caa a22 4500 605510091 CHVBK 20210128100644.0 cr unu---uuuuu 210128e20150601xx s 000 0 eng 10.1007/s10877-014-9613-3 doi (NATIONALLICENCE)springer-10.1007/s10877-014-9613-3 Increasing signal processing sophistication in the calculation of the respiratory modulation of the photoplethysmogram (DPOP) [Elektronische Daten] [Paul Addison, Rui Wang, Alberto Uribe, Sergio Bergese] DPOP (∆POP or Delta-POP) is a non-invasive parameter which measures the strength of respiratory modulations present in the pulse oximetry photoplethysmogram (pleth) waveform. It has been proposed as a non-invasive surrogate parameter for pulse pressure variation (PPV) used in the prediction of the response to volume expansion in hypovolemic patients. Many groups have reported on the DPOP parameter and its correlation with PPV using various semi-automated algorithmic implementations. The study reported here demonstrates the performance gains made by adding increasingly sophisticated signal processing components to a fully automated DPOP algorithm. A DPOP algorithm was coded and its performance systematically enhanced through a series of code module alterations and additions. Each algorithm iteration was tested on data from 20 mechanically ventilated OR patients. Correlation coefficients and ROC curve statistics were computed at each stage. For the purposes of the analysis we split the data into a manually selected ‘stable' region subset of the data containing relatively noise free segments and a ‘global' set incorporating the whole data record. Performance gains were measured in terms of correlation against PPV measurements in OR patients undergoing controlled mechanical ventilation. Through increasingly advanced pre-processing and post-processing enhancements to the algorithm, the correlation coefficient between DPOP and PPV improved from a baseline value of R=0.347 to R=0.852 for the stable data set, and, correspondingly, R=0.225 to R=0.728 for the more challenging global data set. Marked gains in algorithm performance are achievable for manually selected stable regions of the signals using relatively simple algorithm enhancements. Significant additional algorithm enhancements, including a correction for low perfusion values, were required before similar gains were realised for the more challenging global data set. The Author(s), 2014 Hemodynamic monitoring nationallicence Fluid responsiveness nationallicence Pulse oximetry nationallicence DPOP nationallicence PPV nationallicence Addison Paul Advanced Research Group, Covidien Respiratory and Monitoring Solutions, The Technopole Centre, EH26 0PJ, Edinburgh, Scotland, UK aut Wang Rui Advanced Research Group, Covidien Respiratory and Monitoring Solutions, The Technopole Centre, EH26 0PJ, Edinburgh, Scotland, UK aut Uribe Alberto Department of Anesthesiology, The Ohio State University Wexner Medical Center, Columbus, OH, USA aut Bergese Sergio Department of Anesthesiology, The Ohio State University Wexner Medical Center, Columbus, OH, USA aut Journal of Clinical Monitoring and Computing Springer Netherlands 29/3(2015-06-01), 363-372 1387-1307 29:3<363 2015 29 10877 https://doi.org/10.1007/s10877-014-9613-3 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10877-014-9613-3 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Addison Paul Advanced Research Group, Covidien Respiratory and Monitoring Solutions, The Technopole Centre, EH26 0PJ, Edinburgh, Scotland, UK aut NATIONALLICENCE 700 1- Wang Rui Advanced Research Group, Covidien Respiratory and Monitoring Solutions, The Technopole Centre, EH26 0PJ, Edinburgh, Scotland, UK aut NATIONALLICENCE 700 1- Uribe Alberto Department of Anesthesiology, The Ohio State University Wexner Medical Center, Columbus, OH, USA aut NATIONALLICENCE 700 1- Bergese Sergio Department of Anesthesiology, The Ohio State University Wexner Medical Center, Columbus, OH, USA aut NATIONALLICENCE 773 0- Journal of Clinical Monitoring and Computing Springer Netherlands 29/3(2015-06-01), 363-372 1387-1307 29:3<363 2015 29 10877