An MLP neural network for ECG noise removal based on Kalman filter.
In this paper, application of Artificial Neural Network (ANN) for electrocardiogram (ECG) signal noise removal has been investigated. First, 100 number of ECG signals are selected from Physikalisch-Technische Bundesanstalt (PTB) database and Kalman filter is applied to remove their low pass noise. Then a suitable dataset based on denoised ECG signal is configured and used to a Multilayer Perceptron (MLP) neural network to be trained. Finally, results and experiences are discussed and the effect of changing different parameters for MLP training is shown.
Moein
Sara
S
Multimedia University, Cyberjaya, Malaysia. sara.moein08@mmu.edu.my
D001185:Artificial Intelligence; D019295:Computational Biology; D016208:Databases, Factual; D004562:Electrocardiography / Q000706:statistics & numerical data*; D006331:Heart Diseases / Q000175:diagnosis / Q000503:physiopathology; D006801:Humans; D016571:Neural Networks, Computer; D012016:Reference Values; D012815:Signal Processing, Computer-Assisted