Real time perfusion and oxygenation monitoring in an implantable optical sensor

Simultaneous blood perfusion and oxygenation monitoring is crucial for patients undergoing a transplant procedure. This becomes of great importance during the surgical recovery period of a transplant procedure when uncorrected loss of perfusion or reduction in oxygen saturation can result in patient death. Pulse oximeters are standard monitoring devices which are used to obtain the perfusion level and oxygen saturation using the optical absorption properties of hemoglobin. However, in cases of varying perfusion due to hemorrhage, blood clot or acute blockage, the oxygenation results obtained from traditional pulse oximeters are erroneous due to a sudden drop in signal strength. The long term goal of the project is to devise an implantable optical sensor which is able to perform better than the traditional pulse oximeters with changing perfusion and function as a local warning for sudden blood perfusion and oxygenation loss. In this work, an optical sensor based on a pulse oximeter with an additional source at 810nm wavelength has been developed for in situ monitoring of transplant organs. An algorithm has been designed to separate perfusion and oxygenation signals from the composite signal obtained from the three source pulse oximetry-based sensor. The algorithm uses 810nm reference signals and an adaptive filtering routine to separate the two signals which occur at the same frequency. The algorithm is initially applied to model data and its effectiveness is further tested using in vitro and in vivo data sets to quantify its ability to separate the signals of interest. The entire process is done in real time in conjunction with the autocorrelation-based time domain technique. This time domain technique uses digital filtering and autocorrelation to extract peak height information and generate an amplitude measurement and has shown to perform better than the traditional fast Fourier transform (FFT) for semi-periodic signals, such as those derived from heart monitoring. In particular, in this paper it is shown that the two approaches produce comparable results for periodic in vitro perfusion signals. However, when used on semi periodic, simulated, perfusion signals and in vivo data generated from an optical perfusion sensor the autocorrelation approach clearly (Standard Error, SE = 0.03) outperforms the FFT-based analysis (Standard Error, SE = 0.62).