Using Artificial Neural Networks for ECG Signals Denoising

• Main Authors: Zoltán Germán-Salló, Katalin György
• Format: Article
• Language: English
• Published: Editura Universităţii "Petru Maior" 2010-12-01
• Series: Scientific Bulletin of the ''Petru Maior" University of Tîrgu Mureș
• Subjects: artificial neural network; ECG signal; prediction; backpropagation; prediction error
• Online Access: http://scientificbulletin.upm.ro/papers/2011/01/Using-Artificial-Neural-Networks-for-ECG-Signals-Denoising3.pdf

The authors have investigated some potential applications of artificial neural networks in electrocardiografic (ECG) signal prediction. For this, the authors used an adaptive multilayer perceptron structure to predict the signal. The proposed procedure uses an artificial neural network based learning structure to estimate the (n+1)th sample from n previous samples To train and adjust the network weights, the backpropagation (BP) algorithm was used. In this paper, prediction of ECG signals (as time series) using multi-layer feedforward neural networks will be described. The results are evaluated through approximation error which is defined as the difference between the predicted and the original signal.The prediction procedure is carried out (simulated) in MATLAB environment, using signals from MIT-BIH arrhythmia database. Preliminary results are encouraging enough to extend the proposed method for other types of data signals.