DocumentCode :
3416556
Title :
Artificial neural network for ECG arryhthmia monitoring
Author :
Hu, Y.-H. ; Tompkins, W.J. ; Xue, Q.
Author_Institution :
Dept. of Electr. & Comput. Eng., Wisconsin Univ., Madison, WI, USA
fYear :
1992
fDate :
31 Aug-2 Sep 1992
Firstpage :
350
Lastpage :
359
Abstract :
The application of a multilayer perceptron artificial neural network model (ANN) to detect the QRS complex in ECG (electrocardiography) signal processing is presented. The objective is to improve the heart beat detection rate in the presence of severe background noise. An adaptively tuned multilayer perceptron structure is used to model the nonlinear, time-varying background noise. The noise is removed by subtracting the predicted noise from the original signal. Preliminary experimental results indicate that the ANN based approach consistently outperforms the conventional bandpass filtering approach and the linear adaptive filtering approach. Such performance enhancement is most critical toward the development of a practical automated online ECG arrhythmia monitoring system
Keywords :
electrocardiography; feedforward neural nets; patient monitoring; QRS complex detection; artificial neural network model; automated online system; background noise; heart beat detection rate; multilayer perceptron; nonlinear time varying noise; signal processing; Adaptive filters; Artificial neural networks; Background noise; Band pass filters; Electrocardiography; Heart beat; Heart rate detection; Monitoring; Multilayer perceptrons; Signal processing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks for Signal Processing [1992] II., Proceedings of the 1992 IEEE-SP Workshop
Conference_Location :
Helsingoer
Print_ISBN :
0-7803-0557-4
Type :
conf
DOI :
10.1109/NNSP.1992.253677
Filename :
253677
Link To Document :
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