title: An MLP neural network for ECG noise removal based on Kalman filter.
abstract: 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.
authors.affiliation: Multimedia University, Cyberjaya, Malaysia. sara.moein08@mmu.edu.my
authors.forename: Sara
authors.initials: S
authors.lastname: Moein
mesh_terms: 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