ECG signal classification based on BPNN

Author

Qu Xiao ; Jian, Cai Wei ; Fei, Ge Ding

Author_Institution

Sch. of Autom. & Electr. Eng., Zhejiang Univ. of Sci. & Technol., Hangzhou, China

fYear

2011

fDate

15-17 April 2011

Firstpage

1362

Lastpage

1364

Abstract

This paper focus on the ECG signal classification based on the BP Neuron Network. The AR coefficients and relative errors were used to represent the ECG segments in current research. The data in the paper obtained from MIT-BIH database. It included Normal Sinus Rhythm (NSR), premature ventricular contraction (PVC), Ventricular Tachycardia (VT), and Ventricular Fibrillation (VF). The back propagation neural network (BPNN) was utilized to classify the classes. The training and testing data was 100 and 100 samples,. The results show that overall accuracy is 95.72%-97.36%.

Keywords

backpropagation; electrocardiography; medical signal processing; neural nets; signal classification; AR coefficient; BP neuron network; BPNN; ECG signal classification; MIT-BIH database; Normal Sinus Rhythm; Ventricular Fibrillation; Ventricular Tachycardia; back propagation neural network; premature ventricular contraction; relative error; Adaptation model; Classification algorithms; Computational modeling; Databases; Electrocardiography; Feature extraction; Rhythm; BPNN; Classification Autoregressive algorithm; ECG signal; Feature Extraction;

fLanguage

English

Publisher

ieee

Conference_Titel

Electric Information and Control Engineering (ICEICE), 2011 International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-8036-4

Type

conf

DOI

10.1109/ICEICE.2011.5777902

Filename

5777902