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Published ahead of print June 9, 2016

The photoplethysmographic (PPG) waveform, also known as the pulse oximeter waveform, is one of the most commonly displayed clinical waveforms. First described in the 1930s, the technology behind the waveform is simple. The waveform, as displayed on the modern pulse oximeter, is an amplified and highly filtered measurement of light absorption by the local tissue over time. It is optimized by medical device manufacturers to accentuate its pulsatile components. Physiologically, it is the result of a complex, and not well understood, interaction between the cardiovascular, respiratory, and autonomic systems. All modern pulse oximeters extract and display the heart rate and oxygen saturation derived from the PPG measurements at multiple wavelengths. “As is,” the PPG is an excellent monitor for cardiac arrhythmia, particularly when used in conjunction with the electrocardiogram (ECG). With slight modifications in the display of the PPG (either to a strip chart recorder or slowed down on the monitor screen), the PPG can be used to measure the ventilator-induced modulations which have been associated with hypovolemia. Research efforts are under way to analyze the PPG using improved digital signal processing methods to develop new physiologic parameters. It is hoped that when these new physiologic parameters are combined with a more modern understanding of cardiovascular physiology (functional hemodynamics) the potential utility of the PPG will be expanded. The clinical researcher's objective is the use of the PPG to guide early goal-directed therapeutic interventions (fluid, vasopressors, and inotropes), in effect to extract from the simple PPG the information and therapeutic guidance that was previously only obtainable from an arterial pressure line and the pulmonary artery catheter.

The focus of intraoperative monitoring is moving away from invasive monitoring. This has been attributed to procedure time, cost, and the known risks, which include carotid artery puncture, arrhythmia, pneumothorax, and infection. Until recently, the venous system's contribution to the circulatory system has been incorrectly identified as being insignificant. This article summarizes the unique characteristics of the peripheral venous system.

This article reviews the development of novel reflectance pulse oximetry sensors for the esophagus and bowel, and presents some of the techniques used to analyze the waveforms acquired with such devices.

Objective: For years researchers have been attempting to understand the relationship between central hemodynamics and the resulting peripheral waveforms. This study is designed to further understanding of the relationship between ear pulse oximeter waveforms, finger pulse oximeter waveforms and cardiac output (CO). It is hoped that with appropriate analysis of the peripheral waveforms, clues can be gained to help to optimize cardiac performance. Methods.Part 1:Studying the effect of cold immersion test on plethysmographic waveforms. Part 2:Studying the correlation between ear and finger plethysmographic waveforms and (CO) during CABG surgery. The ear and finger plethysmographic waveforms were analyzed to determine amplitude, width, area, upstroke and downslope. The CO was measured using continuous PA catheter. Using multi-linear regression, ear plethysmographic waveforms, together with heart rate (HR), were used to determine the CO Agreement between the two methods of CO determination was assessed. Results.Part 1:On contralateral hand immersion, all finger plethysmographic waveforms were reduced, there was no significant change seen in ear plethysmographic waveforms, except an increase in ear plethysmographic width. Part 2: Phase1: Significant correlation detected between the ear plethysmographic width and other ear and finger plethysmographic waveforms. Phase 2: The ear plethysmographic width had a significant correlation with the HR and CO. The correlation of the other ear plethysmographic waveforms with CO and HR are summarized (Table 5). Multi-linear regression analysis was done and the best fit equation was found to be: CO = 8.084 − 14.248 × Ear width + 0.03 ×HR+ 92.322 × Ear down slope+0.027 × Ear Area Using Bland & Altman, the bias was (0.05 L) but the precision (2.46) is large to be clinically accepted. Conclusion.The ear is relatively immune to vasoconstrictive challenges which make ear plethysmographic waveforms a suitable monitor for central hemodynamic changes. The ear plethysmographic width has a good correlation with CO.Objective: For years researchers have been attempting to understand the relationship between central hemodynamics and the resulting peripheral waveforms. This study is designed to further understanding of the relationship between ear pulse oximeter waveforms, finger pulse oximeter waveforms and cardiac output (CO). It is hoped that with appropriate analysis of the peripheral waveforms, clues can be gained to help to optimize cardiac performance. Methods.Part 1:Studying the effect of cold immersion test on plethysmographic waveforms. Part 2:Studying the correlation between ear and finger plethysmographic waveforms and (CO) during CABG surgery. The ear and finger plethysmographic waveforms were analyzed to determine amplitude, width, area, upstroke and downslope. The CO was measured using continuous PA catheter. Using multi-linear regression, ear plethysmographic waveforms, together with heart rate (HR), were used to determine the CO Agreement between the two methods of CO determination was assessed. Results.Part 1:On contralateral hand immersion, all finger plethysmographic waveforms were reduced, there was no significant change seen in ear plethysmographic waveforms, except an increase in ear plethysmographic width. Part 2: Phase1: Significant correlation detected between the ear plethysmographic width and other ear and finger plethysmographic waveforms. Phase 2: The ear plethysmographic width had a significant correlation with the HR and CO. The correlation of the other ear plethysmographic waveforms with CO and HR are summarized (Table 5). Multi-linear regression analysis was done and the best fit equation was found to be: CO = 8.084 − 14.248 × Ear width + 0.03 ×HR+ 92.322 × Ear down slope+0.027 × Ear Area Using Bland & Altman, the bias was (0.05 L) but the precision (2.46) is large to be clinically accepted. Conclusion.The ear is relatively immune to vasoconstrictive challenges which make ear plethysmographic waveforms a suitable monitor for central hemodynamic changes. The ear plethysmographic width has a good correlation with CO.

Objective.In the process of determining oxygen saturation, the pulse oximeter functions as a photoelectric plethysmograph. By analyzing how the frequency spectrum of the pulse oximeter waveform changes over time, new clinically relevant features can be extracted. Methods.Thirty patients undergoing general anesthesia for abdominal surgery had their pulse oximeter, airway pressure and CO2waveforms collected (50 Hz). The pulse oximeter waveform was analyzed with a short-time Fourier transform using a moving 4096 point Hann window of 82 seconds duration. The frequency signal created by positive pressure ventilation was extracted using a peak detection algorithm in the frequency range of ventilation (0.08–0.4 Hz = 5–24 breaths/minute). The respiratory rate derived in this manner was compared to the respiratory rate as determined by CO2detection. Results.In total, 52 hours of telemetry data were analyzed. The respiratory rate measured from the pulse oximeter waveform was found to have a 0.89 linear correlation when compared to CO2detection and airway pressure change. the bias was 0.03 breath/min, SD was 0.557 breath/min and the upper and lower limits of agreement were 1.145 and −1.083 breath/min respectively. The presence of motion artifact proved to be the primary cause of failure of this technique. Conclusion.Joint time frequency analysis of the pulse oximeter waveform can be used to determine the respiratory rate of ventilated patients and to quantify the impact of ventilation on the waveform. In addition, when applied to the pulse oximeter waveform new clinically relevant features were observed.Objective.In the process of determining oxygen saturation, the pulse oximeter functions as a photoelectric plethysmograph. By analyzing how the frequency spectrum of the pulse oximeter waveform changes over time, new clinically relevant features can be extracted. Methods.Thirty patients undergoing general anesthesia for abdominal surgery had their pulse oximeter, airway pressure and CO2waveforms collected (50 Hz). The pulse oximeter waveform was analyzed with a short-time Fourier transform using a moving 4096 point Hann window of 82 seconds duration. The frequency signal created by positive pressure ventilation was extracted using a peak detection algorithm in the frequency range of ventilation (0.08–0.4 Hz = 5–24 breaths/minute). The respiratory rate derived in this manner was compared to the respiratory rate as determined by CO2detection. Results.In total, 52 hours of telemetry data were analyzed. The respiratory rate measured from the pulse oximeter waveform was found to have a 0.89 linear correlation when compared to CO2detection and airway pressure change. the bias was 0.03 breath/min, SD was 0.557 breath/min and the upper and lower limits of agreement were 1.145 and −1.083 breath/min respectively. The presence of motion artifact proved to be the primary cause of failure of this technique. Conclusion.Joint time frequency analysis of the pulse oximeter waveform can be used to determine the respiratory rate of ventilated patients and to quantify the impact of ventilation on the waveform. In addition, when applied to the pulse oximeter waveform new clinically relevant features were observed.

Objective. We report the off-line calculation of the vascular complianceof the finger and suggest the continuous on-line use of this methodology asan aid to monitoring the peripheral vascular resistance. This method consistsof the simultaneous analysis of the waveform signals from the pulse oximetermonitors and the arterial pressure as indicators of “volume” andpressure respectively to continuously calculate the vascular“compliance” (volume change per unit pressure change). This shouldbe seen as a “relative compliance” as the pulse plethysmographsignal is not calibrated. This new methodology allows for continuousmonitoring of peripheral vascular compliance as a beat-to-beat indicator ofperipheral vascular resistance. The vaso-constrictors, phenylephrine andephedrine, were shown to decrease the compliance as predicted. Methods. Thearterial pressure and pulse oximeter waveforms were obtained during routineanesthetic care. The waveforms were collected with a computer data-acquisitionsystem and then analyzed “off-line” as an indirect indicator oftotal vascular tone. Demographic and clinical information including drugadministration were recorded. Results. A case report is presented using thisnew form of analysis. Vascular compliance changes induced by phenylephrine andephedrine were studied. A dose response curve of peripheral vascularcompliance to phenylephrine was generated from these data. Conclusions. Byplotting the pulse oximeter waveforms versus the arterial waveforms, multiplevolume versus pressure (relative compliance) loops were obtained. Analysis ofthese loops may assist in the monitoring of vascular compliance.

Abstract: The pulse oximeter can serve as a sensitive photoelectric plethysmograph in the operating room. It was noted in several cases that the plethysmographic waveform showed a high degree of variability during diastole. Three patients are described with discrete diastolic peaks on the plethysmograph. Further investigation revealed that these diastolic peaks appear to correlate with peripheral venous pulsation, which seems to have a central venous origin. Evidence is presented that the plethysmographic detection of the venous pulse may be useful in estimating the changing volume status of the patient.

Published ahead of print August 22, 2016.