The Automatic Classification of ECG Based on BP Neural Network

Lan Lan Yu; Bo Xue Tan; Tian Xing Meng

doi:10.4028/www.scientific.net/AMR.121-122.111

Paper Titles

Study on Maximum Power Point Tracking for Grid-Connected Photovoltaic System
p.93

Power Dissipation Analysis of Adiabatic Circuits and Active Leakage Power Estimation in Nanometer CMOS Processes
p.97

Evaluation Method to Determine Perpendicular Error of Spindle Units Using Transformation Matrix
p.103

Perpendicular Error Calibration of Simulation Turntable Using Transformation Matrix
p.107

The Automatic Classification of ECG Based on BP Neural Network
p.111

Design of a Permanent Magnet Synchronous Generator for Vehicle Application
p.117

Local Saddle Points Theory of a New Augmented Lagrangians
p.123

The Judgement for Generalized Positive Definite Matrices in Signal Processing
p.128

A New Method for Stochastic Mathematical Programs with Equilibrium Constraints
p.133

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 121-122The Automatic Classification of ECG Based on BP...

The Automatic Classification of ECG Based on BP Neural Network

Abstract:

The classification and recognition of ECG are helpful to distinguish and diagnose heart diseases, which also have very important clinical application value for the automatic diagnoses of ECG. The traditional recognition methods need people to extract determinant rules and have no learning ability so that they are unable to simulate the intuition and fuzzy diagnoses function used by doctor very well. The neural network technology has strongpoint of self-organization, self-learning and strong tolerance for error. It provides a new method for the automatic classification of ECG. In this paper, we use BP neural network to do automatic classification for five kinds of ECG which are natural stylebook, paced heart beating, left branch block, right branch block and ventricular tachycardia. The average recognition level is 98.1%. Experiment results show that the neural networ k technology can greatly improve the recognition level of ECG. It has good clinical application value.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 121-122)

Pages:

111-116

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.121-122.111

Citation:

Cite this paper

Online since:

June 2010

Authors:

Lan Lan Yu, Bo Xue Tan, Tian Xing Meng

Keywords:

Automatic Classification, BP Neural Network (BPNN), ECG

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Jiaren Lin, Fansan Zhu: Foreign Medical Sciences, Vol. 17 (1994).

Google Scholar

[2] Wenjin Shen: The new technology of ECG diagnoses (China medicine publishing company, 2000).

Google Scholar

[3] Lin He, Wensheng Hou and Xiaolin Zhen: Sixth International Conference on Intelligent Systems Design and Applications, Vol. 2 (2006), pp.477-481.

Google Scholar

[4] Tianhua Chen, Zheng Yu and Liqun Han: 2nd International Conference on Bioinformatics and Biomedical Engineering, (2008), pp.543-546.

Google Scholar

[5] Güler Inan, Ubeyli Elif Derya: Elsevier Ltd on behalf of Pattern Recognition Society, (2005), pp.199-208.

Google Scholar

[6] Pingfan Yan, Changshui Zhang: Artifical neural network and simulation evolution arithmetic (Qinghua university publishing company, 2000).

Google Scholar

[7] Linlin Zheng, Weixing Han and Mei Li: Clinical health care magazine of China, Vol. 17 (2004), pp.86-88.

Google Scholar

[8] Tezel Gülay, Ozbay Yüksel: 11th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, (2007), pp.1-8.

Google Scholar

[9] G. Krishna Prasad J.S. Sahambi: IEEE conference on convergent technologies for Asia-Pacific Region, Vol. 1 (2003), pp.227-231.

Google Scholar