RESUMO
Low Noise Electrocardiogram (ECG) has been widely used for heart disease diagnosis. The anisotropic median-diffusion is the filter obtained by intercalating a median filtering in each diffusion step. We propose to use anisotropic median-diffusion to filter noisy ECG signals. We describe how to estimate appropriate parameters of the proposed filter. We validate our method using ECG signals from the MIT-BIH databases (many of them with premature ventricular contraction) and compare our method with other filtering methods. Experiments show that the proposed technique can effectively remove the noise without changing the instants and amplitudes of events, as well as preserving the morphologies of ECG signals in sections of the QRS complex.
Assuntos
Anisotropia , Eletrocardiografia/instrumentação , Eletrocardiografia/métodos , Algoritmos , Artefatos , Bases de Dados Factuais , Desenho de Equipamento , Ventrículos do Coração/patologia , Humanos , Processamento de Imagem Assistida por Computador , Modelos Estatísticos , Contração Muscular , Processamento de Sinais Assistido por Computador , Software , Técnica de SubtraçãoRESUMO
This paper presents a new, accurate, and efficient technique to increase the spatial resolution of binary halftone images. It makes use of a machine learning process to automatically design a zoom operator starting from pairs of input-output sample images. To accurately zoom a halftone image, a large window and large sample images are required. Unfortunately, in this case, the execution time required by most of the previous techniques may be prohibitive. The new solution overcomes this difficulty by using decision tree (DT) learning. Original DT learning is modified to obtain a more efficient technique (WZDT learning). It is useful to know, a priori, sample complexity (the number of training samples needed to obtain, with probability 1 - delta, an operator with accuracy epsilon): we use the probably approximately correct (PAC) learning theory to compute the sample complexity. Since the PAC theory usually yields an overestimated sample complexity, statistical estimation is used to evaluate, a posteriori, a tight error bound. Statistical estimation is also used to choose an appropriate window and to show that DT learning has good inductive bias. The new technique is more accurate than a zooming method based on simple inverse halftoning techniques. The quality of the proposed solution is very close to the theoretical optimal obtainable quality for a neighborhood-based zooming process using the Hamming distance to quantify the error.