The first automatic algorithm was designed to estimate the pulse pressure variation (PPVPPV) from arterial blood pressure (ABP) signals under spontaneous breathing conditions. While currently there are a few publicly ...The first automatic algorithm was designed to estimate the pulse pressure variation (PPVPPV) from arterial blood pressure (ABP) signals under spontaneous breathing conditions. While currently there are a few publicly available algorithms to automatically estimate PPVPPV accurately and reliably in mechani-cally ventilated subjects, at the moment there is no automatic algorithm for estimating PPVPPV on sponta-neously breathing subjects. The algorithm utilizes our recently developed sequential Monte Carlo method (SMCM), which is called a maximum a-posteriori adaptive marginalized particle filter (MAM-PF). The performance assessment results of the proposed algorithm on real ABP signals from spontaneously breath-ing subjects were reported.展开更多
文摘The first automatic algorithm was designed to estimate the pulse pressure variation (PPVPPV) from arterial blood pressure (ABP) signals under spontaneous breathing conditions. While currently there are a few publicly available algorithms to automatically estimate PPVPPV accurately and reliably in mechani-cally ventilated subjects, at the moment there is no automatic algorithm for estimating PPVPPV on sponta-neously breathing subjects. The algorithm utilizes our recently developed sequential Monte Carlo method (SMCM), which is called a maximum a-posteriori adaptive marginalized particle filter (MAM-PF). The performance assessment results of the proposed algorithm on real ABP signals from spontaneously breath-ing subjects were reported.
