The basic computation of DPOP, expressed by (1), is relatively simple, requiring only a few lines of computer code to implement.
We coded a DPOP algorithm incorporating the steps described above.
The performance analysis of the DPOP parameter against the PPV signal comprises computing statistics that describe quantitatively the relationship between them, including correlation and receiver operator characteristic (ROC) curve analysis.
Figure 4(a) shows the scatter plot of DPOP versus PPV for the global data records of the OSU data set.
The ROC statistics are summarized in Table 1, including the sensitivity and specificity at the maximum Youden index and the DPOP threshold that this operating point corresponds to.
The present study demonstrates a strong relationship between DPOP and PPV and its further improvement through accounting for low perfusion signals within the algorithm.
DPOP has been proposed as a noninvasive surrogate for pulse pressure variation (PPV) used in the determination of the response to volume expansion in hypovolemic patients [8, 11] and many studies have found good agreement between the two parameters in the OR and ICU [1-8, 11, 12].
Thus a correction is required for DPOP at low perfusions as the pressure-mechanical dynamical system changes state.
The number of messages sent between agents in DPOP is linear in the number of agents.
Complete DCOP Inference Algorithms As BE can be used to solve both CSPs and COPs, DPOP, which we described earlier for solving DCSPs, can also be used to solve DCOPs.
The number and size of messages and the memory requirement of each agent in this version of DPOP are the same as those in the version that solves DCSPs.
Constraint reasoning CSP COP DCSP DCOP models Complete inference BE BE DPOP DPOP algorithms Incomplete inference AC-3 W-AC3 DisAC-9 Distributed SAC algorithms Complete search BT AOBB ABT ADOPT algorithms Incomplete search BA BA DBA DBA algorithms Table 2.