Towards robust reduction of nonlinear errors in dynamic spectrum spectroscopy for effective noninvasive optical detection of blood components - Awelisah, Yussif Moro; Li, Gang; Lin, Ling (Infrared physics & technology, 2022)
•Scattering of blood tissue can cause nonlinear errors in spectroscopy detection.•Multi-position optical pathlength spectrum detection can reduce the scattering.•Optimized filtering and nonlinear modeling demonstrate robust nonlinearity reduction.•The correlation coefficient of linear hemoglobin prediction can reach over 0.97. Most conventional noninvasive optical methods for blood component detection suffer from individual subjects’ variability interferences. Dynamic spectrum (DS) is a spectroscopy technique that can eliminate these limitations and improve the accuracy of blood component assessment. The DS method is an adaptive technique that can extract blood information from multiple and different spectral wavelengths of the pulsating arterial part of the photoplethysmography (PPG) signal. However, high concentrations of human blood have strong scattering properties that result in a nonlinear effect during spectral analysis. There is still a paucity of robust techniques to address this nonlinear effect in DS and noninvasive optical detection of blood components. This investigation proposed a novel optimized extended Kalman filter (O-EKF) technique and radial basis function neural network (RBF-NN) to address the nonlinear effect in spectroscopy measurements. The O-EKF and RBF-NN were considered nonlinear algorithms with the adaptive ability to approximate the nonlinear relationship between spectral absorbance and concentration. Hemoglobin samples with concentration levels between 120 g/L and 175 g/L acquired from 231 volunteer subjects were used for sample prediction and spectrum accuracy evaluation. The spectrum extraction from single positioning obtained a correlation coefficient of prediction (Rp) = 0.721 and a root mean square error of prediction (RMSEp) = 10.023 with the partial least square (PLS) model. Further evaluation with the original dual-position spectrum extraction without optimized filtering recorded Rp = 0.636 and RMSEp = 10.630. The results of the dual-position spectrum extraction with O-EKF and the RBF-NN model proved to be effective with a significant increase in Rp = 0.976 and a decrease in RMSEp = 8.132. The results represent a 35% increase in hemoglobin prediction and 31% reduction in prediction error when compared to spectrum detection in single-position. The results demonstrate that nonlinearity from scattering can be reduced through multi-position spectrum detection and optimized filtering. The present study could provide a robust alternative to eliminate nonlinearity and thus improve the linear concentration detection of blood components.