An autocorrelation-based time domain analysis technique for monitoring perfusion and oxygenation in transplanted organs.

In designing an implantable sensor for perfusion monitoring of transplant organs the ability of the sensor to gather perfusion information with limited power consumption and in near real time is paramount. The following work was performed to provide a processing method that is able to predict perfusion and oxygenation change within the blood flowing through a transplanted organ. For this application, an autocorrelation-based algorithm was used to reduce the acquisition time required for fast Fourier transform (FFT) analysis while retaining the accuracy inherent to FFT analysis. In order to provide data proving that the developed method is able to predict perfusion as accurately as FFT two experiments were developed isolating both periodic and quasi-periodic cardiac frequencies. It was shown that the autocorrelation-based method was able to perform comparably with FFT (limited to a sampling frequency of 300 Hz) and maintain accuracy down to acquisition times as low as 4 s in length.