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An ECG signal contains important information required for diagnosis and analysis of heart diseases. So if there is noise induced in an ECG signal then scrutinizing of that signal for pathological, anatomical and physiological aspects goes worthless. Noises can be introduced by various sources, but a common source for high frequency noise is due to forces acting on the electrodes. In this paper noise...
In this paper, a new representation method for electrocardiogram (ECG) signals analysis is suggested. This representation scheme is based on the spectral correlation function (SCF) which appears hidden periodicity of signals. The SCF presents a second-order statistical description in the frequency domain and can be used for several applications of ECG analysis such as classification. The SCF of each...
Wavelet transform has been emerged over recent years as a powerful time-frequency analysis and signal coding tool favored for the interrogation of complex non stationary signals. Its application to bio-signal processing has been at the forefront of these developments where it has been found particularly useful in the study of these, often problematic, signals: none more so than the Electrocardiogram...
Presented paper describes a system of biomedical signal classifiers with preliminary feature extraction stage based on matched wavelets analysis, where two structures of classifier using Neural Networks (NN) and Support Vector Machine (SVM) are applied. As a pilot study the rules extraction algorithm applied for two of mentioned machine learning approaches (NN & SVM) was used. This was made to...
According to the wide application of time- frequency distributions in Biomedical signal analyses, especially in ECG signals, and due to existence of high energy and low frequency components and sharp templates in ECG signals, the components which have very low amplitude do not appear clearly in time-frequency displays. In this research, a suitable method for preprocessing the biological signals by...
We expand the idea to develop new bio-signal processing tools that could predict possibility of future risk of abnormalities in ECG signals. The goal is to detect an inherent defect hidden in an ECG signal using wavelet analysis and support vector machines. We apply singular value decomposition analysis of spectral energy distribution in time-frequency plane to extract features, which is essentially...
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