Noninvasive blood glucose detection system based on dynamic spectrum and “M+N″ theory

High-precision spectra captured by a spectral camera and suppression of their nonlinearity

The high sensitivity of photoplethysmography (PPG) spectral signals provides conditions for extracting dynamic spectra carrying nonlinear information. By the idea of spatial conversion precision, this paper uses a spectral camera to collect highly sensitive spectral data of 24 wavelengths and proposes a method for extracting dynamic spectra of three different optical path lengths and their joint modeling. In the experiment, the models of the red blood cells and white blood cells established by the joint spectra achieved good results, with the correlation coefficients above 0.77. This study has great significance for achieving high-precision noninvasive quantitative analysis of human blood components.

High-precision non-invasive RBC and HGB detection system based on spectral analysis

Non-invasive blood composition analysis based on dynamic spectrum (DS) theory has gained significant attention due to its non-invasive, simple, and fast performance. However, most of the multi-wavelength photoplethysmography (PPG) detection devices used to obtain DS are composed of halogen light sources and spectrometers and cannot detect effective PPG signals in the visible light short band (400-620 nm), which limits the detection accuracy of blood components with significant absorption spectral differences in that band. Therefore, this paper designs a multi-wavelength spectral acquisition system that can measure high signal-to-noise ratio (SNR > 65 dB) PGG signals at wavelengths of 405, 430, 450, 505, 520, and 570 nm and combines this system with a halogen lamp spectrometer acquisition system for non-invasive blood component detection. Furthermore, this paper collects the DS of 272 subjects with the combined system and establishes a predictive model for DS with the content of red blood cell (RBC) and hemoglobin (HGB) components. The results show that, compared with the halogen lamp spectrometer acquisition system, the correlation coefficient (Rp) of RBC and HGB prediction model established by the combined system has increased by 0.0619 and 0.0489, respectively, and the root mean square error (RMSE) has decreased by 0.08 1e12/L and 0.85 g/L, which confirm the feasibility of the designed multi-wavelength spectrum acquisition system to enhance the accuracy of blood component detection.

Improving the accuracy of quantitative spectroscopic analysis of leukocyte by suppressing the influence of the "M" factors based on "M + N" theory

Leukocytes play a crucial role in clinical diagnosis. The noninvasive and immediate detection of this low blood component has both important application and academic significance. The "M + N" theory makes it obvious that suppressing the influence of N factors and reducing the influence of M factors are both necessary in order to accurately detect the low content of blood components like leukocytes. Therefore, based on the strategy of "fixing the influencing factors" in the "M + N" theory, this paper proposes the method of "partition modeling based on the content of large concentrations of non-target components." Firstly, a dynamic spectral acquisition system was built to achieve the noninvasive acquisition of spectra. Then this paper applies the method proposed above to the modeling process of the samples. In order to lessen the impact of the M factors, the approach first divides the samples into groups based on the concentrations of the major blood components (platelets and hemoglobin). This reduces the range of fluctuation of the non-target components in each interval. The modeling of the leukocyte content was then carried out independently for each sample contained in each compartment. Compared with the result of modeling the sample directly, the related coefficient of the calibration set (Rc) improved by 11.70%, and the root mean square error (RMSEC) decreased by 76.97%; the related coefficient of the prediction set (Rp) improved by 32.68%, and the root mean square error (RMSEP) decreased by 52.80%. When the model was applied to predict all samples, the related coefficient (R-all) increased by 16.67%, and the root mean square error (RMSE-all) dropped by 63.00%. It was shown that, as compared to direct modeling of leukocyte concentration, the method of "partition modeling based on the content of large concentrations of non-target components" considerably increased the accuracy of quantitative analysis of leukocytes. The method can be used to analyze other blood components as well, offering a fresh approach and technique to increase the precision of spectral analysis of the blood's small content components.

Noninvasive and simultaneous quantitative analysis of multiple human blood components based on the grey analysis system

Noninvasive detection of human blood components is the dream of human beings and the goal of clinical detection. From the perspective of mathematical analysis, based on the grey analysis system, the principle of spectral chemical quantitative analysis and the solution method of multivariate linear equation, this paper pioneers the spectrum elimination method, and obtains a complete, high-precision, synchronous and noninvasive detection system for a variety of human blood components. The spectral elimination method applies the principle of elimination method in mathematics to the noninvasive quantitative analysis of human blood components by spectral method, reduces the influence of non-target components on the detection of target components, and improves the accuracy of noninvasive quantitative analysis of human blood components. To demonstrate the effectiveness of the method, taking the analysis of the contents of seven blood components (hemoglobin, red blood cell count, neutrophils, lymphocytes, monocytes, eosinophils and basophils) in blood as an example, fourteen models were established by two different methods. From the comparison of modeling results, it can be concluded that when the seven models established by spectral elimination method predict the corresponding seven components of all samples, the predicted correlation coefficients are more than 0.9500. The experimental results show that the spectral elimination method and non-invasive detection system proposed can predict the content of human blood components with high accuracy. This paper studies a high-precision, simultaneous and noninvasive quantitative analysis system of multiple human blood components for the first time, which not only makes great progress in the non-invasive chemical quantitative analysis of human blood components by spectroscopy, but also has great application value for clinical medical treatment and disease diagnosis.

Non-invasive quantitative analysis of human blood components in renal three items using spectral compensation method based on spectral data and component content correlation

BACKGROUND AND OBJECTIVE

The non-invasive measurement of the blood component contents in humans using spectroscopic methods has important clinical application value. In the absorption spectrum of human blood, for a target component with a small content, the absorption spectrum line is low, the absorption spectrum line of the target component changes weakly at different wavelengths, and the absorption spectrum lines of the target and non-target components overlap. The above reasons cause the spectral line difference between the target and non-target components to be small. However, non-target components also have physical properties such as scattering, which will reduce the contribution of the target component in the absorption spectrum of blood, that is, the spectral line difference between the target and non-target components becomes smaller, thus making the accuracy of the target component model lower.

METHODS

To increase the accuracy of modeling analysis, according to the correlation between spectral data and the content of the target component, this paper proposes a spectral compensation method. The method compensates for the influence caused by the non-target components on the target components through the mathematical relations between the data of the spectrum and the contents of the target components. Thus, a high-precision, non-invasive quantitative analysis system of human blood components is obtained. Taking the analysis of the three components of urea nitrogen, creatinine, and uric acid as examples, the experiment used spectral data before and after spectral compensation and partial least squares (PLS) methods to build two models and used them to predict the content of the target component.

RESULTS

When the models of different components established by the spectral compensation method respectively predict the all samples' contents of urea, creatinine, and uric acid, the three correlation coefficients can reach more than 0.9700.

CONCLUSION

It can be obtained from the experimental results that the spectral compensation method can greatly increase the performance of each component model. Spectral compensation is a new and comprehensive method for improving the quality of spectral data. Therefore, this paper obtains a non-invasive quantitative analysis system of human blood components with higher precision, and can quantitatively analyze blood urea nitrogen, creatinine, and uric acid in human blood with high precision, which has great practical application value in clinical practice.

A non-contact oxygen saturation detection method based on dynamic spectrum

Blood oxygen saturation (SpO2) is an important monitoring indicator for many respiratory diseases. Non-contact oximetry offers outstanding advantages in both coronavirus pandemic monitoring and sleep monitoring, but at the same time poses both challenges regarding technology and environment. Therefore, we propose a method for non-contact SpO2 measurement based on the principle of DS (dynamic spectrum) in this paper. A multispectral camera with 24 wavelengths (range in 660 nm-950 nm) is used to capture video of the people's cheek region, and then the two-dimensional images are converted into a one-dimensional temporal PPG signal. After pre-processing the PPG signal, the 24 wavelengths DS values are extracted. The optimal wavelength combination is obtained by wavelength screening using the one-by-one elimination method, and a PLS (partial least squares) model is established using the SpO2 values measured simultaneously by pulse oximetry as the modeled true values. The facial videos of eight healthy subjects were collected, and a total of 140 valid samples were obtained. By analyzing the modeling results, the regression coefficient (R) and root mean square error (RMSE) of the modeled set were 0.6366 and 0.9906, respectively. This method can significantly respond to the variation of SpO2, and the prediction results are approaching to the prediction accuracy (±2%) of most pulse oximeters in the market. Using DS theory in this method eliminates in principle the interference of static tissue, individual differences, and environment. It fully meets the strong demand for non-contact oximetry and provides a new measurement idea.