Lee Seungjoon, Ibey Bennett L, Xu Weijian, Wilson Mark A, Ericson M Nance, Coté Gerard L
Department of Biomedical Engineering, Texas A&M University, Biomedical Engineering, College Station, TX 77843, USA.
IEEE Trans Biomed Eng. 2005 Jul;52(7):1350-2. doi: 10.1109/TBME.2005.847538.
A wavelet-based signal processing technique was employed to improve an implantable blood perfusion monitoring system. Data was acquired from both in vitro and in vivo sources: a perfusion model and the proximal jejunum of an adult pig. Results showed that wavelet analysis could isolate perfusion signals from raw, periodic, in vitro data as well as fast Fourier transform (FFT) methods. However, for the quasi-periodic in vivo data segments, wavelet analysis provided more consistent results than the FFT analysis for data segments of 50, 10, and 5 s in length. Wavelet analysis has thus been shown to require less data points for quasi-periodic data than FFT analysis making it a good choice for an indwelling perfusion monitor where power consumption and reaction time are paramount.
一种基于小波的信号处理技术被用于改进植入式血液灌注监测系统。数据采集自体外和体内来源：一个灌注模型以及一头成年猪的空肠近端。结果表明，小波分析能够像快速傅里叶变换（FFT）方法一样，从原始的、周期性的体外数据中分离出灌注信号。然而，对于准周期性的体内数据段，在长度为50秒、10秒和5秒的数据段中，小波分析比FFT分析提供了更一致的结果。因此，已证明小波分析对准周期性数据所需的数据点比FFT分析少，这使其成为功耗和反应时间至关重要的植入式灌注监测仪的一个不错选择。
