Abstract:
Signal quantitation in most near infrared spectroscopy
(NIRS) instruments is achieved through solving simultaneous
equations or multiple regression analysis. The aim of this study was
to compare NIRS signal quantitation by conventional multiple
regression to artificial neural networks. Sixteen adult sheep were
used in the study of the effects of changes in cerebral blood flow and
metabolism through induction of seizures, ischemia, and
hypercapnia. NIRS-derived signal attenuation for relative blood
volume (BY) and oxygen desaturation (DESA1) were compared to
simultaneous blood flow values measured by laser Doppler
flowmetry and venous oxygen saturation (SvO2) determined from
direct blood gas analysis. The regression for flow provided a zero p-value,
a variance S=17.57 and F statistic=50.49. The residuals vs,
fits plots suggest that the current model would underestimate values
below the mean and overestimate those above the mean. An
improved regression model for SvO2 provided a zero p-value, a
variance S=14.1 and F statistic=4.26. Two different neural networks
were implemented for flow and oxygen saturation. Both networks
"tracked" their values closely and with low cycle errors. Neural
networks are powerful tools for evaluation of rapidly changing,
variable environments.
