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A Survey on Classification of ECG Signal Study

Taha E. Taha, Ayman El-Sayed, Salma R. El-Soudy. Published in Signal Processing.

Communications on Applied Electronics
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Taha E. Taha, Ayman El-Sayed, Salma R. El-Soudy
10.5120/cae2016652467

Taha E Taha, Ayman El-Sayed and Salma R El-Soudy. A Survey on Classification of ECG Signal Study. Communications on Applied Electronics 6(5):11-16, December 2016. BibTeX

@article{10.5120/cae2016652467,
	author = {Taha E. Taha and Ayman El-Sayed and Salma R. El-Soudy},
	title = {A Survey on Classification of ECG Signal Study},
	journal = {Communications on Applied Electronics},
	issue_date = {December 2016},
	volume = {6},
	number = {5},
	month = {Dec},
	year = {2016},
	issn = {2394-4714},
	pages = {11-16},
	numpages = {6},
	url = {http://www.caeaccess.org/archives/volume6/number5/692-2016652467},
	doi = {10.5120/cae2016652467},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Electrocardiogram (ECG) is a non-linear dynamic signal which plays the main role in diagnosis heart diseases. Classification of ECG signal is one of the most important reason of diagnosing the heart diseases. Detecting accurate ECG signal not only the most difficult task but also classifying heart signal is very difficult task. There are many types of classifiers are available for ECG classification. The most popular classifier that used in ECG classification is Artificial Neural Network (ANN) and in second degree is Support Vector Machine (SVM). In this paper, we discuss a survey of preprocessing, ECG database, feature extraction and classifiers. This paper also discusses background of Electrocardiogram, evaluation matrices of classifiers and issues of classifiers.

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Keywords

Electrocardiogram, ECG classification, preprocessing, ANN, SVM, feature extraction, MIT-BIH database, Pan-Tompkins