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This paper introduces a new method of heartbeat classification based on the support vector machine classifier using morphological descriptors and High Order Statistic using MIT/BIH Arrhythmia database. Using the morphological descriptors and polynomial kernel, we have obtained an average sensitivity equal to 89,92% and an average specificity about 82,45%,(More)
This paper introduces a new method for R waves locations using the multiscale wavelet analysis. That is based on Mallatpsilas and Hwangpsilas approach for singularity detection via local maxima of the wavelet coefficients signals. Using a first derivative Gaussian function as prototype wavelet, we apply the point-wise product of the wavelet coefficients(More)
This paper introduces a new method for R wave’s locations using the multiscale wavelet analysis, that is based on Mallat’s and Hwang’s approach for singularity detection via local maxima of the wavelet coefficients signals. Using a first derivative Gaussian function as prototype wavelet, we apply the pointwise product of the wavelet coefficients (PWCs) over(More)
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