CIVILICA We Respect the Science
Publisher of Iranian Journals and Conference Proceedings
Paper
title

Classification of ECG signals using Hermite functions and MLP neural networks

Credit to Download: 1 | Page Numbers 11 | Abstract Views: 58
Year: 2016
COI code: JR_JADM-4-1_007
Paper Language: English

How to Download This Paper

For Downloading the Fulltext of CIVILICA papers please visit the orginal Persian Section of website.

Authors Classification of ECG signals using Hermite functions and MLP neural networks

  A. Ebrahimzadeh - Faculty of Electrical & Computer Engineering, Babol University of Technology.
  M. Ahmadi - Faculty of Electrical & Computer Engineering, Babol University of Technology
  M. Safarnejad - Faculty of Electrical & Computer Engineering, Babol University of Technology

Abstract:

Classification of heart arrhythmia is an important step in developing devices for monitoring the health of individuals. This paper proposes a three module system for classification of electrocardiogram (ECG) beats. These modules are: denoising module, feature extraction module and a classification module. In the first module the stationary wavelet transform (SWF) is used for noise reduction of the ECG signals. The feature extraction module extracts a balanced combination of the Hermit features and three timing interval feature. Then a number of multi-layer perceptron (MLP) neural networks with different number of layers and eight training algorithms are designed. Seven files from the MIT/BIH arrhythmia database are selected as test data and the performances of the networks, for speed of convergence and accuracy classifications, are evaluated.  Generally all of the proposed algorisms have good training time, however, the resilient back propagation (RP) algorithm illustrated the best overall training time among the different training algorithms. The Conjugate gradient back propagation (CGP) algorithm shows the best recognition accuracy about 98.02% using a little amount of features.

Keywords:

ECGbeat Classification, Premature Ventricular Contraction, MLP Neural Network, Training Algorithms, Wavelet Transform, Hermit Features

Perma Link

https://www.civilica.com/Paper-JR_JADM-JR_JADM-4-1_007.html
COI code: JR_JADM-4-1_007

how to cite to this paper:

If you want to refer to this article in your research, you can easily use the following in the resources and references section:
Ebrahimzadeh, A.; M. Ahmadi & M. Safarnejad, 2016, Classification of ECG signals using Hermite functions and MLP neural networks, Journal of Artificial Intelligence & Data Mining 4 (1), https://www.civilica.com/Paper-JR_JADM-JR_JADM-4-1_007.htmlInside the text, wherever referred to or an achievement of this article is mentioned, after mentioning the article, inside the parental, the following specifications are written.
First Time: (Ebrahimzadeh, A.; M. Ahmadi & M. Safarnejad, 2016)
Second and more: (Ebrahimzadeh; Ahmadi & Safarnejad, 2016)
For a complete overview of how to citation please review the following CIVILICA Guide (Citation)

Scientometrics

The University/Research Center Information:
Type: state university
Paper No.: 5493
in University Ranking and Scientometrics the Iranian universities and research centers are evaluated based on scientific papers.

Research Info Management

Export Citation info of this paper to research management softwares

New Related Papers

Iran Scientific Advertisment Netword

Share this paper

WHAT IS COI?

COI is a national code dedicated to all Iranian Conference and Journal Papers. the COI of each paper can be verified online.