Increasing signal processing sophistication in the calculation of the respiratory modulation of the photoplethysmogram (DPOP)
| LEADER | caa a22 4500 | ||
|---|---|---|---|
| 001 | 605510091 | ||
| 003 | CHVBK | ||
| 005 | 20210128100644.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20150601xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s10877-014-9613-3 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10877-014-9613-3 | ||
| 245 | 0 | 0 | |a Increasing signal processing sophistication in the calculation of the respiratory modulation of the photoplethysmogram (DPOP) |h [Elektronische Daten] |c [Paul Addison, Rui Wang, Alberto Uribe, Sergio Bergese] |
| 520 | 3 | |a 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. | |
| 540 | |a The Author(s), 2014 | ||
| 690 | 7 | |a Hemodynamic monitoring |2 nationallicence | |
| 690 | 7 | |a Fluid responsiveness |2 nationallicence | |
| 690 | 7 | |a Pulse oximetry |2 nationallicence | |
| 690 | 7 | |a DPOP |2 nationallicence | |
| 690 | 7 | |a PPV |2 nationallicence | |
| 700 | 1 | |a Addison |D Paul |u Advanced Research Group, Covidien Respiratory and Monitoring Solutions, The Technopole Centre, EH26 0PJ, Edinburgh, Scotland, UK |4 aut | |
| 700 | 1 | |a Wang |D Rui |u Advanced Research Group, Covidien Respiratory and Monitoring Solutions, The Technopole Centre, EH26 0PJ, Edinburgh, Scotland, UK |4 aut | |
| 700 | 1 | |a Uribe |D Alberto |u Department of Anesthesiology, The Ohio State University Wexner Medical Center, Columbus, OH, USA |4 aut | |
| 700 | 1 | |a Bergese |D Sergio |u Department of Anesthesiology, The Ohio State University Wexner Medical Center, Columbus, OH, USA |4 aut | |
| 773 | 0 | |t Journal of Clinical Monitoring and Computing |d Springer Netherlands |g 29/3(2015-06-01), 363-372 |x 1387-1307 |q 29:3<363 |1 2015 |2 29 |o 10877 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10877-014-9613-3 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s10877-014-9613-3 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Addison |D Paul |u Advanced Research Group, Covidien Respiratory and Monitoring Solutions, The Technopole Centre, EH26 0PJ, Edinburgh, Scotland, UK |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Wang |D Rui |u Advanced Research Group, Covidien Respiratory and Monitoring Solutions, The Technopole Centre, EH26 0PJ, Edinburgh, Scotland, UK |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Uribe |D Alberto |u Department of Anesthesiology, The Ohio State University Wexner Medical Center, Columbus, OH, USA |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Bergese |D Sergio |u Department of Anesthesiology, The Ohio State University Wexner Medical Center, Columbus, OH, USA |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Journal of Clinical Monitoring and Computing |d Springer Netherlands |g 29/3(2015-06-01), 363-372 |x 1387-1307 |q 29:3<363 |1 2015 |2 29 |o 10877 | ||