A Gauss-GPD hybrid model that links a Gaussian distribution to a Generalized Pareto Distribution (GPD) is considered for asymmetric heavy tailed data. The paper proposes a new unsupervised iterative algorithm to find successively the junction point between the two distributions and to estimate the hybrid model parameters. Simulation results show that this method provides a reliable position for the junction point, as well as an accurate estimation of the GPD parameters, which improves results when compared with other methods. Another advantage of this approach is that it can be adapted to any hybrid model.
In this paper, we present a novel method for Rpeak detection in the ElectroCardioGram (ECG) signal in a noisy environment. We interpret the R-peak occurrence as an irregularity in the signal. Thereby, we transform the problem of R-peaks detection into irregularity instants estimation. To point out these irregularities, we use an algebraic approach based on differential algebra and operational calculus. To make the R-peak detection more accuracy, we propose a new decision rule permitting a reliable distinction between R-peaks and false alarms. To assess the validity of theoretical analysis, numerical simulations, according to signals from the MIT-BIH arrhythmias database, are performed. To carry out the performance of the proposed method, a comparison with the most used technique in literature is achieved. The obtained results show the robustness of the algebraic method in the context of complicated pathologies as well as to various types of noises.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.