Title :
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
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;
Conference_Titel :
Electric Information and Control Engineering (ICEICE), 2011 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-8036-4
DOI :
10.1109/ICEICE.2011.5777902
