This study aims to develop a high-fidelity prediction model based on artificial neural networks to quantify changes in blood oxygen saturation of the internal jugular vein (IJV) (ΔSijvO2) from the pulsatile component of diffuse reflectance spectra measured non-invasively from the neck surface above the IJV. Training and testing data are generated using a surrogate model, which is millions of times faster than the original Monte Carlo simulations. We have investigated the model’s resilience to measurement noise, changes in surrounding tissue’s oxygen saturation, and fluctuations in IJV’s depth and size due to respiration. Results of validating the prediction model by simulated data have exhibited root mean square errors of less than 4%. Finally, validation of the prediction model on healthy subjects performing the Valsalva maneuver in vivo has demonstrated agreements between predicted results and expected physiological responses.
A prediction model based on artificial neural networks was built to quantify changes in blood oxygen saturation of the internal jugular vein (dSijvO2) from diffuse reflectance measured at five wavelengths. The model was trained by Monte Carlo simulations with various tissue optical coefficients and subject-specific tissue structure determined by ultrasound imaging. Errors in dSijvO2 estimated from simulated data are below 2.2% and independent of the initial oxygen saturation. The model was further validated by excellent agreements between modeled and measured in-vivo reflectance spectra from a healthy volunteer undergoing hyperventilation, and the quantified trend of dSijvO2 followed expectations during and after hyperventilation. The proposed method is promising to provide non-invasive quantification of dSijvO2.
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