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ABSTRACT: The photoplethysmographic waveform sits at the core of the most used, and arguably the most important, clinical monitor, the pulse oximeter. Interestingly, the pulse oximeter was discovered while examining an artifact during the development of a noninvasive cardiac output monitor. This article will explore the response of the pulse oximeter waveform to various modes of ventilation. Modern digital signal processing is allowing for a re-examination of this ubiquitous signal. The effect of ventilation on the photoplethysmographic waveform has long been thought of as a source of artifact. The primary goal of this article is to improve the understanding of the underlying physiology responsible for the observed phenomena, thereby encouraging the utilization of this understanding to develop new methods of patient monitoring. The reader will be presented with a review of respiratory physiology followed by numerous examples of the impact of ventilation on the photoplethysmographic waveform.
Sensors 01/2012; 12(2):2236-54. · 1.74 Impact Factor
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ABSTRACT: The photoplethysmographic (PPG) waveforms are modulated by the respiratory, cardiac and autonomic nervous system. Lower body negative pressure (LBNP) has been used as an experimental tool to simulate loss of central blood volume in humans. The aim of our research is to understanding PPG waveform changes during progressive hypovolemia.
With IRB approval, 11 volunteers underwent a LBNP protocol at baseline, 30, 75, and 90 mmHg (or until the subject became symptomatic). Subjects were monitored with finger and ear pulse oximeter probes, ECG, and finger arterial blood pressure monitor (FABP). Heart rate variability (HRV) was analyzed to high frequency (HRV-HF) (0.12-0.4 Hz) and low frequency (HRV-LF) (0.04-0.12 Hz). Frequency analysis of PPG waveforms were computed to low (0.04-0.11 Hz) frequency (PPG-LF), intermediate (0.12-0.18 Hz) frequency (PPG-IF), respiratory (0.19-0.3 Hz) frequency (PPG-Resp.) and cardiac (0.75-2.5 Hz) frequency (PPG-Cardiac)during different phases of LBNP protocol
Heart rate increased significantly while systolic, mean and pulse pressure of the FABP declined slowly together with significant reductions in HRV-HF (0.12-0.4 Hz) and HRV-LF (0.04-0.12 Hz) power at LBNP(75). There was significant reduction in finger PPG-Cardiac modulation which is consistent with the reduction in the pulse pressure of the FABP. As the LBNP progress there was shift in the amplitude density of the ear PPG-Cardiac to PPG-Resp. Oscillation as an evidence of progressive hypovolemia with reduction in pulse pressure and increase in the respiratory induced variations. At LBNP(75), there were significant increased (>140% increase from the baseline) in ear PPG-IF (0.12-0.18 Hz) in the meantime HRV-HF showed significant reduction (>89%) from the baseline. At the symptomatic phase; there was a shift in ear PPG-IF to PPG-Resp. With an increase in the ear PPG-Resp. Modulation to ≥175% from the baseline
The pulse oximeter waveform contains a complex mixture of the effect of cardiac, venous, autonomic, and respiratory systems on the central and peripheral circulation. The occurrence of autonomic modulation needs to be taken into account when studying signals that have their origins from central sites (e.g. ear and forehead).
International Journal of Clinical Monitoring and Computing 11/2011; 25(6):387-96.
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ABSTRACT: Our study sought to explore changes in photoplethysmographic (PPG) waveform param- eters, during lower body negative pressure (LBNP) which simulated hypovolemia, in spontaneously breathing volunteers. We hypothesize that during progressive LBNP; there will be a preservation of ear PPG parameters and a decrease in finger PPG parameters.
With IRB approval, 11 volunteers underwent a LBNP protocol at baseline, 30, 75, and 90 mm Hg (or until the subject became symptomatic). Subjects were monitored with finger and ear pulse oximeter probes, an ECG, and a finger arterial blood pressure monitor. The square root of the mean of the squared differences between adjacent NN intervals (RMSSD) which is the time domain analysis of the heart rate variability (HRV) was measured. PPG waveforms were analyzed for height, area, width 50, maximum and minimum slope. Data are presented as median and inter-quartile range. Friedman ANOVA and Wilcoxon tests were used to identify changes in hemo- dynamic and PPG parameters, P < 0.017 was considered statistically significant.
There were no significant changes in the blood pressure variables at LBNP(30), but at and beyond LBNP(75), the decreases in systolic, mean and pulse pressure were significant as was the increase in diastolic pressure. Heart rate increased significantly at LBNP(30), reaching a maximum of 75.4% above baseline at the symptomatic phase while RMSSD showed significant reduction at LBNP(75). Finger PPG height, area, width 50, and maximum slope decreased significantly at LBNP(30) and during symptomatic phase they showed a reduction of 59.4, 76.9, 27.4 and 51.6%, respectively. Ear PPG height, area, width 50 and maximum slope did not change significantly until the LBNP(75), reached. During symptomatic phase, the respective declines reached 39.3, 61.0, 21.4 and 34.9%.
PPG waveform parameters may prove to be sensitive and specific as early indicators of blood loss. These PPG changes were observed before profound decreases in arterial blood pressure. The relative sparing of central cutaneous blood flow is consistent with the increased parasympathetic innervation of central structures.
International Journal of Clinical Monitoring and Computing 11/2011; 25(6):377-85.
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ABSTRACT: The objective of this study was to determine the function that best expressed the true shape of the regression line between transgastric short axis (TGSA) transesophageal echocardiographic (TEE)) views of the left ventricle (LV) and radionuclear LVEF.
The literature was searched for relevant articles published between 1979 and 2007. Articles that directly compared TGSA LVEF with radionuclear LVEF were reviewed. Inclusion criteria included the provision that TGSA estimations be acquired by manual tracing of the endocardial border. Digital tabular data sets were created from electronically scanned graphic data (Photoshop, Adobe Systems, Inc., San Jose, CA). Software consisted of SPSS (SPSS, Inc., Chicago, IL). Analysis was by regression curve fitting with standard parametric and LOWESS (locally weighted scatterplot smoothing) data-driven models.
Three articles met our study design criteria. The studies generated a total of 32 patients with 99 data points. These data were pooled and analyzed. The LOWESS regression surface demonstrated non-linearity. The best "goodness of fit" template function was determined by selecting the function with the highest R-squared value. The best fit (R(2) = 0.807) consisted of a polynomial function with a power trajectory.
Linear regression analysis provides a linear regression function. Thus, analyses are forced to conform to a straight-line relationship, irrespective of the disposition of the data points. By contrast, parametric and nonparametric regression models allow a choice of functions. This feature permits construction of a more appropriate "goodness of fit" curve. In this study, our purpose was to determine the function that best expressed the true shape of the regression line. Results indicated that the relationship between TEE TGSA and radionuclear LVEF was curvilinear.
International Journal of Clinical Monitoring and Computing 07/2008; 22(3):169-73.
