NDLI: The Sorting Method of ECG Signals Based on Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Chen Tian-hua Zheng Yu Han Li-qun Guo Pei-yuan He Xiao-yan
Copyright Year	2008
Description	Author affiliation: Coll. of Inf. Eng., Beijing Technol. & Bus. Univ., Beijing (Chen Tian-hua; Zheng Yu; Han Li-qun; Guo Pei-yuan; He Xiao-yan)
Abstract	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.
Starting Page	543
Ending Page	546
File Size	117842
Page Count	4
File Format	PDF
ISBN	9781424417476
DOI	10.1109/ICBBE.2008.132
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-05-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Filtering Neural networks Cardiac disease Artificial neural networks Electrocardiography Signal processing Finite impulse response filter Cardiovascular diseases Digital filters Sorting
Content Type	Text
Resource Type	Article

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