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We describe a novel automatic algorithm to continuously estimate the pulse pressure variation (PPV) index from arterial blood pressure (ABP) signals. The algorithm utilizes our recently developed sequential Monte Carlo method (SMCM) based on a maximum a-posterior adaptive marginalized particle filter (MAM-PF). The PPV index is one of most specific and sensitive dynamic indicators of fluid responsiveness...
Cardiovascular signals such as arterial blood pressure (ABP), pulse oximetry (SpO2), and central venous pressure (CVP) contain useful information such as heart rate, respiratory rate, and pulse pressure variation (PPV). We present a statistical state-space model of cardiovascular signals that can be used with the extended Kalman filter or smoother to simultaneously estimate and track many cardiovascular...
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