A joint evaluation method of dynamic spectrum extraction methods for non-invasive blood component measurement based on stability coefficient, data point adoption rate, and smoothness of the spectrum

Abstract

Background and ObjectiveDynamic spectrum (DS) theory is a new non-invasive detection method of human blood components that can theoretically eliminate individual differences in static tissues and the influence of other measurement conditions to achieve blood component analysis with high precision. In order to obtain a high signal-to-noise ratio dynamic spectrum, researchers have proposed various dynamic spectrum extraction methods. MethodsIn this article, we propose three indexes: stability coefficient (SC), data point adoption rate (DAR), and smoothness of spectrum (SS). These solve the difficulty in evaluating different dynamic spectrum extraction methods without establishing mathematical models. ResultsIn this study, DS is extracted using different dynamic spectrum extraction methods from the experimental data of 677 volunteers. Then three indexes, SC, DAR, and SS, are calculated. The trends in the scatter plot of the relationship between the three indexes and modeling results of hemoglobin, red blood cell count, and white blood cell count and the related coefficients demonstrate that SC, DAR, and SS are feasible and effective for evaluation. The results show that the root mean square extraction performs best, while the peak-to-peak value and the fast Fourier transform extraction are the worst. ConclusionsThis study proposes feasible and effective indexes for evaluating dynamic spectrum extraction methods, providing a possibility for further research on high-precision dynamic spectrum extraction methods.