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JCR Impact Factor: 0.800
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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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  3/2009 - 16

 HIGH-IMPACT PAPER 

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
 
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Download PDF pdficon (484 KB) | Citation | Downloads: 1,378 | Views: 5,332

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.


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Cited-By Clarivate Web of Science

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Cited-By SCOPUS

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Cited-By CrossRef

[1] Heart Arrhythmia Detection using support vector machines, Khazaee, Ali, Ebrahimzadeh, Ataollah, Intelligent Automation & Soft Computing, ISSN 1079-8587, Issue 1, Volume 19, 2013.
Digital Object Identifier: 10.1080/10798587.2013.771456
[CrossRef]

[2] Automatic Detection of Atrial Fibrillation from ECG Signal Using Hybrid Deep Learning Techniques, Pandey, Saroj Kumar, Kumar, Gaurav, Shukla, Shubham, Kumar, Ankit, Singh, Kamred Udham, Mahato, Shambhu, Yin, Aijun, Journal of Sensors, ISSN 1687-7268, Issue , 2022.
Digital Object Identifier: 10.1155/2022/6732150
[CrossRef]

[3] On ECG Compressed Sensing using Specific Overcomplete Dictionaries, FIRA, M., GORAS, L., BARABASA, C., CLEJU, N., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 4, Volume 10, 2010.
Digital Object Identifier: 10.4316/aece.2010.04004
[CrossRef] [Full text]

[4] Improving ECG Classification Accuracy Using an Ensemble of Neural Network Modules, Javadi, Mehrdad, Ebrahimpour, Reza, Sajedin, Atena, Faridi, Soheil, Zakernejad, Shokoufeh, Androulakis, Ioannis P., PLoS ONE, ISSN 1932-6203, Issue 10, Volume 6, 2011.
Digital Object Identifier: 10.1371/journal.pone.0024386
[CrossRef]

[5] Automatic ECG arrhythmia classification using dual tree complex wavelet based features, Thomas, Manu, Das, Manab Kr, Ari, Samit, AEU - International Journal of Electronics and Communications, ISSN 1434-8411, Issue 4, Volume 69, 2015.
Digital Object Identifier: 10.1016/j.aeue.2014.12.013
[CrossRef]

[6] Detection of electrocardiogram signals using an efficient method, Ebrahimzadeh, A., Shakiba, B., Khazaee, A., Applied Soft Computing, ISSN 1568-4946, Issue , 2014.
Digital Object Identifier: 10.1016/j.asoc.2014.05.003
[CrossRef]

[7] Novel Cardiac Arrhythmia Processing using Machine Learning Techniques, Prashar, Navdeep, Sood, Meenakshi, Jain, Shruti, International Journal of Image and Graphics, ISSN 0219-4678, Issue 03, Volume 20, 2020.
Digital Object Identifier: 10.1142/S0219467820500230
[CrossRef]

[8] Searching Appropriate Mother Wavelets for Hyperanalytic Denoising, FIROIU, I., NAFORNITA, C., BOUCHER, J. M., ISAR, A., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 4, Volume 10, 2010.
Digital Object Identifier: 10.4316/aece.2010.04020
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[9] A Novel Steerable Filter in the Frequency Domain: The Rose Curve Filter, MINTEMUR, O., KAYA, H., DEMIRCI, R., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 2, Volume 21, 2021.
Digital Object Identifier: 10.4316/AECE.2021.02006
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[10] A Proposal for Cardiac Arrhythmia Classification using Complexity Measures, AROTARITEI, D., COSTIN, H., PASARICA, A., ROTARIU, C., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 3, Volume 17, 2017.
Digital Object Identifier: 10.4316/AECE.2017.03004
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[11] A Telesurveillance System With Automatic Electrocardiogram Interpretation Based on Support Vector Machine and Rule-Based Processing, Ho, Te-Wei, Huang, Chen-Wei, Lin, Ching-Miao, Lai, Feipei, Ding, Jian-Jiun, Ho, Yi-Lwun, Hung, Chi-Sheng, JMIR Medical Informatics, ISSN 2291-9694, Issue 2, Volume 3, 2015.
Digital Object Identifier: 10.2196/medinform.4397
[CrossRef]

[12] RETRACTED ARTICLE: A kernel support vector machine based anomaly detection using spatio-temporal motion pattern models in extremely crowded scenes, Priyadharsini, N. K., Chitra, D., Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, Issue 5, Volume 12, 2021.
Digital Object Identifier: 10.1007/s12652-020-02000-3
[CrossRef]

[13] Classification of cardiac arrhythmias based on dual tree complex wavelet transform, Thomas, Manu, Das, Manab Kr, Ari, Samit, 2014 International Conference on Communication and Signal Processing, ISBN 978-1-4799-3358-7, 2014.
Digital Object Identifier: 10.1109/ICCSP.2014.6949939
[CrossRef]

[14] Abnormality detection in ECG using hybrid feature extraction approach, Singh, Ritu, Rajpal, Navin, Mehta, Rajesh, 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC), ISBN 978-1-5386-6373-8, 2018.
Digital Object Identifier: 10.1109/ICSCCC.2018.8703349
[CrossRef]

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