors are asked to make the submissions for this section no later than the 31st of March 2017, placing "IoT - " before the paper title in OpenConf.

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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: 906 | Views: 3,499

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

References keywords
classification(4)
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

Abstract
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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 318]


[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 95] [SCOPUS Times Cited 157]


[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 41] [SCOPUS Times Cited 54]


[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 309] [SCOPUS Times Cited 527]


[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", http://ecg.mit.edu/dbinfo.html

[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 465] [SCOPUS Times Cited 782]


References Weight

Web of Science® Citations for all references: 910 TCR
SCOPUS® Citations for all references: 1,838 TCR

Web of Science® Average Citations per reference: 101 ACR
SCOPUS® Average Citations per reference: 204 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 2017-10-23 09:32 in 39 seconds.




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Copyright ©2001-2017
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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