A Multivariate Density Estimator For Contrast Agent Injection Monitoring Using A Bayesian Sparse Kernel Approach
2008 30TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-8(2008)
摘要
The administration of intravenous contrast media during CT examinations is routine, but carries with it a risk of extravasation. Hence, we define an injection state to be either intravenous or extravasated. With a new Doppler ultrasound monitoring technique, we propose a method for estimating the probability of an injection state during the various stages of an examination. A smoothed time-frequency representation of the Doppler signal is used to analyze at which frequencies there is the largest difference in response between signals from intravenous and extravasated injections. A vector of response values based on this analysis forms this study's feature space. A Relevance Vector Machine is used to estimate the probability density for a particular injection state. We present preliminary results (n=5) showing the time-frequency representation of the Doppler ultrasound signal, the frequency analysis and the density estimation.
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关键词
density estimation,feature space,relevance vector machine,frequency analysis,probability density,time frequency representation
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