Subramanian Hariharan, Ibey Bennett L, Xu Weijian, Wilson Mark A, Ericson M Nance, Coté Gerard L
Department of Biomedical Engineering, Texas A&M University, College Station, TX 77843-3120, USA.
IEEE Trans Biomed Eng. 2005 Jul;52(7):1355-8. doi: 10.1109/TBME.2005.847552.
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.
在设计用于移植器官灌注监测的可植入传感器时，传感器以有限功耗近乎实时地收集灌注信息的能力至关重要。开展以下工作是为了提供一种能够预测流经移植器官的血液中灌注和氧合变化的处理方法。对于此应用，使用了一种基于自相关的算法来减少快速傅里叶变换（FFT）分析所需的采集时间，同时保留FFT分析固有的准确性。为了提供数据证明所开发的方法能够像FFT一样准确地预测灌注，开展了两个实验，分别分离出周期性和准周期性心脏频率。结果表明，基于自相关的方法能够与FFT相媲美（限于300 Hz的采样频率），并且在低至4秒的采集时间内仍能保持准确性。
