Monitoring respiration rate in PACU patients using the plethysmogram from a commercial pulse oximeter

Introduction Post-operative patients with undiagnosed obstructive sleep apnea are at risk of sudden respiratory failure after receiving anesthesia because of repressed respiratory and hemodynamic responses [1]. While all patients have their oxygen saturation monitored in the Post Anesthesia Care Unit (PACU), few have their respiration rate monitored. The respiratory rate could be used to detect a breathing problem significantly faster than using oxygen saturation alone [8]. The goal of our study is to develop algorithms that reliably estimate the respiration rate from the pleth waveform collected by a standard, off-the-shelf pulse oximeter. The phtotplethysmogram (PPG) measured by the pulse oximeter, commonly referred to as the “pleth waveform”, is an indirect measurement of blood volume under the sensor [7]. The temporal behavior of this signal is influenced both by the cardiac and respiratory cycles. Respiratory induced variations (RIV) in PPG amplitude have been documented and associated with airway obstruction, hypovolemia, and hypotension [4,5,6]. Many of these studies were qualitative and relied on analysis of baseline variations (DC component) of the PPG from a specialized pulse oximeter using a printout of the waveform. Our algorithm extracts pulse morphology parameters from the PPG using a mixed-state feature extractor based on previous work on sequential state estimation [3]. This feature extractor allows us to obtain statistics about each individual pulse, including pulse height, width, area, rise and fall time. Our experimental results demonstrate that these features show measurable variations due to respiration, and can provide a reliable measure of respiration rate. Experimental Procedure With IRB approval, six patients (ASA Class 1 and 2) were monitored for up to an hour during their stay in the PACU. The patients were monitored using a Datex-Ohmeda® bedside monitor, a Nonin® forehead reflectance pulse oximeter, and a digital video recorder. The respiration rate was manually extracted from the video recording by observing the rise and fall of the chest. We chose the forehead location because other studies suggest that this is the best location for detecting respiratory variations in the PPG [2]. Data from the pulse oximeter was pre-processed using standard Nonin hardware (AC coupled and bandpass filtered). Results The respiratory rates obtained from our feature extraction software were compared to the respiratory rates from the video. As shown in Figure 1, the pulse height, the difference between the rise and fall time and the instantaneous heart rate all provide robust statistics for estimating instantaneous respiration rate. However, during brief periods of movement, talking, or change in pose, motion artifacts obscure the pulsitile component of the signal. Also, periods of low perfusion gives a small pulse amplitude, less than ten units, and results in immeasurable RIV. Very shallow breathing can also result in very small RIV, which, along with a low or falling oxygen saturation could serve as a marker of respiratory distress. Subsequent work in our laboratory has shown that when excessive pressure is used to hold the pulse ox to the forehead the PPG is suppressed and will result in a immeasurable RIV. 510 520 530 540 550 560 570 100 120