Comput. Biol. Med. - Multi-Gaussian fitting for pulse waveform using Weighted Least Squares and multi-criteria decision making method.

Tópicos

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Resumo

Analysis of pulse waveform is a low cost, non-invasive method for obtaining vital information related to the conditions of the cardiovascular system. In recent years, different Pulse Decomposition Analysis (PDA) methods have been applied to disclose the pathological mechanisms of the pulse waveform. All these methods decompose single-period pulse waveform into a constant number (such as 3, 4 or 5) of individual waves. Furthermore, those methods do not pay much attention to the estimation error of the key points in the pulse waveform. The estimation of human vascular conditions depends on the key points' positions of pulse wave. In this paper, we propose a Multi-Gaussian (MG) model to fit real pulse waveforms using an adaptive number (4 or 5 in our study) of Gaussian waves. The unknown parameters in the MG model are estimated by the Weighted Least Squares (WLS) method and the optimized weight values corresponding to different sampling points are selected by using the Multi-Criteria Decision Making (MCDM) method. Performance of the MG model and the WLS method has been evaluated by fitting 150 real pulse waveforms of five different types. The resulting Normalized Root Mean Square Error (NRMSE) was less than 2.0% and the estimation accuracy for the key points was satisfactory, demonstrating that our proposed method is effective in compressing, synthesizing and analyzing pulse waveforms.

Resumo Limpo

analysi puls waveform low cost noninvas method obtain vital inform relat condit cardiovascular system recent year differ puls decomposit analysi pda method appli disclos patholog mechan puls waveform method decompos singleperiod puls waveform constant number individu wave furthermor method pay much attent estim error key point puls waveform estim human vascular condit depend key point posit puls wave paper propos multigaussian mg model fit real puls waveform use adapt number studi gaussian wave unknown paramet mg model estim weight least squar wls method optim weight valu correspond differ sampl point select use multicriteria decis make mcdm method perform mg model wls method evalu fit real puls waveform five differ type result normal root mean squar error nrmse less estim accuraci key point satisfactori demonstr propos method effect compress synthes analyz puls waveform

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