Han Li-qun

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 50 Hz noise. The neural network adopted is improved back propagation three-layer 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 non-trained 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.