The Sorting Method of ECG Signals Based on Neural Network
This paper gives a new way of recognizing and sorting of ECG signals, using ECG signals analysis and improved Back Propagation artificial Neural Network. Firstly, the ECG signals has to be digitally filtered to eliminating the 50Hz noise. The neural network adopted is improved Back Propagation threelayer artificial Neural Network, with an optimized BPN4-3-2 network topology. Get the four characteristic parameters: span of QRS wave, interval of R-R segment, voltage of S-T segment, slop of S-T from filtered ECG signals to act as the input parameters of the input layer. After about 1200 times training of 16 samples, the network can correctly distinguishes coronary heart disease sample from non-coronary heart disease samples. Meanwhile, this network can also be applied to sort the 10 nontrained ECG signals samples correctly. The results of experiment show, that the method of recognizing and sorting ECG signals discussed in this paper totally accords with the clinic diagnosis. So it is a new, promising, non-invasive method of diagnosing coronary heart disease.
Coronary Heart Diseas ECG signals Non-invasive Diagnosis Artificial Neural Network Digital Filter Pattern Recognition
Chen Tian-hua Zheng Yu Han Li-qun Guo Pei-yuan He Xiao-yan
College of Information Engineering, Beijing Technology and Business University Beijing 100037, Peoples Republic of China
国际会议
上海
英文
543-546
2008-05-16（万方平台首次上网日期，不代表论文的发表时间）