Processing of pulse oximeter data using discrete wavelet analysis.
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.
Lee
Seungjoon
S
Department of Biomedical Engineering, Texas A&M University, Biomedical Engineering, College Station, TX 77843, USA. sjlee@tamu.edu
Ibey
Bennett L
BL
Xu
Weijian
W
Wilson
Mark A
MA
Ericson
M Nance
MN
Coté
Gerard L
GL
D000465:Algorithms; D000818:Animals; D001783:Blood Flow Velocity / Q000502:physiology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005583:Fourier Analysis; D010092:Oximetry / Q000379:methods*; D011673:Pulsatile Flow / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013552:Swine