Increasing signal processing sophistication in the calculation of the respiratory modulation of the photoplethysmogram (DPOP)

Abstract

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.

On Better Estimating and Normalizing the Relationship between Clinical Parameters: Comparing Respiratory Modulations in the Photoplethysmogram and Blood Pressure Signal (DPOP versus PPV)

“…Synchronized acquisition of the pulse oximeter and arterial pressure signals was performed during the whole procedure and saved for later analysis. Further details of the acquisition system, patient exclusion criteria, and the data are provided in [ 14 , 15 ]. The 20 subject data records used in the study had a mean length of data recording of 115 minutes (43–204 minutes).…”

“…The oximeter device amplification on the pleth waveform was linear across the operating range and through gain changes. Full details of the algorithm used for DPOP are provided elsewhere by the authors [ 14 , 15 ] (see also [ 1 ] and references contained therein for additional pertinent information on algorithms for the extraction of respiratory modulations for the derivation of respiratory information from a pleth).…”

Finger and forehead photoplethysmography-derived pulse-pressure variation and the benefits of baseline correction

“…Previously, finger PI has been shown to affect the agreement between PAV and PPV [29]. In their work, to correct for this, finger-derived PAV values were reduced artificially when the finger was poorly perfused (PI < 3%).…”

“…To prevent displaying a spuriously high PPV value, which can, for example, be caused by an irregular beat, we adopted a 5-point median filter to smooth the PPV values. Intensive PPV post-processing can further improve the results at the cost of a clinically-relevant [20,29]. Future research to design algorithms incurring less delay could further aid the clinical utility of PPG-derived PAV as a non-invasive measure of PPV.…”

Pulse Oximetry: Beyond SpO2

“…41 Algorithms have been developed to allow extraction of the breathing frequency from the pulse oximeter plethysmogram. [42][43][44][45][46][47][48] Addison and colleagues 45 describe a method that determines breathing frequency from 3 aspects of the plethysmogram (Fig. 6): (1) baseline (direct current) mod- Waveform display: Ability to change time scales, switch between scroll and erase bar display modes, wavelength selectable (infrared signal vs red signal vs other) Ability to turn off auto-gain function and auto-center function Ability to set the amplitude gain Numeric display of amplitude and direct current signal Ability to use a wide range of probes (finger, ear, and reflective) Digital and analog outputs for capture by data collection equipment Data from Reference 39. ulation, (2) pulse amplitude modulation (POP), and (3) respiratory sinus arrhythmia due to the variation in heart rate that occurs throughout the respiratory cycle (sinus arrhythmia).…”